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The effect of winter weather on U.S. economic activity
Justin Bloesch and François Gourio

Introduction and summary
The unusually cold and snowy 2013–14 winter substantially disrupted the routines of people across the United
States, leading commentators and policymakers to ask if
the weather affected economic activity as well. There were
many media stories that supported this hypothesis. For
instance, some employees were reported as unable to commute to work, and some projects, particularly in construction, were delayed due to equipment limitations or
concerns about safety in the cold and snow. Supply chains
were sometimes interrupted; for instance, steel production
along the coast of Lake Michigan was affected because
the boats delivering iron ore were unable to navigate the
deeply frozen Great Lakes. Furthermore, retailers reported
that households may have delayed shopping due to extreme weather. And finally, some expected that the higher
heating costs and the expenses for home repairs (such as
burst pipes) would hamper consumer spending.

Consistent with these anecdotes, economic indicators published early in 2014, such as industrial production, employment, and car sales, showed that economic
activity had slowed substantially in December 2013 and
January 2014. While the economic recovery following
the Great Recession had appeared to accelerate in the
fall of 2013, these statistics suggested a renewed slowdown. To illustrate these patterns, figure 1 depicts the
evolution of several economic indicators:1 the monthly
change in nonfarm employment, the National Association of Purchasing Managers (NAPM) Index, lightweight vehicle sales, retail sales (excluding auto sales),
manufacturing industrial production, and the Chicago
Fed National Activity index (CFNAI), which itself
summarizes a variety of indicators. These indicators
are seasonally adjusted using statistical methods, which
amounts to removing the effects of a “normal winter.”
In these figures, the three red dotted points correspond
to December 2013, January 2014, and February 2014,
respectively. The decline of these indicators during the
December to February period is consistent with a

Federal Reserve Bank of Chicago

slowdown in economic activity due to the weather,
but could also have reflected other sources of weakness.
Indeed, there was much controversy at the time on
how much of the decline in the indicators was driven
by the bad weather as opposed to other factors. The
conventional wisdom was that a slowdown in economic
activity due to weather would be very temporary;
projects that had been delayed due to weather would
eventually be finished and consumer shopping would
likely resume.
Justin Bloesch is an associate economist and François Gourio is
a senior economist in the Economic Research Department at the
Federal Reserve Bank of Chicago. The authors thank their colleagues
at the Chicago Fed and Lisa Barrow, Olivier Deschênes, Charles
Gilbert, Ben Herzon, Alejandro Justiniano, and Thomas Klier for
useful discussions and feedback.
© 2015 Federal Reserve Bank of Chicago
Economic Perspectives is published by the Economic Research
Department of the Federal Reserve Bank of Chicago. The views
expressed are the authors’ and do not necessarily reflect the views
of the Federal Reserve Bank of Chicago or the Federal Reserve
System.
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President and Director of Research; Spencer Krane, Senior Vice
President and Economic Advisor; David Marshall, Senior Vice
President, financial markets group; Daniel Aaronson, Vice President,
microeconomic policy research; Jonas D. M. Fisher, Vice President,
macroeconomic policy research; Richard Heckinger, Vice President,
markets team; Anna L. Paulson, Vice President, finance team;
William A. Testa, Vice President, regional programs; Lisa Barrow,
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ISSN 0164-0682

1

FIGURE 1

Economic indicators
A. Employment change

B. Purchasing Manager Index

thousands
400

index
60

300
55

200
100

50
2013m1

2014m1

2014m7

2013m1

2014m1

2014m7

2013m1

2014m1

2014m7

2013m1

2014m1

2014m7

C. Car sales

D. Retail sales ex-autos

millions of units
18

12.65

log index

17
16

12.60

15
12.55

14
2013m1

2014m1

2014m7

E. Manufacturing industrial production

F. CFNAI

log index
4.62
4.60
4.58
4.56
4.54

index
.5
0
–.5
–1.0

2013m1

2014m1

2014m7

Source: Haver Analytics.

Whether the economic slowdown was due to winter
weather or an underlying trend had implications for
monetary policy. The Federal Reserve Open Market
Committee (FOMC), which sets monetary policy for
the United States, decided in its December 2013 meeting to start reducing the monthly volume of its asset
purchases (from $85 billion per month to $75 billion
per month). This “tapering” policy was motivated by
improvements in the economy toward the Federal
Reserve’s inflation and employment targets during 2013,
but it was explicitly made data dependent.2 This means
that if the economy was indeed becoming weaker
persistently, the committee would likely continue its
asset purchases at current levels rather than “taper” them.
The weak economic data released early in 2014 made
this a real possibility. However, if the disappointing
data reflected only transitory weather effects, then the
Federal Reserve would continue its gradual decline
of asset purchases. The challenge for both for the
committee and investors was to disentangle how
much of the weakness of the economy was weather

2

related. It is fairly unusual that the weather affects the
economy in a very significant way, and hence there is
little established knowledge, or even a good rule of
thumb, that economists can rely on.3 In part, it also
reflects the fact that good-quality weather data were
not readily available to allow economists to perform
the statistical analyses they would need to estimate
causal relationships. For instance, commonly used
databases do not have data on aggregate snowfall for
the United States, and temperature series are often area
weighted rather than population weighted, which is
probably better for physical science applications but
less useful when one is trying to measure the economic
impact of weather since it gives a large importance to
some sparsely populated states. Reflecting this difficulty in measuring the precise effects of weather, the
March 2014 FOMC statement noted simply that “growth
... slowed during the winter months, in part reflecting
adverse weather conditions,” with the qualifier “in
part” hedging the statement but suggesting that staff
work had not found weather to be the sole determinant

1Q/2015, Economic Perspectives

of weakness.4 In the related press conference, Federal
Reserve Chair Janet Yellen stated that “we did spend
a lot of time discussing weather and how it’s affected
businesses and households in various parts of the
country—certainly weather has played an important role
in weakening economic activity in [the first quarter].
It’s not the only factor that is at work, and most projections for growth in the first quarter are reasonably
weak.”5 Over time, however, economic data started
to improve, as figure 1 shows, and most analysts
came to attribute the winter weakness to weather. For
instance, the April 30, 2014, FOMC statement noted
that “growth ... has picked up recently, after having
slowed sharply during the winter in part because of
adverse weather conditions.”6 Chair Yellen reflected
this in a speech on April 16, 2014, when she said that:
“In recent months, some indicators have been notably
weak ... [and] my FOMC colleagues and I generally
believe that a significant part of the recent softness
was weather related.”7
Later, following a fairly strong increase in growth
in the second quarter, it became folk wisdom that the
weakness of growth in the first quarter was mostly
weather related. For instance, Justin Wolfers wrote in
the New York Times on September 26 that “Much of
[the second quarter] growth is simply catching up
from the first quarter when severe winter storms led
the economy to contract. ... The snow, it seems, led
spending to be deferred a quarter, rather than canceled.”8
Clearly, there is a tension between the initial assessment, which was highly uncertain regarding the effect
of weather on economic activity, and the folk wisdom
that emerged. The goal of this article is to resolve this
tension by providing more robust statistical evidence
regarding the effects of the weather on economic activity.
To do so, we build on work started by some analysts at
the Board of Governors of the Federal Reserve System
(the Board) and at the private forecasting firm Macroeconomic Advisers (2014) and construct better data
using records of individual weather stations across the
entire continental United States. Our analysis improves
over this previous work along two main dimensions:
First, we use longer historical records, allowing us
to increase significantly the length of data. Second,
we use regional variation in economic activity to further increase the span of data available. We discuss
later why having larger samples is especially useful
in this context.
The rest of this article is organized as follows.
We start by reviewing some related literature on the
effects of weather on economic activity. We then present
our measures of weather and discuss in particular the
winter of 2013–14. Next, we present our empirical

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approach, which uses state-level measures of weather
and economic activity to evaluate the effects of the
weather on economic activity; then we show the results.
We also present some results using national-level
measures of weather and economic activity. Finally,
we use our estimates to reassess how much of last
winter’s bad economic data was weather related.
We finish with a note about the potential effects of
climate change on our results.
Related research
The idea that weather is an important source of
fluctuations of production is an old theme in economics.
A century ago, when the economy was still in large
part driven by agriculture, bad crops had a measurable
effect on aggregate income. Thus, the Dust Bowl had
a notable effect during the Great Depression. Weather
may continue to have a significant economic impact
in countries that are very reliant on agriculture, either
because they are poor or because their exports are
concentrated on a small number of crops. Even today,
economists such as Jeffrey Sachs9 attribute the limited economic development of some countries to their
extreme climate. Extremes of temperature, dryness or
humidity, and precipitation (rain or snow) make economic progress difficult for some countries in Africa
and Asia. Closer to home, the Caribbean countries
and Central America regularly experience hurricanes
that destroy housing, infrastructure, and production
capacity. Furthermore, the prospect of climate change
raises the question of how the world economy will be
affected by higher temperatures.
Dell, Jones, and Olken (2014) review the recent
literature on weather and the economy, including their
own study (2012), which shares many methodological
similarities with our approach. Their focus is very
different, however. They use annual country-level data
on temperature (and precipitation) and gross domestic
product (GDP) to estimate the effects of weather on
GDP. They find a significant effect for poor countries,
which appears to be largely, though not exclusively,
driven by the impact on agriculture. An increase in
the average annual temperature of 1 degree Celsius
(that is, 1.8 degrees Fahrenheit) leads GDP to fall by
1.3 percent. Perhaps surprisingly, there seems to be
little tendency for GDP to recover from its decline the
following year, that is, little “bounceback.” They find
no effect on developed countries and no effect of precipitation. The contribution of their paper is to offer an
identification of the effects of weather based on variation over time within countries, rather than on crosscountry relationships. However, because they focus on
annual data, they are unable to study the short-term

3

movements in economic activity that may be due to
weather in developed countries. In a related analysis,
Deschênes and Greenstone (2007) measure the effects
of weather on U.S. agricultural production using detailed geographic data. Here, too, measures of output
are annual.
Most closely related to our study are three papers
that were written contemporaneously with ours. Boldin
and Wright (2015) calculate the effect of weather on
national nonfarm payroll employment. One important
conclusion they draw is that weather affects the seasonal adjustment. Colacito, Hoffman, and Phan (2014)
and Deryugina and Hsiang (2014) both use crossregional U.S. data to study the effects of weather on
economic activity. An important difference is that
they focus on annual measures of income or production rather than on the higher-frequency measures that
we use. These papers also study the total annual weather
effect, whereas we focus on the effect of unusual
winter weather only.
Measuring the weather
Measuring the weather may seem to be a simple
and straightforward exercise. However, exploring the
details of the data reveals various challenges. First, we
need to decide which measure of weather to study.
Temperature alone does not fully capture the ways
in which weather can affect economic activity; other
factors may be important, such as precipitation, wind
(direction and strength), and humidity, for example.
Additionally, several variables may interact. Second,
weather can be highly localized, and a snowstorm in
southern Illinois is unlikely to have the same effect on
employment as a snowstorm in Chicago. Because of
this, the correct way to weight and aggregate our weather
variables is not clear ahead of time. This section outlines our approach.
Our source of weather measurements is a data set
called the U.S. Historical Climatology Network, which
is part of the Global Historical Climatology Network
(GHCN); these data were constructed by the National
Climatic Data Center (NCDC), a part of the National
Oceanic and Atmospheric Administration (NOAA).10
This data set has daily measures of many weather
variables, including temperature, snowfall, and total
precipitation; in this article, we focus on temperature
and snowfall. The data set reports conditions from
about 1,200 weather stations throughout the United
States. However, not all stations were in use in all years
(that is, these data are an unbalanced panel). We use
data from 1950 through 2014 for our estimation.11
There are a few potential issues with the quality of
these data. First, changes in station design or practices

4

sometimes introduce changes in measured temperatures.
For instance, the station instrumentation may change;
the station’s neighborhood may change due to human
activity or the station itself might be moved; the time
at which observations are made during the day may
change. These changes are especially important when
we try to measure long-term changes in the mean temperature; some researchers have developed algorithms
to take into account the changes. However, these adjustments are not available for our data.12
A possibly more important issue with the data is
that stations are introduced partway through the data
set, and some stations stop reporting measurements in
the middle of the data set. This can cause problems with
aggregation. To illustrate this, imagine constructing a
state index for temperature in Illinois. Suppose that
numerous stations are introduced in southern Illinois
and some are discontinued in northern Illinois. Since
the southern part of the state is on average substantially warmer than the northern part, the data would
show a large increase in the statewide temperature
even though the actual temperature never changed.
Failing to account for the evolution of active stations
over time could create artificial changes in measured
weather conditions. When constructing our state weather
indexes, we resolve this problem by aggregating deviations from local long-run averages. For example, suppose the temperature in Illinois is uniformly 2 degrees
above average across the entire state. Suppose it is 52
degrees in southern Illinois but 42 degrees in Chicago,
with a state average of 47 degrees. The normal temperatures for a given day are 50, 40, and 45 degrees,
respectively. If the Chicago station drops out of the
data set, then the state average temperature will suddenly jump to 52 degrees. It then appears that the
state average is 7 degrees above normal, rather than
the actual 2 degrees. However, if one were to average
the deviations from normal, the observed average temperature would still be only 2 degrees above average.
This is precisely the process that we use to construct our weather indexes at the state and monthly level.
We construct the index in six steps. We start from the
daily temperature for a given weather station13 and first
calculate the average monthly temperature for each
month and each year. Mathematically, for a month that
lasts 30 days, and denoting Ts, d, m, y as the temperature
in day d of month m of year y in station s, we define
1
Ts , m , y = ∑ d Ts , d, m , y . Second, we define the “normal
30
weather” for a station and a month as the monthly
temperature averaged over all years from 1950
1
through 2014. Mathematically, Ts , m =
∑T ,
65 y s ,m , y

1Q/2015, Economic Perspectives

FIGURE 2

Temperature in all stations in Illinois
Fahrenheit
60

40

20

0

−20
Jan. 1, 2014

Jan. 8, 2014

Jan. 15, 2014

Jan. 22, 2014

Jan. 29, 2014

Source: National Climatic Data Center.

since we use 65 years of data. Third, we calculate the
monthly deviation as the difference between the monthly
∧
average and the long-run normal, or Ts ,m , y = Ts ,m , y − Ts ,m .
This yields a measure of temperature deviation from
its normal. It is important to note here that stations
naturally experience different levels of variation: A
day 20 degrees above or below normal will be more
common in Minneapolis than in San Diego. Therefore,
it is intuitive to normalize the monthly deviation by
a measure of variability.14 Hence, in the fourth step,
we calculate a station- and month-specific measure
of variability: the standard deviation across years of
the monthly temperature Ts ,m , y , which we denote
σTs ,m . The mathematical formula is
σTs ,m =

(

)

2
1
Ts ,m , y − Ts ,m .
∑
65 y

We then define the normalized deviation as the
ratio of deviation to this standard deviation:
∧

T
Ts ,m , y = s ,Tm , y .
σ s ,m

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The next step involves aggregating over all weather
stations within a state. A refined approach would be to
weight stations according to the population surrounding them, since economic activity is correlated with
population. However, in the interests of simplicity,
we calculate the simple average of Ts ,m , y across all
stations in a state; this yields a temperature index that
we denote Ti,m, y (where i denotes the state):
∧

Ti ,m , y =

1
∑ Ts,m, y .
N i ,m ,y s∈i

Ni,m, y is the number of stations in state i in month m
and year y, and the sum runs over all stations s in a
state i. In a final step, we normalize this index so it
has mean zero and standard deviation one:
Ti ,m , y =

(

)

Ti ,m , y − E Ti ,m , y
,
σ T

(

i ,m , y

)

where E and σ denote the mean and standard deviation, calculated over the winter months.
One potential concern is that the simple average
across all stations might be misleading if the weather
is very different across the state. Figure 2 presents the
temperatures of all stations in Illinois in January 2014;

5

even in this fairly large state, the co-movement of
temperatures is striking. This suggests that the simple
average may be good enough for our purposes.
We also construct regional (Midwest, West, Northeast, and South) and national weather indexes by weighting the state indexes according to their employment.
Finally, in exactly the same way, we construct a snowfall index, replacing temperature T with snowfall data
S. In this case, the lumpier nature of snowfall makes
our simple averaging within a state less compelling,
though figure 3 suggests that there is still some significant co-movement.
Figure 4 shows a scatter plot of our monthly statelevel indexes of temperature and snow. As could be
expected, the negative correlation is fairly strong (–0.49).
Finally, figures 5 and 6 depict the correlograms of our
temperature and snow indexes respectively; these figures
provide a visual way to assess how long a good temperature (or snow) index lasts. While there is some significant correlation over a few days, we see that the
correlation falls fairly quickly, especially for snowfall.
The 2013–14 winter in perspective
Much of the past winter’s cold temperatures was
caused by the “polar vortex,” a low-pressure weather
system that typically stays above the Arctic Circle
during the winter, spinning in a tight bowl over high
latitudes. It is held in place by the jet stream, a fastmoving, high-altitude wind that keeps cold air to the
north and warmer air from the south from interacting.
However, during the winter of 2013–14, the polar
vortex slowed down, causing it to “wobble,” much
like a spinning top that loses momentum. This pushed
the jet stream farther south than normal, bringing the
cold arctic winds to lower latitudes.
The severity of the winter can be seen in figures 7
and 8, which show the deviation of first-quarter average
temperatures and snowfall from the long-term averages.
Clearly, this winter was cold and snowy, but the polar
vortex did not impact the country evenly. Figures 9
and 10 (p. 10) show the weather deviations for each
region. Temperatures were above average in the West
of the United States. For the eastern half of the country,
however, the winter was brutally cold. The first-quarter
average temperature in the Midwest was about two
standard deviations below the mean, making it the
third coldest in our data and fairly similar to the two
worst ones, 1979 and 1980. The Northeast similarly
had its third-coldest first-quarter temperature in our
data, and the South experienced its sixth coldest. As
well as being cold, the first quarter of 2014 was also
snowy in the Midwest, Northeast, and South.

6

Empirical approach using state-level data
To evaluate how weather affects economic activity,
we use a commonly used statistical model known as regression analysis. The equation describing the model is
1)	 ΔlogYi, m,  y = αi + δm,  y + βTi, m,  y + γSi, m,  y + εi, m,  y ,
where ΔlogYi, m,  y is the change in the logarithm of a variable measuring seasonally adjusted economic activity
(such as employment) in state i in month m of year
y;15 Ti, m,  y is our temperature index for state i in month
m of year y; Si, m,  y is our snow index. The factors αi
and δm,   y are so-called fixed effects, that is, constants
that depend solely on the state (αi ) or time (δm,  y ). These
factors serve to capture, respectively, the fact that some
states grow faster on average and that all states tend
to co-move, for instance, due to economic recessions.
By removing this variation from the data, we obtain
statistically more precise estimates of the weather effect.
Finally, εi, m,  y is a so-called error term that captures factors
other than temperature and snow, not constant across
time or states, that affect economic activity.
The key assumption underlying this model is that
these factors are uncorrelated with the temperature index
Ti, m,  y and with the snow index Si, m,  y. This is plausible
in our case since short-term variations in weather are
unlikely to be caused by the factors thought to affect
economic activity, such as productivity, interest rates,
or consumer confidence.16 This allows us to estimate
the model using a simple technique known as OLS
(ordinary least squares).17 It is important to note that
this model imposes several assumptions: First, the
effect of our weather indexes on the growth rate of
economic activity is linear, so that the effect of a one
standard deviation increase in the temperature index
is half the effect of a two standard deviation increase
in the temperature index, and the exact opposite of a
standard deviation increase in the temperature index.
One might think that this is an unrealistic assumption.
For example, in January 2014, the very low temperatures in Chicago had the extreme effect of leading
many people not to commute to work, so perhaps the
effect of very low temperatures is more than proportional. We performed some exploratory analysis and
did not find support for such nonlinearities. For instance, one can create indexes to capture “extreme
cold” or “extreme snow” by counting the number of
days within a month with very low temperatures or
very high snowfall. These indexes do not seem to
convey important additional information relative to
our simple index. However, this certainly deserves
more study. A second important implicit assumption
is that the effect of a high weather index is the same

1Q/2015, Economic Perspectives

FIGURE 3

Snowfall in all stations in Illinois
inches

30

20

10

0
Jan. 1, 2014

Jan. 8, 2014

Jan. 15, 2014

Jan. 22, 2014

Jan. 29, 2014

Source: National Climatic Data Center.

FIGURE 4

Correlation between temperature and snow (winter months)
snow index
10

5

0

–5
−4

−2

0
temperature index

2

4

Source: Authors’ calculations based on data from the National Climatic Data Center.

Federal Reserve Bank of Chicago

7

FIGURE 5

Correlogram of temperature index
autocorrelation

0.80

0.60

0.40

0.20

0.00
0

5

10
days

15

20

15

20

Source: Authors’ calculations based on data from the National Climatic Data Center.

FIGURE 6

Correlogram of snowfall index
autocorrelation

0.40

0.30

0.20

0.10

0.00
0

5

10
days

Source: Authors’ calculations based on data from the National Climatic Data Center.

8

1Q/2015, Economic Perspectives

FIGURE 7

National temperature deviation from 1950–2014 average
degrees Fahrenheit
5

0

−5
1950

1970

year

1990

2010

Source: Authors’ calculations based on data from the National Climatic Data Center.

FIGURE 8

National snowfall deviation from 1950–2014 average
inches
.15

.10

.05

0

−.05

−.10
1950

1970

year

1990

2010

Source: Authors’ calculations based on data from the National Climatic Data Center.

Federal Reserve Bank of Chicago

9

FIGURE 9

Regional temperature index for first quarter
A. Midwest temperature
Deviation from 1950–2014 average
Fahrenheit degrees

B. Northeast temperature
Fahrenheit degrees
10

10
5

5
0

0

−5
−5

−10
1950

1970

year

1990

2010

C. South temperature
Fahrenheit degrees

1950

1970

year

1990

2010

1990

2010

1990

2010

1990

2010

D. West temperature
Fahrenheit degrees
4

4
2

2

0

0

−2

−2

−4
−6

−4
1950

1970

year

1990

2010

1950

1970

year

Source: Authors’ calculations based on data from the National Climatic Data Center.

FIGURE 10

Regional snow index for first quarter
A. Midwest Snow Index
Deviation from 1950–2014 average

B. Northeast Snow Index

inches

inches
.3

.2

.2
.1

.1

0

0

−.1

−.1

−.2
1950

1970

year

1990

2010

1950

1970

C. South Snow Index

D. West Snow Index

inches

inches
.3

.10

year

.2
.05
.1
0

0
−.1

−.05
1950

1970

year

1990

2010

1950

1970

year

Source: Authors’ calculations based on data from the National Climatic Data Center.

10

1Q/2015, Economic Perspectives

TABLE 1

Effect of temperature and snowfall indexes on state-level economic activity
	

Nonfarm	
payrolls	

Unemployment	
rate	

U.I. new	
claims	

Housing	
permits 	

Housing
starts

Temperature 	
	

0.041*** 	
(0.008) 	

– 0.034 	
(0.253) 	

– 0.967***	
(0.260) 	

1.124* 	
(0.648) 	

2.431***
(0.889)

Snowfall 	
	

– 0.029*** 	
(0.006) 	

0.100 	
(0.153) 	

0.861*** 	
(0.191) 	

– 2.091*** 	
(0.451) 	

–1.905***
(0.595)

Observations 	
R2 	
Sample start 	

37,154 	
0.262 	
1950 	

22,517 	
0.451	
1976 	

25,084 	
0.148 	
1971 	

19,900 	
0.118 	
1980 	

25,660
0.098
1970

	

Notes: Results from estimation of equation 1 using monthly data from November through March by ordinary least squares with state and time
effects; standard errors in parentheses are two-way clustered by state and time. The left-hand-side variables are all in log changes, except the
unemployment rate, which is in level change. Sample start date as shown; end date is 2014 for all series. U.I. indicates unemployment insurance.
Temperature and snowfall indexes are normalized to have mean zero and standard deviation one for each state. Asterisks indicate statistical
significance at the 10 percent (*), 5 percent (**), and 1 percent (***) level.
Source: Authors’ calculations based on data from the National Climatic Data Center.

in all states. As we discussed above, our weather indexes are normalized to have the same standard deviation in all states, which makes this assumption more
plausible, but it also deserves more study. A third assumption is that the average weather during the
month is the relevant metric; for instance, we do not
differentiate bad weather during the week from bad
weather during the weekend.
Equation 1 estimates the effect of a given
month’s weather on the same month’s economic variable Y. An important question is how long these effects last. To answer this question, we also estimate
the same model, but allowing for lags in the weather:
K

K

k =0

k =0

2) ∆ log Yi ,m , y = α i + δ m , y + ∑ βk Ti ,m−k , y + ∑ γ k Si ,m−k , y
+ εi ,m , y ,
that is, the weather in the previous K months may
affect Y. This specification allows us to evaluate the
strength and speed of the bounceback from a bad
weather spell.18 The coefficient β0 measures the effect
in a given month of a one standard deviation change
in the temperature index that month, the coefficient β1
is the effect the following month, and so on.
Finally, note that we estimate this equation using
the “cold season” only. That is, we define the temperature and snowfall indexes for November through March
only (and allow the lags to work through until K months
later). While weather affects the economy in the summer as well, the effects are likely to be different—for
instance, high temperatures might have negative effects
rather than positive effects and rainfall rather than snowfall might be relevant. This requires a different model.

Federal Reserve Bank of Chicago

Our analysis requires us to measure economic activity at the state level and at high frequency. Unfortunately,
there is a relative paucity of economic data at this level
of regional disaggregation and at this frequency. The
main source of our data is the Bureau of Labor Statistics’ Current Establishment Survey (CES), which surveys a large number of establishments each month
regarding how many employees are on the payroll
(during the pay period of the week including the 12th
of the month). The headline national number (“nonfarm
payrolls”) is released the first Friday of the following
month to considerable attention, but the same survey
also produces monthly estimates of employment in
each state and in each industry. These are our main
sources of data. We also use monthly data on new
unemployment insurance claims, housing starts, and
housing permits.19 Finally, we exclude Alaska and
Hawaii from our analysis given their distinctive
weather patterns and small population.
Results using state-level data
We first present the immediate effect of the weather,
then discuss the bounceback. Finally, we study whether
the economy has become less sensitive to weather
over time.
Immediate effect
Table 1 presents the estimates of equation 1, which
shows the effects of temperature and snowfall on total
nonfarm employment, the unemployment rate, new
unemployment insurance claims, housing permits, and
housing starts. (Note that the economic data become
available in different years, depending on the specific
statistic.) A one standard deviation increase in the

11

		

TABLE 2

 
Effect of temperature and snowfall on the subcomponents of state-level nonfarm employment

	
Private nonfarm payrolls (A+B) 	
A. Goods 	
1. Mining 	
2. Construction 	
3. Manufacturing 	
3a. Durable manufacturing 	
3b. Nondurable manufacturing 	
B. Private services 	
B1. Trade, transportation, and utilities 	
B1a. Wholesale 	
B1b. Retail 	
B1c. Transportation and utilities 	
B2. Information 	
B3. Financial services 	
B4. Professional and business services 	
B5. Education and health 	
B6. Leisure and hospitality 	
B6a. Accommodation and food 	
B6b. Arts and leisure	
B7. Other services 	
C. Total government 	
C1. Federal government 	
C2. State government 	
C3. Local government 	

Temperature	
0.024*** 	
0.080*** 	
– 0.016	
0.185*** 	
0.036** 	
– 0.077	
– 0.003	
0.009	
0	
0.011	
– 0.003	
0.005	
0.013	
0.017	
0.003	
0.009	
0.041** 	
0.043*** 	
0.054	
– 0.019	
0.026** 	
0.007 	
– 0.007	
0.040*** 	

Snowfall	

(0.007) 	
(0.016) 	
(0.108) 	
(0.045) 	
(0.016) 	
(0.132) 	
(0.014) 	
(0.006) 	
(0.008) 	
(0.012) 	
(0.010) 	
(0.018) 	
(0.021) 	
(0.012) 	
(0.019) 	
(0.008) 	
(0.016) 	
(0.016) 	
(0.045) 	
(0.013) 	
(0.011) 	
(0.030) 	
(0.016) 	
(0.015) 	

– 0.031*** 	
– 0.056*** 	
– 0.149* 	
– 0.181*** 	
0.001 	
– 0.093	
0.004 	
– 0.025*** 	
– 0.024*** 	
– 0.015 	
– 0.033*** 	
– 0.003 	
0.021 	
0.001 	
– 0.014 	
– 0.022*** 	
– 0.067*** 	
– 0.062*** 	
– 0.079	
– 0.043*** 	
– 0.015 	
– 0.010	
– 0.030 	
– 0.021 	

(0.006) 	
(0.014) 	
(0.084) 	
(0.032) 	
(0.011) 	
(0.096) 	
(0.014) 	
(0.006) 	
(0.008) 	
(0.011) 	
(0.010) 	
(0.014) 	
(0.013) 	
(0.011) 	
(0.016) 	
(0.008) 	
(0.014) 	
(0.014) 	
(0.051) 	
(0.012) 	
(0.010) 	
(0.016) 	
(0.022) 	
(0.014) 	

Observations 	

R2

14,143 	
14,143 	
12,067 	
13,377 	
13,828 	
12,226 	
13,257 	
14,143 	
14,143 	
13,545 	
14,143 	
13,848 	
12,679 	
14,143 	
14,143 	
14,143 	
14,143 	
13,839 	
13,388 	
14,143 	
14,143 	
13,393 	
13,819 	
13,963 	

0.426
0.339
0.058
0.273
0.235
0.950
0.125
0.353
0.322
0.165
0.259
0.184
0.126
0.142
0.213
0.092
0.148
0.146
0.066
0.069
0.133
0.624
0.027
0.061

Notes: Results from estimation of equation 1 using monthly data from November through March by ordinary least squares with state and time
effects; standard errors in parentheses are two-way clustered by state and time. All left-hand-side variables are in log changes. Sample is
1990–2014. Temperature and snowfall indexes are normalized to have mean zero and standard deviation one for each state. Asterisks indicate
statistical significance at the 10 percent (*), 5 percent (**), and 1 percent (***) level.
Source: Authors’ calculations based on data from the National Climatic Data Center.

temperature index during the winter leads nonfarm
employment to grow by 0.04 percent, while a one
standard deviation increase in the snowfall index leads
to a decline of 0.03 percent. While these percentages
are small, they can be important relative to the usual
month-to-month fluctuations. For example, nonfarm
payrolls are around 140 million nationwide, so the
0.04 percent estimated effect of temperature amounts
to a difference of around 56,000 employees, which can
make the difference between a “good” labor report and
an “average” one. Importantly, the effects here are
highly statistically significant, but the precision of the
estimate is not extremely high. This may reflect the
complexity of measuring the weather accurately.
The effect on the unemployment rate has the expected sign: A one standard deviation increase in the
temperature index lowers the unemployment rate by
0.03 percentage points, and a one standard deviation
increase in the snowfall index increases the unemployment rate by one-tenth of a percentage point (for instance, the unemployment rate would go from 5.8 to
5.9 percentage points). However, these effects are not

12

statistically significant. The effects on new unemployment insurance claims, housing permits, and housing
starts are all highly significant, of the expected sign,
and fairly large: A one standard deviation shock to either
snowfall or temperature moves new claims by about
1 percent and housing starts and permits by 1–2 percent.
These are larger effects, but the underlying series are
also more volatile, so that a percentage point difference
is not extremely important.
Table 2 provides a breakdown of the employment
effects by industry.20 Some industries stand out as
particularly affected, notably construction, hospitality,
and, to a lesser extent, retail. Manufacturing is affected
more by temperature than by snowfall. There is also
an effect on education and health services, as well as
government employment (the latter are very volatile,
making the snowfall results not significant, despite
the large negative point estimates). These education
and government values may partly reflect school closures during bad weather. Overall, the results suggest
that both the “supply” and “demand” channels are at
work during the weather-related slowdown; that is,

1Q/2015, Economic Perspectives

some sectors contract because it is impossible to produce, while some contract because there is less demand
for their services.
Beyond showing the mechanics of the weather
effect, these industry responses are useful because they
allow us to identify episodes in which the weather may
be an important driver of the economy. Thus, if a slowdown is associated with a large decline in construction,
hospitality, and retail, it may in fact be weather related (even if the weather is not well measured).

reports these results. The temperature sensitivity of
nonfarm employment, unemployment insurance claims,
and housing permits and starts appears to have declined,
though not all these changes are statistically significant.
However, the snowfall sensitivity appears to have remained constant (for nonfarm employment) and may
be even larger (for permits and starts). These last results could be due to structural changes in the homebuilding industry, such as the lengthening of the
homebuilding season.

Bounceback
To evaluate how long the effects of weather last,
we estimate equation 2 with three lags (K = 3). These
results are in table 3. The same-month impact effect
of weather is obtained for each economic time series
in the row k = 0 for temperature and snowfall, respectively. These are very similar to the effects found in
table 1.21 The novel result in this table is that last month’s
weather typically affects these economic time series
with the opposite sign. For instance, a higher temperature index pushes nonfarm employment up by 0.043
percent the first month, but this recedes by 0.009 percent the next month and 0.025 percent the following
month. This means that after two months, the effect
of a higher temperature has largely receded. This pattern
is very general across all time series, suggesting a strong
bounceback, such that the level of economic activity
returns roughly to where it was before the weather. A
formal test of the hypothesis that a temperature shock
has no effect on the level of economic activity three
months later can be formulated as ∑ kK=0 βk = 0; and
for snowfall the test is ∑ kK=0 γ k = 0. This amounts to
a test of whether the weather only has transitory effects.
For all of the series studied here, we cannot reject the
hypothesis that weather has only transitory effects.22
Overall, our results strongly support the intuitive notion
of a bounceback.23 The bounceback usually happens
within a month or two, though in at least one case
(nonfarm employment) there appears to be some remaining bounceback three months later.

Methodology and results using national data

Has the weather sensitivity declined?
The U.S. economy has changed in many ways since
the 1960s and 1970s. New technologies for homebuilding have been developed, just-in-time inventory
systems have been introduced, and there has been a
shift away from industry and toward services. It is
possible that as a result of these changes, the weather
has less of an impact on the economy now than it once
had. To evaluate this hypothesis, we estimate separately the effects of both temperature and snow on two
subsamples: prior to 1990 and after 1990.24 Table 4

Federal Reserve Bank of Chicago

In this section, we present our results using national
data. The main advantage of using national data is that
there are many more economic data series available at
a monthly frequency at the national rather than state
level. The key disadvantage, which will become obvious as we proceed, is that by discarding regional
variation in the weather, we have less data available,
which does not allow as precise estimates of the weather
effect. In part, this reflects the difficulty of disentangling the effects of snowfall and temperature, which
are strongly negatively correlated (–0.60) in our national data.
Our methodology here is similar to our state-level
work. We first construct a national temperature index
and a national snowfall index by weighting the state
indexes using nonfarm employment:
48

Tm , y = ∑ ωi ,m , yTi ,m , y ,
i =1

where ωi,m, y is the share of national nonfarm employment in state i in month m of year y; and similarly for
snowfall. We then run a simple time-series regression
of an economic indicator on our national weather
indexes:
3) 	 ΔlogYm,  y = α + βTm,  y + γSm,  y + εm,  y ,
and later on allow for lags to capture the bounceback:
K

K

k =0

k =0

4)	 ∆ log Ym , y = α + ∑ βk Tm− k , y + ∑ γ k S m−k , y + ε m , y .
Table 5 presents the results. Overall, there are
fewer statistically significant results, and often the
temperature coefficient β has the “wrong” sign. For
instance, the effect of snowfall on nonfarm employment is negative, but so is the temperature effect, so
that this equation predicts that lower temperatures
lead to higher employment. Both effects are statistically insignificant. This pattern is fairly general,
though in the cases of industries or activities that are

13

		

TABLE 3

Effect of temperature and snowfall on state-level economic indicators with lags
	
Lag	
Temperature
Current month (k = 0) 	
	

Nonfarm	
Unemployment	
employment	
rate	

0.043*** 	
(0.007) 	

U.I. new	
Housing	
Housing	
claims	permits	 starts

–   0.011 	
(0.247) 	

– 1.269*** 	
(0.280) 	

– 0.065 	
(0.262) 	

1.357*** 	
(0.347) 	

Last month (k = 1) 	
	

– 0.009 	
(0.007) 	

Two months ago (k = 2) 	
	

– 0.025*** 	
(0.008) 	

0.174 	
(0.232) 	

Three months ago (k = 3)	
	

0.006 	
(0.007) 	

1.568*** 	
(0.683) 	

3.128***
(0.915)

– 1.577** 	
(0.724) 	

– 3.120***
(0.888)

– 0.053 	
(0.232) 	

– 1.111*** 	
(0.458) 	

– 0.239
(0.827)

0.104 	
(0.216) 	

0.109 	
(0.197) 	

0.033 	
(0.495) 	

– 0.917	
(0.855)

– 0.027*** 	
(0.004) 	

0.091 	
(0.152) 	

0.848*** 	
(0.189) 	

Last month (k = 1) 	
	

0.009 	
(0.005) 	

0.113 	
(0.180) 	

Two months ago (k = 2) 	
	

0.011*** 	
(0.006) 	

Three months ago (k = 3) 	
	
Observations 	
R2 	
Sample start 	

Snowfall
Current month (k = 0) 	
	

– 2.038*** 	
(0.445) 	

– 1.891***
(0.598)

– 0.275 	
(0.234) 	

0.944* 	
(0.516) 	

0.693
(0.553)

0.104 	
(0.149) 	

– 0.457** 	
(0.190) 	

0.385 	
(0.548) 	

1.222*
(0.665)

0.015*** 	
(0.005) 	

– 0.103 	
(0.187) 	

0.008 	
(0.193) 	

0.483 	
(0.330) 	

0.255
(0.468)

36,964 	
0.294 	
1950 	

22,466 	
0.449 	
1976 	

25,036 	
0.152 	
1971 	

19,852 	
0.120 	
1980 	

25,612
0.101
1970

Notes: Results from estimation of equation 2 using monthly data from November through March by ordinary least squares with state and time
effects; standard errors in parentheses are two-way clustered by state and time. All left-hand-side variables are in log changes except the
unemployment rate, which is in difference. U.I. indicates unemployment insurance. Temperature and snowfall indexes are normalized to have
mean zero and standard deviation one for each state. Asterisks indicate statistical significance at the 10 percent (*), 5 percent (**), and 1 percent
(***) level.
Source: Authors’ calculations based on data from the National Climatic Data Center.

		

TABLE 4

Effect of temperature and snowfall on measures of state-level economic activity, pre- and post-1990
	
	

Nonfarm	 Unemployment	 U.I.	 Housing	Housing
employment	
rate	
claims	permits	 starts

Temperature, pre-1990 	
	

0.050*** 	
(0.011) 	

–0.588 	
(0.373) 	

–1.369*** 	
(0.359) 	

1.227 	
(1.371) 	

4.207***
(1.350)

Temperature, post-1990 	
	

0.023*** 	
(0.006) 	

0.374 	
(0.325) 	

–0.580* 	
(0.341) 	

1.049* 	
(0.619) 	

0.618
(0.974)

Snowfall, pre-1990 	
	

–0.030*** 	
(0.008) 	

0.056 	
(0.219) 	

0.663** 	
(0.261) 	

–0.987 	
(0.870) 	

–0.379
(0.745)

Snowfall, post-1990 	
	

–0.026*** 	
(0.006) 	

0.143 	
(0.214) 	

1.069*** 	
(0.261) 	

–2.650*** 	
(0.632) 	

–3.563***
(1.062)

Observations 	
R2 	

37,154 	
0.262 	

22,517 	
0.451 	

25,084 	
0.148 	

19,900 	
0.118 	

25,660
0.099

Notes: Results from estimation of equation 1 using monthly data from November through March by ordinary least squares
with state and time effects; standard errors in parentheses are two-way clustered by state and time. The left-hand-side
variables are all in log changes, except the unemployment rate, which is in level change. U.I. indicates unemployment
insurance. Temperature and snowfall indexes are normalized to have mean zero and standard deviation one for each state
and are interacted with two dummies, pre- and post- 1990. Asterisks indicate statistical significance at the 10 percent (*),
5 percent (**), and 1 percent (***) level.
Source: Authors’ calculations based on data from the National Climatic Data Center.

14

1Q/2015, Economic Perspectives

		

TABLE 5

 
Effect of temperature and snowfall on national economic indicators

	
Nonfarm employment 	
Unemployment rate 	
Private nonfarm employment 	
Construction employment 	
Retail sales (excluding cars) 	
Private average hours per worker 	
Industrial production (IP): Total 	
IP: Manufacturing	
IP: Utilities 	
Lightweight vehicle sales 	
CFNAI 	
New orders of core capital goods 	
Shipments of core capital goods 	
Housing starts 	
Housing permits 	
Purchasing Managers Index 	

Temperature 	
–  0.01 	
– 0.15 	
– 0.03 	
0.163* 	
0.03 	
– 0.05* 	
– 0.061 	
0.035 	
– 1.504*** 	
– 0.97 	
– 0.053 	
– 0.46 	
– 0.467* 	
0.92 	
0.58 	
– 0.069 	

Snowfall 	

(0.028) 	
(0.151) 	
(0.031) 	
(0.098) 	
(0.077) 	
(0.028) 	
(0.126) 	
(0.137) 	
(0.159) 	
(0.595) 	
(0.085) 	
(0.442) 	
(0.281) 	
(0.629) 	
(0.480) 	
(0.543) 	

– 0.009 	
– 0.16 	
– 0.014 	
– 0.191*** 	
– 0.132* 	
– 0.181*** 	
– 0.14 	
– 0.13 	
– 0.23 	
– 1.407** 	
– 0.12 	
– 1.573*** 	
– 0.610** 	
– 2.270*** 	
– 0.949** 	
0.45 	

(0.028) 	
(0.151) 	
(0.031) 	
(0.098) 	
(0.079) 	
(0.029) 	
(0.126) 	
(0.137) 	
(0.167) 	
(0.610) 	
(0.088) 	
(0.450) 	
(0.286) 	
(0.631) 	
(0.482) 	
(0.543) 	

Observations	
854 	
791 	
854 	
854 	
571 	
608 	
979 	
979 	
511 	
572 	
570 	
270 	
271 	
667 	
655 	
791 	

R2
0.002
0.000
0.001
0.021
0.011
0.074
0.001
0.002
0.210
0.009
0.003
0.054
0.017
0.055
0.022
0.002

Notes: Results from estimation of equation 3 using monthly data from November through March of all years by ordinary least squares (OLS).
Standard errors in parentheses are simple OLS. All left-hand-side variables are in log changes, except for the unemployment rate (in change) and
CFNAI (in level). Temperature and snowfall indexes are normalized to have mean zero and standard deviation one. Asterisks indicate statistical
significance at the 10 percent (*), 5 percent (**), and 1 percent (***) level.
Source: Authors’ calculations based on data from the National Climatic Data Center.

heavily affected by temperature or snowfall we do obtain
clear and intuitive results. For instance, the coefficients
on construction employment are similar to those obtained at the state level (0.163 on temperature and
–0.191 on snowfall, compared with 0.185 and –0.181
for state-level data). Average hours worked, retail and
car sales, housing starts and permits, and shipments
and order of new capital goods are all affected significantly by snowfall. In the case of utilities production,
the very strong negative effect of temperature is likely
not an artifact but simply reflects the higher demand
for heating. This shows that some sectors of the economy
react positively to cold weather. Overall, the general
message is that snowfall seems better at capturing the
effect of weather on the economy, but these effects are
more difficult to measure using aggregate data alone.
While it is reassuring that the magnitudes of the
effects are similar (where available) in both exercises,
this need not be the case. For instance, if there are spillovers across states such that bad weather in one state
negatively affects economic activity in another state,
and if the weather is positively correlated across the
two states, our state-level regression would overestimate the effect of local weather. However, these spillovers are likely to be small.
Table 6 studies the bounceback in the national
data by adding lags to equation 3. As in the state-level
data, we find significant evidence of bounceback for
the categories that are highly affected by temperature

Federal Reserve Bank of Chicago

or snowfall. For instance, snowfall is estimated to reduce car sales by 1.3 percent on impact, but the bounceback is estimated to be 1.27 percent the next month.
Similarly, average hours fall by 0.17 percent then rebound by 0.13 percent the next month. In many cases,
however, the bounceback is estimated imprecisely, probably due to sparseness of data at the national level.
Revisiting the 2013–14 weather contribution
We are finally in a position to estimate the effect of
the 2013–14 winter on economic activity. We present
two sets of results—the first one based on the national
model of the previous section and the second based
on the state-level model. In all cases, we simply use
the actual weather observed during the winter, together
with the sensitivities estimated using historical data (that
is, our estimates of β and γ), to obtain the effect of the
observed weather on the growth rates of these economic
indicators. Table 7 presents the results based on the statelevel model (which is more precisely estimated), while
table 8 (p. 18) shows the national results.25 In the first
row of table 7, we see that nonfarm employment displays the slowdown presented in figure 1 (p. 2): Employment growth rates of 0.06 percent in December and
0.1 percent in January were below the recent trend of
about 0.16 percent (that is, 200,000 jobs created per
month). The second row shows that, according to our
estimates, the weather contributed negatively to the
growth rate of nonfarm employment from November

15

16

		

TABLE 6

 
Effect of temperature and snowfall on national economic indicators with lags

	

Temperature 	

Snowfall

						
	
Current month 	
Last month 	
Two months ago 	
Current month 	
Last month 	
Nonfarm employment	
Unemployment rate 	
Private nonfarm employment 	
Construction employment 	
Retail sales (excluding cars) 	
Private average hours per worker 	
Industrial production (IP): Total 	
IP: Manufacturing 	
IP: Utilities 	
Lightweight vehicle sales 	
New orders of core capital goods 	
New orders of core capital goods 	
Shipments of core capital goods 	
Housing starts 	
Housing permits 	
Purchasing Managers Index 	

– 0.002 	 (0.028) 	
– 0.12 	
(0.152) 	
0.004 	 (0.031) 	
0.193** 	 (0.092) 	
0.094 	 (0.078) 	
–  0.014 	 (0.028) 	
– 0.063 	 (0.120) 	
0.037 	 (0.130) 	
– 1.636*** 	 (0.145) 	
– 0.62 	
(0.605) 	
– 0.014 	 (0.087)	
– 0.34 	
(0.446) 	
– 0.522* 	 (0.283) 	
1.345** 	 (0.620) 	
0.70 	
(0.475) 	
– 0.096 	 (0.552) 	

–0.033 	
0.25 	
– 0.043 	
– 0.252*** 	
– 0.10 	
– 0.044 	
– 0.061 	
– 0.087 	
0.906*** 	
– 0.23 	
0.023 	
0.19 	
0.11 	
– 1.783*** 	
– 1.578*** 	
– 0.036 	

(0.028) 	
(0.152) 	
(0.031) 	
(0.092) 	
(0.078) 	
(0.028) 	
(0.120) 	
(0.130) 	
(0.146) 	
(0.606) 	
(0.087) 	
(0.464) 	
(0.295) 	
(0.621) 	
(0.476) 	
(0.553) 	

0.011 	
– 0.21 	
0.005 	
– 0.082 	
– 0.011 	
0.034 	
0.037 	
– 0.006 	
0.536*** 	
0.001 	
0.097 	
– 0.032 	
0.608** 	
0.28 	
1.239*** 	
– 0.035 	

(0.027)	
(0.150) 	
(0.030) 	
(0.090) 	
(0.077) 	
(0.027) 	
(0.118) 	
(0.128) 	
(0.143) 	
(0.596) 	
(0.086) 	
(0.462) 	
(0.294) 	
(0.610) 	
(0.470) 	
(0.543) 	

–0.011 	
– 0.12 	
– 0.016 	
– 0.217** 	
– 0.12 	
– 0.170*** 	
– 0.15 	
– 0.14 	
– 0.11 	
– 1.303** 	
– 0.11 	
– 1.621*** 	
– 0.659** 	
– 2.375*** 	
– 1.114** 	
0.42 	

(0.027)	
(0.150) 	
(0.030) 	
(0.091) 	
(0.079) 	
(0.028) 	
(0.118) 	
(0.128) 	
(0.152) 	
(0.613) 	
(0.088) 	
(0.467) 	
(0.297) 	
(0.616) 	
(0.472) 	
(0.544) 	

0.011 	 (0.027) 	
0.19 	
(0.151) 	
0.002 	 (0.030) 	
– 0.017 	 (0.091) 	
0.194** 	 (0.080) 	
0.126*** 	 (0.028) 	
0.047 	 (0.119) 	
0.050 	 (0.129) 	
0.094 	 (0.152) 	
1.274** 	 (0.621) 	
0.179** 	 (0.089) 	
0.69 	
(0.467) 	
0.056 	 (0.296) 	
0.87 	
(0.620) 	
– 0.18 	
(0.475) 	
0.18 	
(0.548) 	

Two months
ago
0.027
– 0.21
0.032
0.264***
– 0.015
0.008
– 0.003
– 0.004
– 0.24
– 0.051
0.15
0.46
0.624**
1.846***
2.004***
0.087

1Q/2015, Economic Perspectives

Notes: Results from estimation of equation 4 using monthly data from November through March of all years by ordinary least squares (OLS). Simple OLS standard errors are reported in parentheses. All left-handside variables are in log changes, except for the unemployment rate (in change) and CFNAI (in level). Temperature and snowfall indexes are normalized to have mean zero and standard deviation one. Asterisks
indicate statistical significance at the 10 percent (*), 5 percent (**), and 1 percent (***) level.
Source: Authors’ calculations based on data from the National Climatic Data Center.

		

TABLE 7

 
Estimated effect of 2013–14 winter using state model

		
Nonfarm employment 	
	
Unemployment rate 	
	
New unemployment 	
insurance claims	
Housing permits 	
	
Housing starts 	
	

Data 	
Weather effect	
Data 	
Weather effect 	
Data 	
Weather effect 	
Data	
Weather effect	
Data 	
Weather effect 	

Nov. 	
0.20 	
–  0.02 	
– 0.20 	
– 0.02 	
– 5.30 	
0.24 	
– 2.85 	
– 0.59 	
16.60 	
– 1.71 	

Dec. 	

Jan. 	

0.06 	
0.10 	
– 0.02 	 – 0.01 	
– 0.30 	 – 0.10 	
0.03 	 – 0.05 	
8.27 	 – 9.27 	
0.22 	 – 0.07 	
– 1.46 	 – 8.47 	
– 0.32 	
0.95 	
– 6.64 	 – 14.21 	
– 0.27 	
0.85 	

Feb. 	
0.16 	
– 0.04 	
0.10 	
0.06 	
5.43 	
0.98 	
7.39 	
– 1.04 	
3.40 	
– 0.67 	

Mar.	

Apr. 	

May

0.15 	
0.00 	
0.00 	
0.03 	
– 3.26 	
– 0.31 	
– 1.09 	
1.11 	
2.34 	
0.22 	

0.22 	
0.03 	
– 0.40 	
– 0.03 	
– 2.29 	
– 1.13 	
5.73 	
1.51 	
11.24 	
3.35 	

0.17
0.03
0.00
– 0.27
– 0.83
0.08
– 5.23
1.08
– 7.72
0.62

Notes: Based on state model with three lags. All results are in percentage growth rates, except for the unemployment rate, which is the change
in percentage points.
Source: Authors’ calculations based on data from the National Climatic Data Center.

through February, to the tune of 0.04 percent in February, or about 50,000 to 60,000 jobs. The weather
effects are then reversed in April and May. However,
the weather hardly accounts for the weak December
and January employment numbers. Similarly, the unemployment rate grew by 0.06 percentage points in
February due to weather, according to these estimates.
Housing permits and starts were also affected in a significant way, but the estimated effects (about 1 percent)
fall short of the observed magnitude of the decline in
the data (14 percent for starts and 8 percent for permits
in January, for instance).
In table 8, we see that the results with national
data have the same flavor, but are less clear perhaps
due to the imprecision of the estimation. For instance,
the weather effect is now estimated to be positive for
nonfarm employment during most months. However,
this relies on an equation that was insignificant. More
sensible results are obtained for construction employment, retail sales, average hours worked, and lightweight
vehicle sales. For instance, hours fell 0.6 percent in
February, of which 0.19 percent is attributed to the
weather. Utility production grew 3.3 percent in January,
of which 0.52 percent is attributed to the weather. The
decline in starts and permits due to weather is about 3
percent. However, the timing does not fit the observed
decline in indicators well. For instance, the CFNAI
fell sharply in January, and our model attributes little
of this to the weather; and housing starts and permits
rebounded in February, contrary to our model’s prediction. Overall, while some of the patterns observed
in the data can be attributed in part to weather, this
explanation is insufficient to explain the magnitude
and timing of the slowdown.

Federal Reserve Bank of Chicago

How does climate change affect our results?
It is important to note the potential impact of climate change on our study. When we construct our
weather index, we normalize by a base value, which
we take to be simply the long-run average (1950–2014).
However, it is conceivable that given rising global
temperatures, the typical temperature in the United
States increased during the period of observation. This
would make, for example, a 25-degree day in November
more anomalous in 2014 than in 1950. As noted above,
our weather data are not adjusted for changes in instrumentation and other measurement issues. Without
these adjustments, it is difficult to detect a trend in
temperature.26 However, in some cases it is possible
to observe a positive trend starting in 1980, which is
consistent with the evidence on climate change on
the United States. To assess the effect of this potential
trend on our results, we fitted a linear trend starting in
1980 to each weather index and reestimated our models.
All of our results are nearly unaffected by this modification. This is not surprising since the effect of weather
is intuitively identified using the short-run deviations
of weather, which are much larger than the trend. Incorporating the trend has one significant consequence:
It makes the 2013–14 winter look even harsher; that
is, the weather deviation from normal is larger due to
the positive trend. This implies that our estimated effect
of that winter weather is larger, by about 20 percent,
than we discussed in the previous section.
Conclusion
Our results overall support the view that weather
has a significant, but short-lived, effect on economic
activity. Except for a few industries, which are affected
importantly (such as utilities, construction, hospitality,

17

		

TABLE 8

 
Estimated effect of 2013–14 winter using national model

		
Nonfarm employment 	
	
Unemployment rate 	
	
Retail sales (excluding cars) 	
	
Private average hours per worker 	
	
Industrial production (IP)	
	
IP: Manufacturing 	
	
IP: Utilities	
	
Lightweight vehicle sales 	
	
CFNAI 	
	
New orders on	
core capital goods	
	
Shipments of
core capital goods 	
	
Housing starts 	
	
Housing permits 	
	
Purchasing Managers Index 	
	

Nov. 	

Dec. 	

Jan. 	

Feb. 	

Mar. 	

Apr. 	

May

Data	
Weather effect 	
Data 	
Weather effect 	
Data 	
Weather effect 	
Data 	
Weather effect 	
Data 	
Weather effect 	
Data 	
Weather effect 	
Data 	
Weather effect 	
Data 	
Weather effect 	
Data 	
Weather effect 	

0.20	
0.01 	
– 0.20 	
0.16 	
– 0.38 	
– 0.02 	
0.30 	
0.09 	
0.59 	
0.12 	
0.31 	
0.04 	
1.85 	
1.39 	
5.79 	
1.11 	
0.71 	
0.06 	

0.06 	
0.02	
– 0.30 	
– 0.31 	
0.55 	
– 0.11 	
– 0.60 	
– 0.10 	
0.20 	
– 0.02 	
0.10 	
– 0.04 	
0.10 	
– 0.14 	
– 4.74 	
– 0.79 	
– 0.19 	
– 0.15 	

0.10 	
– 0.01 	
– 0.10 	
0.25 	
– 0.47 	
– 0.02 	
0.30 	
– 0.08 	
– 0.30 	
– 0.06 	
– 0.93 	
– 0.08 	
3.32 	
0.52 	
– 1.60 	
0.13 	
– 0.85 	
– 0.15 	

0.16 	
0.02 	
0.10 	
– 0.19 	
0.36 	
– 0.07 	
– 0.60 	
– 0.19 	
0.98 	
– 0.17 	
1.23 	
– 0.20 	
– 0.28 	
0.15 	
0.90 	
– 0.77 	
0.55 	
– 0.03 	

0.15 	
0.07 	
0.00 	
0.31 	
0.81 	
0.35 	
0.89 	
0.30 	
0.78 	
0.21 	
0.81 	
0.14 	
– 0.47 	
0.88 	
6.88 	
3.54 	
0.53 	
0.41 	

0.22 	
0.08 	
– 0.40	
– 0.57 	
0.72 	
0.09 	
0.00 	
0.03 	
0.10 	
0.03 	
0.30 	
0.10 	
– 5.30 	
– 2.05 	
– 2.81 	
0.05 	
0.15 	
0.15 	

0.17
– 0.02
0.00
0.29
0.25
0.02
0.00
– 0.04
0.48
– 0.05
0.40
0.01
0.20
– 0.65
4.29
0.00
0.18
– 0.14

Data 	
Weather effect	

5.72 	
1.03 	

– 0.88 	
– 1.15 	

– 1.90 	
– 1.05 	

0.10 	
– 2.18 	

4.58 	
2.21 	

– 1.10 	
0.60 	

– 1.41
– 0.01

Data 	
Weather effect 	
Data 	
Weather effect 	
Data 	
Weather effect 	
Data 	
Weather effect 	

2.81 	
0.73 	
16.60 	
0.01 	
– 2.85 	
– 0.05 	
57.00 	
– 0.12 	

0.38 	
– 0.23 	
– 6.64 	
–  0.72 	
– 1.46 	
0.51 	
56.50 	
0.20 	

– 1.91 	
– 0.97 	
– 14.21 	
– 2.98 	
– 8.47 	
– 2.87 	
51.30	
0.53 	

0.83 	
– 0.84 	
3.40 	
– 2.77 	
7.39 	
– 1.19 	
53.20 	
1.14 	

2.16 	
0.81 	
2.34 	
3.10 	
– 1.09 	
0.95 	
53.70 	
0.56 	

– 0.31 	
0.57 	
11.24 	
5.50 	
5.73 	
4.87 	
54.90 	
0.22 	

0.08
– 0.83
– 7.72
– 0.55
– 5.23
– 1.78
55.40
0.03

Notes: Based on national model with two lags. All results are in percentage growth rates, except the unemployment rate, which is the change
in percentage points.
Source: Authors’ calculations based on data from the National Climatic Data Center.

and, to a lesser extent, retail), the effect is not very large,
so that even the fairly bad weather during the 2013–14
winter cannot account entirely for the weak economy
during that period. Other factors must have been at play.
Indeed, the National Income and Product Accounts
data suggest that an important share of the slowdown
in the first quarter was driven by an inventory correction
and the effect of foreign trade. Another simple hint
that something more than the weather was at play is
that the timing of the decline, measured in economic
statistics in the period December through March, was
uneven across indicators: Some declined in December

18

and January, others in January and February, and so
on, which seems inconsistent with a simple weather
story. There are several directions in which it would
be interesting to extend this work. First, better weather
indexes could be constructed by weighting station
data using very local employment. The importance
of nonlinearities could also be studied in more detail,
as could the differences across states in sensitivities
to weather. Finally, local measurement of production
and sales would enable us to extend this study and
consider more outcomes.

1Q/2015, Economic Perspectives

NOTES
These indicators come from a variety of data sources, including
private or government surveys, trade associations, or administrative
data. These statistics are followed closely by investors because they
are released often and with little lag, and hence are more timely
and less subject to revisions than the broader and more comprehensive measures such as gross domestic product.
1

FOMC statements noted starting in December 2013 that “asset
purchases are not on a preset course, and the Committee’s decisions
about their pace will remain contingent on the Committee’s outlook
for the labor market and inflation as well as its assessment of the likely
efficacy and costs of such purchases.” See www.federalreserve.
gov/newsevents/press/monetary/20131218a.htm .
2

Recently, there has been some renewed interest by economists in
the question of how weather affects the economy, but this research
was not relevant for the issues at hand, as we explain.
3

temperature, are not important for the measurement of short-term
weather. We discuss this in more detail in the last section.
We calculate the daily temperature as the simple average
of the minimum and maximum daily temperature, that is,
T + Tmin
T = max
.
2
13

The underlying issue is whether normalizing helps capture the effect of unusual weather on economic activity. We hypothesize that
economies in highly variable climates have adapted: For example,
states with highly variable levels of snowfall may have the infrastructure in trucks and salt to deal with large snowfall events. This
is largely an empirical question. In some explorations, we found
that the precise normalization was not critical to our result, but this
is an area that deserves future research.
14

The log change approximates the percentage change in the variable
Y, while reducing the effects of outliers and heteroskedasticity.
15

The minutes from the March 2014 meeting provided more detail:
“The information reviewed for the March 18–19 meeting indicated
that economic growth slowed early this year, likely only in part because of the temporary effects of the unusually cold and snowy winter
weather. ... The staff’s assessment was that the unusually severe
winter weather could account for some, but not all, of the recent
unanticipated weakness in economic activity, and the staff lowered
its projection for near-term output growth. ... Most participants noted
that unusually severe winter weather had held down economic activity during the early months of the year. Business contacts in various
parts of the country reported a number of weather-induced disruptions, including reduced manufacturing activity due to lost workdays, interruptions to supply chains of inputs and delivery of final
products, and lower-than-expected retail sales. Participants expected
economic activity to pick up as the weather-related disruptions to
spending and production dissipated.” See www.federalreserve.gov/
monetarypolicy/fomcminutes20140319.htm.
4

See www.federalreserve.gov/mediacenter/files/
FOMCpresconf20140319.pdf .
5

The minutes noted that “the information reviewed for the April
29–30 meeting indicated that growth in economic activity paused
in the first quarter as a whole, but that activity stepped up late in
the quarter; this pattern reflected, in part, the temporary effects
of the unusually cold and snowy weather earlier in the quarter
and the unwinding of those effects later in the quarter.” See www.
federalreserve.gov/monetarypolicy/fomcminutes20140430.htm.
6

See www.federalreserve.gov/newsevents/speech/yellen20140416a.htm.

7

Published on the New York Times website September 26, 2014;
available at www.nytimes.com/2014/09/27/upshot/gdp-reportemphasizes-the-problem-of-conflicting-economic-signals.html.
8

See, for instance, Gallup, Sachs, and Mellinger (1999).

9

The data are available at ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/
daily. Our data set is version 3.12, retrieved in September 2014.
10

One important data issue is that until recently, snowfall was often
not reported unless it was snowing; that is, the data are reported as
missing rather than zero. As we believe is standard practice, we attribute a zero snowfall to all missing observations (which may include
some observations for which no data were actually observed).
11

However, it may be that economic activity in state i depends on
weather in other states, for example, because of supply chains or
because lower retail sales in one state affect production in another
state. Because weather may be correlated across states, this could
lead to a bias.
16

The error terms εi, m, y may be correlated across states and over
time; we adjust the standard errors to take this into account using
two-way clustering.
17

Technically, this equation requires defining Ti, m–k,  y = Ti, m–k+12,  y–1
if m – k ≤ 0.
18

We are not aware of monthly data available on sales or production
at the state level.
19

The breakdown of employment by industry at the state level and
at the monthly frequency is only available for the period 1990–2014,
so we have fewer data and consequently fewer statistically significant results. The sample size varies further by industry because the
Bureau of Labor Statistics’ establishment survey does not report
employment for some industries in some states.
20

This is expected since our indexes of temperature and snowfall
exhibit relatively little serial correlation; hence, adding lagged values
to the regression does not affect the same-month impact estimates
since current weather and lagged weather are roughly orthogonal.
21

The only marginal case is the effect of temperature on housing
permits, which is significant at the 7 percent level.
22

Note, however, that the precision of the estimates does not permit
us to rule out a small long-run effect.
23

Technically, we interact both of our weather indexes with two
dummies, pre- and post-1990, and run a single regression for each
economic indicator.
24

We construct the national implied weather effects from the state
model by weighting the state-level predictions to adjust for the
state size.
25

There appears to be no trend in precipitation, even in “adjusted” data.

26

As a result of the lack of adjustments, our data do not exhibit
very clear increases in average temperature. We believe the adjustments, while critical for the measurement of the trend in average
12

Federal Reserve Bank of Chicago

19

REFERENCES

Boldin, Michael, and Jonathan H. Wright, 2015,
“Weather-adjusting employment data,” Federal Reserve
Bank of Philadelphia, working paper, No. 15-05,
January.
Colacito, Riccardo, Bridget Hoffman, and Toan
Phan, 2014, “Temperatures and growth: A panel
analysis of the U.S.,” University of North Carolina
at Chapel Hill, working paper, December 18.
Dell, Melissa, Benjamin F. Jones, and Benjamin A.
Olken, 2014, “What do we learn from the weather?
The new climate-economy literature,” Journal of
Economic Literature, Vol. 52, No. 3, September,
pp. 740–798.
__________, 2012, “Temperature shocks and economic
growth: Evidence from the last half century,” American
Economic Journal: Macroeconomics, Vol. 4, No. 3,
July, pp. 66–95.

Deschênes, Olivier, and Michael Greenstone, 2007,
“The economic impacts of climate change: Evidence
from agricultural output and random fluctuations in
weather,” American Economic Review,
Vol. 97, No. 1, March, pp. 354–385.
Gallup, John Luke, Jeffrey D. Sachs, and Andrew
D. Mellinger, 1999, “Geography and economic
development,” International Regional Science
Review, Vol. 22, No. 2, August, pp. 179–232.
Macroeconomic Advisers, 2014, “Elevated snowfall
reduced Q1 GDP growth 1.4 percentage points,”
April 15, available at www.macroadvisers.com/2014/
04/elevated-snowfall-reduced-q1-gdp-growth-1-4percentage-points/.

Deryugina, Tatyana, and Solomon M. Hsiang,
2014, “Does the environment still matter? Daily
temperature and income in the United States,”
National Bureau of Economic Research, working
paper, No. 20750, December.

20

1Q/2015, Economic Perspectives

Derivatives and collateral at U.S. life insurers
Kyal Berends and Thomas B. King

Introduction and summary
Insurance companies serve the important economic
role of helping businesses and households to insulate
themselves against risks. But these risks do not disappear from the economy—they remain on insurers’
books, necessitating careful risk management among
insurers themselves. Over the past two decades, one
way that insurers have managed risk is through the
use of derivative contracts,1 which derive their value
from the performance of an underlying entity. This
underlying entity can be an asset, index, or interest
rate. Some of the more common derivatives include
forwards, futures, options, and swaps. Most derivatives, including interest rate swaps (IRS), have historically been traded over the counter (OTC) rather than
on centralized exchanges.
The use of derivatives comes with its own set of
costs related to the transaction, management, and collateralization of positions. With the implementation of the
Dodd–Frank Act of 2010, those costs seem certain to
rise. Among other provisions, the law requires the central clearing of certain types of OTC derivatives and
mandates that those transactions must satisfy margin requirements that will in most cases require counterparties
to post more collateral than was previously the case.2
Forthcoming rules will impose additional collateral requirements on derivatives positions for which the central clearing mandate does not apply. Thus, the new
rules for both cleared and noncleared derivatives could
generate new costs for insurers or require changes in
their business practices.
In this article, we review life insurers’ use of OTC
derivatives and discuss the possible implications of
these new rules for their financial condition.3 Although
insurers represent a relatively small part of the derivatives markets, they are an interesting case study, in part
because they report very detailed information about
their derivatives positions and associated collateral in

Federal Reserve Bank of Chicago

quarterly regulatory filings. We exploit these data to
study how derivatives are used by insurers and to get a
quantitative sense of what the new regulations are likely
to imply for their business models.
The new regime poses several potential costs for
insurers. For example, like many market participants,
insurers will face a short-term fixed cost of adjusting
operations and corporate structure to meet the new
clearing and collateralization requirements, as well as
ongoing expenses associated with trading, collateral
optimization, and back-office functions; and insurers
may also face some regulatory capital consequences.
Kyal Berends is a former senior associate economist and Thomas
King is a senior financial economist in the Economic Research
Department at the Federal Reserve Bank of Chicago. The authors
thank Anna Paulson, Rich Rosen, and other members of the Chicago
Insurance Initiative for helpful feedback.
© 2015 Federal Reserve Bank of Chicago
Economic Perspectives is published by the Economic Research
Department of the Federal Reserve Bank of Chicago. The views
expressed are the authors’ and do not necessarily reflect the views
of the Federal Reserve Bank of Chicago or the Federal Reserve
System.
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President and Associate Director of Research; Spencer Krane,
Senior Vice President and Senior Research Advisor; Daniel Aaronson,
Vice President, microeconomic policy research; Jonas D. M.
Fisher, Vice President, macroeconomic policy research; Anna L.
Paulson, Vice President, finance team; William A. Testa, Vice
President, regional programs; Lisa Barrow, Senior Economist and
Economics Editor; Helen Koshy and Han Y. Choi, Editors; Julia
Baker, Production Editor; Sheila A. Mangler, Editorial Assistant.
Economic Perspectives articles may be reproduced in whole or in
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of Economic Perspectives articles. To request permission, please
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ISSN 0164-0682

21

TABLE 1

Life insurers with the largest
OTC derivatives portfolios
	
	

Notional OTC	
derivatives	

Statutory
assets

	( - - - - dollars in billions - - - - )
MetLife Inc. 	
188 	
603
Manulife Financial Corp. 	
151 	
267
Massachusetts Mutual
Life Insurance Co. 	
137 	
202
New York Life Insurance Group 	 104 	
261
Nationwide Mutual Group 	
72 	
132
Voya Financial Inc. 	
71 	
193
Ameriprise Financial Inc. 	
65 	
110
AEGON 	
57 	
202
Lincoln National Corp. 	
52 	
222
Prudential Financial Inc. 	
44 	
545
Jackson National Life Group 	
29 	
186
Principal Financial Group Inc. 	
24 	
149
Allianz Group 	
21 	
116
Genworth Financial Inc. 	
19 	
70
AXA 	
16 	
166
Hartford Financial Services 	
12 	
179
American International Group 	
11 	
269
Aflac Inc. 	
7	
111
Delaware Life Partners LLC 	
6	
42
Sun Life Financial Inc. 	
5	
19
Notes: OTC indicates over the counter. Includes interest rate swaps,
caps, floors, collars, and swaptions; credit default swaps; total
return swaps; and inflation-linked products. Data as of 2014:Q3.
Source: Statutory filings via SNL Financial.

In this article, however, we focus on one particular
set of costs that has received attention, namely, costs
related to reallocating insurers’ portfolios to highquality—and therefore low-yielding—assets in order
to meet margin requirements.4 We find that, overall,
the requirements are unlikely to generate large costs
for the industry as a whole through this channel—
although there are some low-probability tail scenarios
in which they could result in substantial forgone investment income for a few larger insurers. This finding is
largely due to the fact that insurance companies already
hold large amounts of high-quality unencumbered
securities that could be pledged for this purpose, and
indeed they may be natural collateral providers to
other market participants.5
After reviewing insurers’ use of derivatives and
collateral in the following section, we develop a Monte
Carlo exercise to attempt to quantify the amount of
margin posted and revenue lost due to required margin
under different scenarios for interest rates and insurer
portfolio evolution. Then, we consider some ways that
insurers may adjust their business practices in light of
the new regulations. Two likely responses are to reduce
the need for hedging by shifting more interest rate risk
onto consumers or markets and to build up new sources

22

of liquidity to cover cash needs. Depending on how
these adjustments play out, they could expose insurers
and their counterparties to new risks, especially in a
crisis environment in which liquidity is constrained.
Life insurers’ use of OTC derivatives
Insurers use a variety of types of derivatives for
hedging different types of risks. Some of these derivatives,
such as equity options and currency swaps, are typically
exchange traded and are not affected by the Dodd–Frank
rules. In this article, we focus on the interest rate and
credit derivatives that are traded OTC, because those
are the contracts to which the central clearing and
collateralization requirements apply. Table 1 lists the
20 life insurance companies that participate most in
the OTC derivatives market, as measured by the gross
notional value of their positions in these instruments.6
These companies include the largest insurers by assets,
but derivatives usage is not perfectly correlated with
firm size—it depends on a variety of factors, including
lines of business and corporate structure. For example,
some very large insurers, including TIAA-CREF and
Northwestern Mutual, have OTC derivatives positions
that are too small to be included in the table.
As a whole, the life insurance industry held
$1.1 trillion of notional value in OTC derivatives as
of September 2014. For a sense of scale, we note the
statutory assets of these companies totaled $6.1 trillion.
Relative to other market participants, such as commercial banks, the OTC derivatives portfolios of life
insurers are relatively modest. The gross market value
of their swaps positions was only about $13.2 billion,
and the net positions are likely smaller still.7 However,
derivatives portfolios are highly concentrated—over
50 percent of notional value of OTC derivatives in the
industry is held by the four insurers with the largest
swaps portfolios (MetLife, Manulife, Mass Mutual, and
New York Life). The companies in the table collectively
hold 97 percent of the industry’s OTC derivatives.
Large insurance operating companies often reside
within even larger, complex corporate structures. Thus,
derivatives positions at the operating company may not
give a complete picture of the derivatives activity at
the whole firm. For companies that are publicly traded
in the United States, it is possible to obtain some information on consolidated derivatives positions from SEC
filings, although this information is not as detailed as
what is available from regulatory reports. Table 2 shows
the sum of interest rate and credit derivative positions
for the largest companies for which such information
is available. For these eight firms (which together hold
about half of the positions listed in table 1), derivative
exposures that are in subsidiaries other than life

1Q/2015, Economic Perspectives

TABLE 2

Selected operating company versus
consolidated derivatives positions
	

Operating company	

Consolidated

	
( - - - - - - - - - dollars in millions - - - - - - - - - )
Prudential	50,179	
316,283
MetLife	189,881	 267,155
Manulife	169,550	
209,486
AIG	 17,685	90,446
Voya	69,773	 73,614
Lincoln	60,009	 56,864
Hartford	11,645	 30,715
Principal	24,063	
25,426
Notes: Data as of 2014:Q3. Includes all interest rate and credit
derivatives. Operating company amounts may not match those
in table 1 due to imperfect overlap between these categories and
OTC derivatives.
Sources: Statutory filings and 10K reports via SNL Financial.

insurance operating companies constitute 55 percent
of the holding companies’ notional positions.
As shown in table 3, insurers’ interest rate swaps
positions at the industry level are roughly equally
balanced between paying and receiving fixed rates.8
This pattern also holds at the individual insurer level:
A typical firm both receives and pays fixed rates. However, as we discuss later, the simultaneous positions
in opposite directions reflect the hedging of different
types of risk and, consequently, typically differ by
maturity. Insurers also hold fairly large positions—
about $327 billion in notional value—in other types
of interest rate derivatives, especially caps and call
swaptions, which hedge against rising rates. They
also hold small amounts of total return and credit
default swaps, which are used for asset replication
purposes as well as hedging, and a smattering of
miscellaneous products.
To understand the potential impact of the new
collateral rules on the insurance industry, it is useful
to review how these derivative positions function
within insurers’ business models. Insurers take very few
directional positions using derivatives, relying on them
almost entirely for hedging purposes. In particular,
they hedge four broad types of risk.9 First, they hedge
the interest rate risk of their fixed-income portfolios.
As of September 2014, the insurers in table 1 collectively held nearly $1 trillion in various types of bonds,
exposing them to rising interest rates.10 They use payfixed interest rate swaps and other interest rate derivatives to hedge against this risk. Statutory data on hedging
purpose (not shown) indicate that about half of OTC
derivatives positions serve this function.
Second, insurers hedge the risks of deposit-like
liabilities, including funding agreements and guaranteed interest contracts (GICs). These may pay fixed

Federal Reserve Bank of Chicago

or floating rates and span a spectrum of maturities,
although they are typically much shorter than insurers’ other liabilities. These contracts may also have
option-like features that require more complex hedging strategies. Some of these strategies may involve
relatively exotic derivatives for which central clearing is not available.
Third, insurers attempt to match the duration of
their long-term insurance and annuity liabilities. For
the simplest contracts, hedging these exposures involves receiving interest payments to match the payments that the firm is required to make. But, for most
insurance and annuity products, cash flows are uncertain. Thus, unlike the security-specific hedging on the
asset side, liability hedging can only be done imperfectly in an economic sense, since there is significant
uncertainty about the timing and duration of future
insurance claims. As shown in table 3, insurers are,
on net, receivers of fixed payments in swaps, implying that on balance they are using swaps to add duration to their portfolios. This makes sense as many life
insurance liabilities are very long duration—indeed,
in some cases longer than can be achieved by buying
fixed-income products in the cash market.
Finally, insurers hedge the optionality of their liabilities. This optionality can take a variety of implicit
and explicit forms. For example, it is common for insurance companies to offer minimum-return guarantees
on variable annuities, which they in turn hedge with a
combination of OTC and exchange-traded derivatives.
Furthermore, most annuities may be surrendered at
the option of the beneficiary. Fixed-rate annuities are
more likely to be surrendered when interest rates rise,
precisely when they are most attractive from the issuer’s point of view. Most of the caps, floors, and swaptions reported in the table are also used to hedge these
types of risk, and interest rate swaps may be used as
part of the strategy. Many of the nonoperating-company
positions shown in table 2 are likely held by captive
reinsurers, which also principally use them for this
type of hedging.
It is important to recognize that derivatives portfolios reflect a mix of risk mitigation, accounting, and
regulatory considerations. In particular, under FAS 133,
insurers can receive hedge accounting treatment for
derivatives positions that are deemed “effective hedges,”
and a similar treatment applies in statutory accounting.
For example, insurers discount the value of future
claims on insurance policies using an assumed maturity structure and discount rate, and they can receive
hedge accounting treatment by entering into (usually
long-dated) swaps that match these terms. Although
long-term bonds might be able to match the duration

23

TABLE 3

Characteristics of insurer OTC derivatives portfolios
	
		
		

Maturity (% of notional)
Notional amount	
(millions of dollars)	

Interest rate swaps 	
	Receive-fixed	
	Pay-fixed	
	 Type not reported	
Other rate products	
	 Floors and puts	
	 Caps and calls	
	Other	
Credit default swaps	
	 Bought protection	
	 Sold protection	
	 Type not reported	
Miscellaneous	

Fair	
value	

< 1	
year	

1–3	
years	

3–7 	
years 	

7–15	
years	

>15
years

705,229	
11,121	
6	
15	
19	
27	
33
346,373	
19,411	
3	
12	
17	
29	
39
296,358	
–9,570	
9	
20	
22	
23	
25
62,498	
1,279	
7	
10	
16	
35	
32
326,961	
1,696	
27	
30	
27	
12	
3
87,675	
796	
53	
30	
3	
13	
1
184,603	
716	
16	
33	
39	
11	
1
54,683	
184	
26	
20	
26	
12	
15
25,896	
209	
17	
21	
56	
3	
3
3,638	
–33	
33	
27	
31	
2	
7
16,871	
183	
19	
19	
55	
4	
2
5,386	
59	
0	
24	
73	
2	
0
32,957	 –28	 73	 4	3	 7	13

Notes: Includes data as of 2014:Q3 from the 20 life insurance operating companies with the largest OTC derivatives portfolios, as measured by
notional value. Other rate products include interest rate collars and swaptions classified as “other.” Miscellaneous includes total return swaps and
inflation swaps.
Source: Statutory filings via SNL Financial.

of those same positions reasonably well and thus hedge
them in an economic sense, such a strategy would not
qualify for hedge accounting treatment. Insurers may
have incentives to engage in offsetting swaps contracts
to hedge both sides of the balance sheet to recognize
accounting benefits.
One should also bear in mind that insurers’ derivatives use takes place against a backdrop of regulatory
controls. Some states require insurers to maintain a
strict “derivatives use plan” that must meet with the
approval of supervisors, and they also set limits on the
quantity of derivatives activity. For example, New York
prohibits swaps holdings with potential exposure in
excess of 3 percent of admitted assets. (“Potential exposure” is a regulatory measure of the total amount of
risk posed by an insurer’s derivatives book.) Insurers
must therefore choose carefully which risks to hedge
and how best to use their limited derivatives capacity.
Margin requirements and Dodd–Frank
Because participants in derivatives contracts have
risk exposures to their counterparties, they are typically
required to post some form of collateral to each other.
The Dodd–Frank Act standardizes these requirements
for OTC derivatives transactions. Collateral requirements associated with derivatives trades are of two
types. Variation margin captures the marked-to-market
change in the value of positions on a daily or, in exceptionally volatile periods, intraday basis. This is meant
to ensure that in the event of a default by one counterparty, the other counterparty can recover the fair value

24

of the position. Initial margin is intended to cover
possible losses incurred by the remaining counterparty
after default, as it goes about liquidating or replacing
the defaulted position. Thus, initial margin is typically
calculated by assuming a certain amount of time for
liquidation and using the data to estimate a worst-case
scenario for the price moves of the position.
Even prior to the Dodd–Frank rules, it was standard for OTC derivatives counterparties to post some
form of variation margin, and the exchange of initial
margin was also common.11 However, derivatives
counterparties typically had a fair amount of leeway
in how these requirements were satisfied. For example, they may have been able to post a variety of collateral types as margin or, depending on their bilateral
agreements, post margin only when the change in the
fair value of the position exceeded some threshold.
Figure 1, panel A, shows margin posted by insurance
companies in support of derivatives since 2013:Q1,
when these data were first collected.12 Figure 1, panel
B, shows the collateral breakdown as of 2014:Q3.
Note that, although variation margin constitutes the
bulk of insurers’ collateral positions, very little of this
collateral consists of cash. This reflects the fact that
most derivatives on insurers’ books, if they require
collateral at all, allow variation margin to be posted
in the form of a range of securities.
Since June 10, 2013, new plain vanilla IRS and
CDS index positions covered under Dodd–Frank have
had to be cleared by a central counterparty (CCP) and
collateralized accordingly. In particular, CCPs must

1Q/2015, Economic Perspectives

FIGURE 1

Fair value of collateral pledged by life insurance companies
A. Collateral posted over time
millions of dollars
16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

Mar 2013

Jun ’13

Sep ’13

Not specified

Dec ’13

Mar ’14

Jun ’14

Sep ’14

Initial margin

Variation margin

B. Securities posted as margin

Other
6%

Corporate
16%
Treasury
43%
Agency and
agency MBS
22%

Cash
13%

Notes: Data for 20 largest OTC derivatives users. Panel B as of September 2013. MBS indicates mortgage-backed securities.
Source: Statutory filings via SNL Financial.

Federal Reserve Bank of Chicago

25

require counterparties to post initial margin sufficient
to cover a hypothetical five-day liquidation period
with at least a 99 percent level of confidence and
variation margin to cover daily fluctuations in the
market value of positions. Forthcoming rules on uncleared trades are likely to impose a similar requirement for variation margin and a more stringent
ten-day liquidation period for initial margin.
As shown in figure 1, margin posted by insurance companies to cover derivatives positions has indeed risen notably since the first quarter of 2013. In
the 18 months surrounding the implementation date,
insurers increased the collateral posted with derivatives counterparties by 45 percent, from $7.2 billion
to $10.4 billion. Although both initial margin and
variation margin have increased significantly, variation margin has fluctuated more. This is because variation margin is heavily influenced by external factors,
such as interest rates. This volatility is suggestive of
one type of risk that insurers now face—large movements in interest rates can require the transfer of large
quantities of securities and, especially, cash into margin accounts. The following section discusses the
scope of this risk in greater detail.
The types of collateral that can be posted to cover
margin for cleared contracts, and the haircuts that
apply, vary across CCPs. Initial margin is most often
satisfied by high-quality securities, such as U.S.
Treasury securities, although at least one major CCP
has begun accepting investment-grade corporate bonds
(within certain limits and subject to steep haircuts).
In contrast, variation margin must be covered by cash.
Moreover, the time frame within which clearing
members must post variation margin after receiving
a margin call is typically very short, often a matter
of hours. (For uncleared trades, proposed rules would
require most insurance companies—as “low-risk
end-users”—only to update variation margin once
per week and when the values involved rise above
some de minimus amount.)
The burden of initial margin requirements is
reduced to a degree by the possibility of netting potential moves in negatively correlated positions against
each other. For example, if an insurer engages in a
receive-fixed swap and a pay-fixed swap on similar
terms with the same counterparty, that counterparty
should expect price movements in the two contracts
to offset exactly. Consequently, the margin needed to
cover the position as a whole should be minimal, even
though the margin needed to cover each swap individually might not be. For cleared trades, the extent
to which such gains are available depends on the CCP’s
rules and models. For uncleared trades, the potential

26

for margin offsets depends on the extent and terms of
master netting agreements. In both cases, it also depends
on the degree to which positions are concentrated at
particular counterparties, since it is generally not
possible to recognize portfolio-margining benefits
from offsetting positions at different counterparties.13
The potential costs of the new collateral and
clearing requirements span a variety of operational
and economic considerations and are discussed more
fully in a later section. It is clear, however, that the
incidence of these costs—and, therefore, the nature
of the industry’s response—will depend greatly on
the quantity of collateral that insurers end up having
to post. We turn to this question next.
Collateral needs under alternative scenarios
In this section, we attempt to quantify the amount
of collateral that may be necessary for life insurers to
provide in support of cleared swaps positions in coming
years. The results are essentially the product of three
inputs: 1) a distribution for the possible path of interest
rates; 2) calculations of how the value of each derivative contract type responds to the various interest rate
configurations; and 3) an assumption regarding how
insurers’ derivative positions will evolve over time.
Given institutional shifts in the industry and limited
historical data, the last item is the most difficult of the
three to ascertain. Therefore, we consider two different
scenarios for the changes in the industry’s derivatives
mix that likely bracket the possibilities.
We summarize the methodology briefly here and
describe it in detail in the appendix.
Model setup
For each of the 20 firms with the largest OTC
derivatives holdings, as measured by notional value,
we break down the interest rate swaps portfolio based
on derivative type (pay- versus receive-fixed), maturity,
and time since the contract was originated. We take
the granularity of maturity buckets and contract ages
to be annual, and we assume that the maximum
maturity is 30 years.14 For each type, we approximate
each firm’s notional holdings using a beta distribution
over maturities, based on the 2014:Q3 data that were
summarized at an aggregate level in table 1 (p. 22).
Our assumptions about how this distribution evolves
generate flows of derivatives originations and terminations in each year in our simulations for each firm
in each type/maturity bin. Knowing the flows allows
us to back out the distribution of contract ages for
each swap bin. Since swaps valuation depends on
the contract’s remaining maturity and the fixed rate
that applies to it, we can track the distribution of

1Q/2015, Economic Perspectives

TABLE 4

Five-day, 1 percentile fair-value changes for
interest rate swaps of various terms
	
Fixed rate (%)	
	
	
	
	
	

2	
4	
6	
8	
10	

1	
– 0.2	
– 0.2	
– 0.2	
– 0.2	
– 0.2	

Remaining maturity (years)
3	
7	
15	
30
– 0.9	
– 0.9	
– 0.9	
– 0.9	
– 0.9	

–2.0	
–2.2	
–2.2	
–2.5	
–2.5	

–3.2	
–3.6	
– 4.1	
– 4.8	
– 4.9	

–3.9
–5.1
–5.9
–7.6
–8.7

Notes: Considers rate changes from the average 2014:Q4 level of
interest rates. Distribution of rate changes is based on daily data,
April 1, 2004–September 30, 2014.
Source: Authors’ calculations based on interest rate data provided
by the Board of Governors of the Federal Reserve System.

swap rates within each bin, given a path of historical
interest rates.
We assume that all swaps held by insurers are
“plain vanilla” interest rate swaps (meaning standard
contracts that exchange fixed and floating payments
based on commonly used benchmarks and schedules).
This assumption allows us to calculate the net present
values of these contracts analytically, given an interest rate path. Furthermore, this assumption implies
that the collateral requirements associated with central clearing apply to all of those contracts that are
originated going forward.15 We do not consider contracts originated prior to 2013:Q2 because, although
many such contracts do involve margin agreements
between the counterparties, the Dodd–Frank rules only
require insurance companies to clear and post margin
on plain vanilla swaps originated after June 2013.
Dodd–Frank mandates initial margin sufficient to
cover a five-day liquidation period on cleared trades.
To give a sense of the magnitudes involved, we calculate the range of initial margin values that could apply
to swaps of various maturities and rates. Specifically,
we calculate the distribution of five-day changes in
value by drawing random five-day yield curve changes
from the last ten years of data, and we apply these
changes to rates that start at their 2014:Q1 level.
Table 4 shows the resulting 99.7 percent quantiles,
corresponding roughly to the levels of confidence used
by CCPs.16 In the absence of netting, the total initial
margin required on a particular portfolio in the current
interest rate environment would simply be given by
the margin rates listed in the table, weighted by the
amount of the portfolio in each corresponding bin.
However, when calculating initial margin, CCPs and
other counterparties generally allow for possible negative correlations between value changes for different
derivatives positions in the same portfolio. This implies
that one needs to evaluate the distribution of outcomes

Federal Reserve Bank of Chicago

at the portfolio level. Our simulations of initial margin
do this for each firm, at each date, for each simulated
path of interest rates, based on our projections for
how the distribution of swaps to which Dodd–Frank
applies evolves over time.
Calculating variation margin, given a path of interest
rates, is somewhat easier, since variation margin is simply
equal to the net fair value of the swaps positions. Thus,
for each firm, at each date, for each simulated path of
rates, we calculate the net present value of swaps of each
age in each type/maturity bin. Total variation margin
is the sum of these values across swaps originated after
2013:Q2, weighted by the respective portfolio shares.
Interest rate simulations
We begin our computations in 2013:Q2. For the
calculations through 2014:Q3, we use actual data on
the yield curve to price the swaps portfolios. For projections beyond that date, we estimate a vector autoregression on Treasury forward rates, the Moody’s Baa
corporate bond yield, gross domestic product (GDP),
and PCE inflation (based on the Personal Consumption
Expenditures Price Index). We then take 10,000 draws
from the estimated residual distribution and simulate
forward ten years beginning with 2014 data. Any
time a simulation results in a nonpositive rate in any
quarter, we discard it and draw again. Figure 2 shows
the distribution of simulated rate paths.
Portfolio scenarios
The amount of margin that will need to be posted
against derivatives positions will depend crucially on
how insurers adjust their derivatives portfolios going
forward. The most natural assumption about this behavior may be simply that they keep the distribution
across contract types and maturities unchanged at its
current level, and this is indeed the first scenario that
we consider. However, market participants generally
anticipate that the net duration of the portfolio will
lengthen going forward. This is also the situation in
which margin is potentially greatest in the risinginterest-rate environment that we consider, and so it
is worth modeling from a stress-testing point of view.
Our second scenario is a variant of this outcome, in
which insurers take new long positions by passively
rolling over their maturing derivatives.
Specifically, in our “constant maturity distribution” scenario, we assume that the distribution of the
stock of derivatives (that is, the percentage of the total
in each type/maturity bin) is static. This means that
the flows—that is, the amount of contracts originated
or extinguished in each quarter—must generally be
nonzero. We assume that the gross flows (the amount
of notional value originated and canceled) are the

27

FIGURE 2

Distribution of simulated interest rate paths
A. Instantaneous

B. Ten-year

10

12

8

10
8

6

6
4

4

2
0
1985

2

’90

’95 2000

’05

’10

’15

’20

’25

0
1985

’90

’95

C. 30-year

D. Corporate Baa

12

12

10

10

8

8

6

6

4

4

2

2

0
1985

’90

’95

2000

’05

’10

’15

’20

’25

0
1985

’90

’95

2000

’05

’10

’15

’20

’25

2000

’05

’10

’15

’20

’25

Notes: The figures show selected interest rate forecasts from the VAR model, with the zero lower bound imposed, based on 10,000
Monte Carlo draws. Solid black lines are means; shaded regions are 10–90% and 1–99% confidence bounds.
Sources: Board of Governors of the Federal Reserve System and authors’ calculations.

minimum possible to achieve the net flow that keeps
the stock distribution unchanged. In our “duration
extension” scenario, we assume that insurers do not
terminate any swaps going forward, but all contracts
(either long or short) that mature are rolled into new
30-year receive-fixed swaps. Given the initial maturity
distribution of swaps, this implies that by the end of
the projection period, about 40 percent of the payfixed and 60 percent of the receive-fixed portfolio
have rolled into new long swaps positions that are
subject to Dodd–Frank.
Importantly, both scenarios assume that the
overall size of insurers’ derivatives portfolios stays
constant. This assumption is simply for ease of comparison to current balance-sheet values. If, as seems
nearly certain, the notional value of swaps positions
continues to increase over time, the dollar values of
posted margin—and the corresponding costs—will be
proportionally higher.

28

Estimated margin
For each firm, we calculate the margin that would
be required in each year under each scenario, given
the distribution of interest rate paths shown in figure 2.
As shown in figure 3, initial margin is forecast to rise
steadily over the projection period in both scenarios.
The smoothness and relative precision of the projected
paths of initial margin reflect the fact that initial margin is largely driven by portfolio turnover, which is
(by assumption) independent of the interest rate environment. However, the size of the increase depends
crucially on the extent of the portfolio lengthening. In
the constant-maturity case, it climbs about $2 billion
between 2013:Q2 and 2014:Q3, reflecting portfolio
changes that we have already observed, but then stays
approximately constant for the remainder of the
projection period. This outcome reflects the strong
negative correlation between changes in receive- and
pay-fixed values, which insulates the value of the overall

1Q/2015, Economic Perspectives

FIGURE 3

FIGURE 4

Required initial margin under
alternative portfolio scenarios

Required variation margin under
alternative portfolio scenarios

$billions
10

$billions
80
60

8

40

6

20

4

0
−20

2
0

−40

2014

’16

’18

’20

’22

’24

Notes: The figure shows the range of initial margin posted by
the top 20 OTC derivatives users under the static-portfolio
(blue) and duration-extension (red) scenarios. Solid lines
represent means; shaded regions are 10–90% confidence
bounds, based on the distribution of forecasted interest rates
depicted in figure 2.
Source: Authors’ calculations.

portfolio from interest rate shocks. In the durationextension case, in which this offset gradually disappears, the required amount of initial margin climbs to
about $8 billion by 2024.17
Under the constant-maturity scenario, the mean level
of variation margin peaks at a level of about $4 billion
after eight years, as shown in figure 4. Under the
duration-extension scenario, this amount is considerably larger, at about $18 billion. Furthermore, the amount
is very sensitive to the path of interest rates, with the
90 percent confidence interval in the duration-extension
scenarios spanning a range of nearly $100 billion. Thus,
the amount of variation margin that will be required
from the industry in coming years is quite uncertain.
Potential costs of margin requirements
Initial margin
Table 5 reports sample firms’ securities holdings
that could, in principle, be used to meet initial margin
requirements. In practice, two reasons that these total
amounts of securities may not be able to be used for
margin are that they are already pledged for some other
purpose or that the CCP imposes a limit on how much
may be used. As shown in column 2, encumbered
assets generally represent a small portion of insurers’
overall securities portfolios. To address the question
of collateral limits imposed by CCPs, we apply the
margining rules for cleared swaps adopted by the CME
(Chicago Mercantile Exchange).18 In particular, we
assume that for margining purposes, each type of

Federal Reserve Bank of Chicago

2014

’16

’18

’20

’22

’24

Notes: The figure shows the range of variation margin posted
by the top 20 OTC derivatives users under the static-portfolio
(blue) and duration-extension (red) scenarios. Solid lines
represent means; shaded regions are 10–90% confidence
bounds, based on the distribution of forecasted interest rates
depicted in figure 2.
Source: Authors’ calculations.

security is discounted by the amount shown in column 3, reflecting a typical haircut applied to that asset
class by the CME. Furthermore, we apply the CME’s
rule that the sum of agency debt and agency mortgagebacked securities (MBS) used as collateral cannot exceed
40 percent of total collateral for any given customer
and that corporate and foreign sovereign bonds cannot
exceed the lesser of 40 percent of total collateral or
$5 billion. The portfolio limits at the CME apply at
the level of the futures commissions merchant (FCM),
not the client, so an insurer effectively competes with
the other clients of an FCM when trying to post corporate
bonds. However, large insurers also have accounts at
multiple FCMs; thus, it is not clear whether the effective
limits on insurers should be considered to be greater
or less than the limits imposed by the CCP. The table
therefore considers both a case in which the CME
rules are passed through one-for-one to insurers and a
more conservative calculation in which insurers are
not able to post any securities at all other than cash and
Treasury securities. The results of these calculations,
reflecting the approximate amount available for initial
margin, are shown in columns 5 and 6, with the actual
amount of margin (both initial and variation) currently
posted shown for comparison in the final column. As
the available securities exceed those being used by a
factor of 6, even under the conservative assumptions,
there is clearly a significant amount of spare capacity
at present.
Furthermore, the amount of securities available to
pledge is large compared with the amount of collateral

29

TABLE 5

Estimated collateral available for initial margin at top 20 swaps users
	Potential
	
collateralized positions
		
		
		
Cash and equivalents	
Treasury securities	
Agencies	
Agency MBS	
Foreign government	
Public corporates	
Total	

					
Cash and	
Margin
Fair value of 	
Less: 	
= Available	 Assumed	
CME-like 	
Treasury 	
currently
securities 	 encumbered	 collateral	 haircuts (%)	
limitsa	 securities	pledged
16 	
84 	
25 	
85 	
63 	
700 	
972 	

0	
33 	
4	
22 	
28 	
117 	
204 	

16 	
2.5	
50 	
4.5	
22 	
6.0	
63 	
11.0	
35 	
8.5	
583 	
20.0	
768 		

15 	
48 	
20	b 	
56	b 	
32	c 	
61	c 	
232 	

15 	
48 	
–	
–	
–	
–	
63 	

1
5
0
2
0
2
10

Uses portfolio limits for each insurer on each asset class based on those currently imposed on clearing members by the CME.
Sum of agency debt and agency mortgage-backed securities (MBS) must be less than 40 percent of total portfolio.
c
Sum of foreign government and corporate bonds must be less than 40 percent of total portfolio and $5 billion per insurer under CME-like limits.
Notes: Amounts in billions of dollars. Data as of 2014:Q3.
Sources: Statutory filings via SNL Financial and authors’ calculations.
a
b

that was projected to be needed for initial margin in
the previous section. Thus, it appears unlikely that
collateral availability for initial margin will be a binding constraint for most insurers in the foreseeable
future.19 This is in contrast to the situation for many
other types of derivatives market participants, which
may have large OTC derivatives positions but do not
necessarily hold large volumes of high-quality securities, giving rise to increased demand for collateraltransformation services.20
Although insurers have little incentive to engage in
collateral transformation (apart, perhaps, from increased
repo activity, as discussed later), the requirement to post
initial margin will still involve some ongoing costs. CCPs
typically charge fees of 10 to 25 basis points to service
collateral (in addition to the other fees associated with
central clearing). This is on top of any collateral administration fees charged by the insurer’s clearing member.
Variation margin
The potentially costly scenario for insurers with
respect to variation margin is one in which long-term
interest rates rise significantly and spreads between
the yields on their assets and overnight rates widen,
even if these moves were to occur over a relatively
long period. This is because such a scenario could involve having to sell bonds to meet variation margin
on long-dated receive-fixed swaps; and the return on
that margin would be low relative to that on bonds,
representing an opportunity cost for the firm. Accordingly, we assume that variation margin on cleared swaps
is posted in cash that is raised by selling corporate
bonds and that it pays the effective federal funds rate.21

30

Thus, the cost of variation margin is driven by the
spread between the corporate bond rate and the fed
funds rate in each of our Monte Carlo scenarios.22
Figure 5 shows the corresponding distribution of losses
(more precisely, forgone revenue), relative to what
would obtain if there were no margin requirements.
For the constant-maturity scenario, the amount
of projected variation margin was relatively small,
and consequently the forgone revenue associated with
variation margin is also small—$180 million per year
by the end of the projection period in the mean case.
While this amount is not trivial, it would not represent
insurmountable costs for the industry. For example,
profits at the firms in our sample were $24 billion in
2013,23 so that even for extreme interest rate paths,
margin-related costs would amount to less than 1 percent of earnings. In large part, this modest outcome
has to do with the factors noted in the previous section
that keep margin small when the distribution of swaps
stays fixed.
Again, the scenario in which insurers extend the
duration of their portfolios results in much larger median outcomes and a much wider range of possibilities. The mean cost of posting variation margin rises
to about $760 million per year; and, for adverse interest rate outcomes (a steeply rising yield curve and a
widening of the spread between corporate yields and
the PAI), the cost could be over $2.5 billion per year.
Other costs
In addition to the opportunity cost of variation margin,
there are other costs for insurers to consider. In particular, organizational and operational details may introduce

1Q/2015, Economic Perspectives

FIGURE 5

Forgone revenue from required margin
under alternative scenarios
$billions
3.0
2.5
2.0
1.5
1.0

Implications for the industry

0.5
0.0

beyond the amount of margin that is required of them
at each point in time. Using a similar calculation as
we did earlier, we note that insurers would require
an increment of about $2 billion in our constantmaturity scenario and $8 billion in our durationextension scenario to keep cash on hand to satisfy,
say, 99 percent of five-day movements in swaps
positions—assuming that margin could be frictionlessly netted across all contracts and accounts. This
compares with their current cash balances of about
$16 billion.

2014

’16

’18

’20

’22

’24

Notes: The figure shows the range of the opportunity cost of
holding cash variation margin for the top 20 OTC derivatives
users under the static-portfolio (blue) and duration-extension
(red) scenarios. Solid lines represent means; shaded regions
are 10–90% confidence bounds, based on the distribution of
forecasted interest rates depicted in figure 2.
Source: Authors’ calculations.

complications, especially in the short run. For example, it may be that the subsidiaries of an insurance
company that currently hold its swaps positions are
not the same subsidiaries that hold its high-quality
collateral. Insurers could respond by consolidating or
rearranging the corporate structure or by transferring
exposures and assets across entities.
Though relatively minor, there are also capital
issues involved with collateral management. For example, collateral pledged for derivatives positions
continues to be counted as an asset of the pledging
insurance company, but it receives an additional
risk-based capital charge, reflecting the risk that it
may not be available to pay policyholder claims in
the event of default.
The cost of derivatives trading may also increase.
CCPs charge maintenance and transaction fees for swap
clearing, although these are on the order of fractions
of basis points. Perhaps more significantly, clearing
members face significant new costs associated with
account administration, default fund contributions to
CCPs, and clearing. It is likely that they will pass on
most of these costs to clients in the form of increased
fees. The costs of trading uncleared derivatives are
likely to increase by even more as liquidity deteriorates for such products.
Furthermore, in order to ensure that they can
meet variation margin on an ongoing basis, insurers
will have to maintain buffers of cash, highly liquid
securities, or access to liquidity from other providers

Federal Reserve Bank of Chicago

Although we find it unlikely that the direct costs
of posting margin will be unbearable for the life insurance industry, these costs could nonetheless amount
to billions of dollars per year, and the bulk of this
amount would fall disproportionately on a handful of
larger firms. These firms thus have incentives to try
to minimize their margin burden.
One obvious way to reduce collateral needs is
simply to reduce derivatives positions. Since most insurer derivative use reflects hedging, rather than speculative activity, this could result in greater exposure
to risk. However, much of insurers’ derivatives-based
hedging activity reflects accounting and regulatory
considerations, not necessarily economic ones. For
example, some of insurers’ receive-fixed swaps are
matched to specific bonds held on their balance sheets
or otherwise qualify as highly effective hedges under
GAAP. (This explains why they maintain large portfolios of both pay- and receive-fixed swaps.) Reducing
hedging of this purely accounting sort would not
necessarily increase overall risk. Indeed, the extent
to which insurers are able to leave economic risks unhedged will be mitigated by regulatory pressure. It
could mean an increase in GAAP earnings volatility
or in regulatory capital requirements, but insurers
would have to weigh those costs against the costs of
holding margin.
On balance, however, insurers may move toward
hedging strategies that require less collateral—particularly those that involve only cleared derivatives. As
shown in table 3 (p. 24), insurers maintain sizable
portfolios of caps, floors, and other derivatives that
are not, for the moment, subject to central clearing.
As noted earlier, many of these positions are intended
to hedge the optionality embedded in various annuity
and insurance products. If the cost of trading in these
products rises significantly—or if liquidity deteriorates—
insurers may find it advantageous to try to hedge some
of these risks using swaps or exchange-traded products,
which could introduce basis risk.

31

Another way for insurers to reduce the need for
derivatives activity—or to cover the potentially higher
costs of that activity—would be to shift some risk that
is currently hedged using derivatives to other parties.
For example, some companies may find it attractive to
offer insurance or annuity products that offload some
interest rate risk onto consumers. Indeed, insurance
companies report that, as a result of the new rules, they
are beginning to shift their mix of products by offering
relatively less attractive pricing on products that provide
long-term guaranteed payments and more aggressively
marketing products with customer participation features, such as certain whole life policies. If insurers
find it too expensive to hedge certain types of insurance products and pull back from offering them, significantly raise their prices, or modify them to pass
through risks to customers, this could reduce the economic function they serve in providing risk-sharing
services to the economy. The rules could also hasten the
exit of insurance companies from variable annuities—
which have proven expensive in the low-rate environment—as the costs of hedging guarantees on these
products will increase. Many companies have already
attempted to reduce their exposures to these products
either by ceding them to captive reinsurers or by selling them outright.
Insurers will also increasingly need to maintain
access to ready sources of liquidity to cover variation
margin. Without such a liquidity buffer, insurers might
have to make relatively large and rapid adjustments
to variation margin during episodes of market volatility, perhaps contributing to fire-sale dynamics. One
likely source of this liquidity is advances from Federal
Home Loan Banks (FHLBs). Insurers maintain sizable
portfolios of mortgage-related assets that qualify them
for FHLB membership (Paulson et al., 2014). And,
indeed, many insurers have begun to tap FHLBs for
funds in recent years. Insurers may also turn to the
broker-dealer sector to offer term repos against their
securities portfolios or other collateral-transformation
services. However, since term repos are not available
to match the duration of long-dated swaps contracts,
this strategy would be subject to rollover risk. Particularly for riskier collateral, insurers could find their
liquidity sources evaporating during a crisis, perhaps
at the same time that variation margin is rising due to
volatile market conditions. Insurers could also look to
sources of cash from elsewhere in their own corporate
structures. Securities lending operations, for example,
could potentially be scaled up to provide a source of
cash for variation margin. Alternatively, firms may look
for new ways to hold liquid assets without occupying
balance-sheet space.24

32

With respect to the impact on capital, insurers
may have an incentive to move derivatives activity
outside of the insurance operating company, where
they will not be subject to regulatory capital requirements. One way this could be done is through captive
reinsurance. However, as noted earlier, captive reinsurers themselves may not maintain reserves of cash
or high-quality securities adequate to meet margin
requirements. Thus, insurers face conflicting incentives
for corporate structure when it comes to swaps margin.
On the one hand, they may wish to move derivatives
transactions to nonoperating subsidiaries that face
less-binding capital constraints. On the other hand,
these subsidiaries themselves will be forced to hold
high-quality collateral, reducing their profitability.
To the extent that insurers need to shift their assets
into cash or liquid securities, they may look to offset
the effect on returns by taking additional risks elsewhere. This activity could be similar to behavior that
has been observed as insurers have faced weak investment returns in the persistently low-interest rate
environment (Becker and Ivashina, 2013). While, in
principle, larger cash positions and larger risky-asset
positions may leave the aggregate risk of their assets
unchanged, such a shift may well result in reduced
liquidity for the industry, since the higher-quality assets
would now be tied up as collateral.
Conclusion
Like other market participants, insurers that rely
on OTC derivatives face challenges from the new
Dodd–Frank regulations requiring the central clearing
and collateralization of most of those positions. We
have used Monte Carlo simulations to study the amount
and type of collateral that insurers may have to hold
against their interest rate swaps portfolios over the next
decade. While we find that the industry-wide costs of
collateralizing positions are likely to be modest, there
are some low-probability tail scenarios in which they
could be substantial for some large insurers, primarily
because collateral must be posted in the form of lowyielding cash assets. We have discussed a variety of
ways in which the industry might respond to these and
the other costs associated with clearing and collateralizing derivatives positions.

1Q/2015, Economic Perspectives

NOTES
Other risk-management techniques employed by insurance companies include insuring a large and diversified portfolio of risks
(which reduces uncertainty), writing insurance on lines of business
that act as natural hedges (for example, the mortality risk insurers
face from life insurance contracts can partially offset the longevity
risk associated with annuities), and sharing risk with other companies through reinsurance.
1

Cummins, Phillips, and Smith (2001), Shiu (2007), and González,
López, and Cunill (2011) investigate the factors that determine insurance companies’ use of derivatives.
9

See Berends et al. (2013) for a broader discussion of insurers’ sensitivity to interest rates.
10

For example, in the Bank of New York Mellon Corporation and
Insurance Risk’s Collateral Management Survey 2013, 7 percent of
respondents (including a global sample of large insurers) indicated
that they did not typically post variation margin, while 32 percent
indicated that they did not post initial margin (available at https://
www.bnymellon.com/_global-assets/pdf/solutions-index/collateralmanagement-survey-2013.pdf).
11

The portion of the Dodd–Frank Act applying to most large life
insurance companies took effect in June 2013. Title VII of Dodd–
Frank mandates central clearing of certain types of swaps contracts,
and in May 2013 the Commodity Futures Trading Commission
(CFTC) finalized its rule indicating the specific classes of swaps
for which central clearing will be required. These include all “plain
vanilla” interest rate swaps, basis swaps, forward-rate agreements,
and overnight index swaps (OIS) written in major currencies against
the standard short-term interest rate benchmarks (the London interbank offered rate or LIBOR, the Euro interbank offered rate or
EURIBOR, and, in the case of OIS, the fed funds rate). Credit
default swaps are also covered under title VII, and the CFTC rule
applies to CDS indexes on corporate debt. The Securities and
Exchange Commission (SEC), which has yet to publish final rules,
is responsible for single-name CDS contracts. The U.S. Department
of the Treasury has determined that physically settled foreign
exchange (FX) swaps are not subject to the Dodd–Frank central
clearing requirements.
2

Among insurance companies, the impact of the rules is only likely
to be material for life insurers, not for property and casualty insurers,
as the latter maintain substantially smaller OTC derivatives positions, both relative to their assets and in absolute terms. Unless
otherwise stated, the terms “insurers” and “insurance companies”
refer to life insurance companies in this article.
3

See, for example, Festa (2013). Others have analyzed similar questions for other types of market participants. For example, Heller
and Vause (2012) examine the collateral that swaps clearing requires
from broker-dealers.
4

While market commentary suggests that forgone revenue from investments likely represents one of the largest potential costs to the
industry associated with Dodd–Frank OTC derivatives rules, our
calculations do not include other possible costs associated with uncleared derivatives or operational and organizational costs that may
result from the new clearing regime.
5

These data come from quarterly statutory filings and cover only
insurance-operating subsidiaries.
6

We also note that although notional value is a convenient way of
summarizing the size of a derivatives position, it is not a good
measure of the potential loss or gain associated with that position,
which is typically an order of magnitude smaller. For this reason,
the importance of derivatives may be better captured by their “fair
value,” which reflects their economic worth based on current market conditions—see table 3 (p. 24).
7

Note that these data include collateral for both OTC and listed derivatives, but the amount associated with the latter is very small as
insurers generally do not engage in much futures activity or write
options.
12

The move to central clearing could actually reduce netting opportunities in some situations by forcing insurers to clear some trades
that could previously have been netted against other trades that will
remain uncleared (and thus with non-CCP counterparties).
13

Experiments using quarterly data did not yield substantially
different results.
14

Most fixed-to-floating swap contracts on insurers’ books already
satisfy the conditions for central clearing. Those that do not likely
differ from clearing-eligible contracts in only relatively minor ways,
such as the timing of interest payments or the day-count convention.
As noted, we essentially assume away the other types of interest
rate derivatives. Evaluating collateral that would have to be held
against nonswap contracts would be a more challenging problem
because of the diversity of such contracts and the complexity involved in computing their fair values. Most of these positions will
not, at least initially, be centrally cleared. Margin requirements for
uncleared derivatives have yet to be finalized but are certain to be
more punitive than those for cleared positions. Given the harsher
rules that will apply to these trades, insurers have an incentive to
move away from such nonstandard contracts going forward, so that
our assumption may not be much of an exaggeration. Furthermore,
the framework developed by the Committee on Payment and
Settlement Systems and the Technical Committee of the International
Organization of Securities Commissions (CPSS-IOSCO, 2013)
proposes exempting uncleared derivatives from initial margin requirements until 2019 for end-users with less than €3 trillion in
notional value. Thus, initial margin on uncleared OTC contracts
will likely not be collected from insurance companies until at least
the middle of the projection period considered here. While most
CDS contracts will be centrally cleared sooner and could in principle
be incorporated into this analysis, those positions are a fairly small
fraction of insurers’ overall portfolios and do not seem likely to
significantly affect the results.
15

Dodd–Frank mandates a 99 percent level of confidence, but the CME
(Chicago Mercantile Exchange), for example, uses 99.7 percent.
As we do here, CCPs typically assess the distributions of derivative
gains and losses for the purposes of calculating initial margin using
a five- or ten-year look-back period. Indeed, the results are roughly
in line with industry estimates, which have suggested that the initial
margin requirements will amount to anywhere from 1 percent to
10 percent of the notional value of a single (one-way) swap contract.
See, for example, Heller and Vause (2012).
16

Interest rate swaps are an agreement between two parties in which
one stream of future interest payments is exchanged for another,
based on specific notional principal amounts. In a pay-fixed (or
“receive-float”) interest rate swap, a company makes fixed payments
and in return receives a floating payment linked to an interest rate.
In a pay-float (or “receive-fixed”) interest rate swap, a company
makes a floating payment linked to an interest rate and in return
receives a fixed payment. In both cases, the fixed payment is agreed
upon by both parties at the inception of the contract.
8

Federal Reserve Bank of Chicago

33

The calculations here assume that potential efficiencies from netting are completely exhausted—that is, that 100 percent of the fairvalue gains in contracts is netted against the fair-value losses of
contracts when determining potential portfolio losses for the purposes of calculating initial margin. In reality, these efficiencies may
be smaller, either because contracts are cleared through multiple,
separate accounts or because CCPs do not fully incorporate all netting possibilities into their initial margin calculations. The CME
has recently begun offering cross-margining between futures and
swaps positions, possibly allowing initial margin requirements to
be reduced further for insurers with futures exposure.
17

See http://www.cmegroup.com/clearing/financial-and-collateralmanagement/collateral-types-accepted-irs.html.
18

This conclusion applies only to insurance operating companies.
As noted earlier, several large insurance organizations have significant derivatives portfolios elsewhere in their corporate structure,
and the legal entities that hold them do not necessarily maintain
securities portfolios that would be adequate to cover initial margin
under these assumptions.
19

Indeed, the demand for high-quality collateral due to OTC derivatives requirements, bank regulatory requirements, and other sources
could create potential opportunities for insurance companies themselves to expand their collateral-transformation services.
20

34

Cash collateral for OTC variation margin receives price alignment interest (PAI) at a short-term interest rate in the corresponding currency. For dollar-denominated contracts, CME and LCH.
Clearnet pay PAI at the federal funds rate.
21

The federal funds rate is not a variable in our VAR, but it is nearly
perfectly correlated with the instantaneous Treasury rate. In our
simulations, we derive the fed funds rate path from the projections
for this rate, as explained in the appendix.
22

This amount reflects net income for domestic life insurance operating companies only. Earnings at the consolidated parent level are
higher. For the ten firms in our sample that are publicly traded in the
United States (and thus have easily available consolidated financial
statements computed under generally accepted accounting principles
[GAAP]), net income was $19.7 billion (relative to $16.46 billion
in net income at the operating company level for these same firms).
23

For example, although not explicitly tied to the Dodd–Frank rules,
Prudential created an off-balance-sheet entity (a special-purpose
vehicle or SPV) in November 2013 to hold Treasury securities.
This structure enables the firm to source Treasury securities as
“contingent liquidity” in exchange for notes issued to the SPV. The
Treasury securities could be sold quickly to meet variation margin.
See Prudential Financial Inc. Annual Report, 2013 (p. 91).
24

1Q/2015, Economic Perspectives

APPENDIX: TECHNICAL DETAIL ON THE
MONTE CARLO EXERCISE
We assume that all fixed-for-floating swaps are plain
vanilla and that they are therefore subject to central
clearing and initial margins reflecting a 99.7 percent
confidence threshold for five-day losses. To calculate
the change in the value of insurers’ swaps positions
under the simulated rate paths, slot each firm’s portfolio
into 60 buckets, reflecting receive-fixed versus payfixed positions and maturities of one through 30 years.
We approximate the proportion of swaps in each of
these buckets for each insurer by a beta distribution
over the range 0–30 years with the parameters chosen
to match the mean and standard deviation of each insurer’s actual swaps portfolio, based on Schedule DB
of their regulatory filings, as of September 2014.
For the “constant maturity distribution” scenario,
we assume that the distribution of the stock of swaps
held by each firm is fixed over time. This implies that
the net flow in each maturity bucket must be nonzero
in each quarter in order to keep the portfolio stable
as contracts mature. In particular, the net amount of
receive-fixed swaps originated by firm i at maturity m
in each period must be
∆xi ( m, fixed )

= Β[m, α ifixed , βifixed ] − Β[m + 1, α ifixed , βifixed ],

where B[…] is the probability density function of the
beta distribution, and αifixed and βifixed are the shape
parameters for the receive-fixed swap distribution at
firm i.1 An analogous equation holds for the pay-fixed
portfolio. In principle, this net amount could be obtained
in a variety of ways. In particular, if the amount is
positive, one could terminate y notional value each
quarter and originate Δx+y in new contracts, for any
arbitrary number y. We assume that, within any type/
maturity bin, a firm never terminates and originates
contracts at the same time. Thus, if xi(m) is the desired
notional value for the stock of swaps in bucket m and
xi(m+1) swaps are rolling down into that bucket from
maturity m+1, the firm will either (if xi(m+1) < xi(m) )
originate swaps with xi(m) – xi(m+1) notional value
without terminating any of the existing ones or (if
xi(m+1) > xi(m)) terminate swaps with xi(m+1) – xi(m)
notional value without originating any new ones. In
the cases in which firms terminate swaps, we assume
that they do so without regard to the contract’s age or
original maturity. New swaps are assumed to be originated at zero fair value.
For the “duration extension” scenario, we assume
that the legacy swaps portfolio gradually matures

Federal Reserve Bank of Chicago

over time. The amounts that mature are rolled into
new 30-year receive-fixed swaps.
For pricing purposes, we assume that all swaps
have quarterly payments, are indexed to the instantaneous risk-free rate, and are priced off of the same
discount curve as Treasury bonds. The m-maturity
swap rate at time t is given approximately by
Rt ( m ) ≈

δt ( 0 ) − δt ( m )
m

∑ δt ( n )

,

n =.0

where δt(n) is the time-t n-period discount rate. This
formula is an approximation because the numerator is
only strictly correct in continuous time and the denominator ignores intraquarter discounting. (If swap payments were made continuously, rather than quarterly,
the formula would be exact.) The fair value (as a fraction of notional value) of a receive-fixed swap contract
with remaining maturity m that was originated s periods
ago, is given by the formula
m

FVt ( m, s ) ≈ δt ( m ) − δt ( 0 ) + Rt − s ( m + s ) ∑ δt ( n ).
n =.0

Consequently, to value the swaps portfolio, one must
know both the distribution of remaining maturities
and the distribution of origination dates conditional
on the current remaining maturity.
To measure δt(n), we use zero-coupon Treasury
rates through 2014:Q3 and projections for those rates
from a vector autoregression (VAR) for subsequent
dates. For the m-maturity yield yt(m), by definition,
δt (m) = exp  −myt ( m )  .

The data are the zero- (instantaneous), one-,
three-, seven-, 15-, and 30-year yields computed by
Gürkaynak, Sack, and Wright (2007) over the period
1986:Q1–2014:Q3. The Moody’s Baa corporate yield,
real gross domestic product growth, and Personal
Consumption Expenditures Price Index inflation are
also included in the VAR. We begin the sample in
1986 because that is the first date at which 30-year
yields become available.
Data are simulated from the VAR by drawing both
from the distribution of parameter estimates and the
distribution of error terms, assuming normality for
both, and simulating forward 20 quarters from 2014:Q3.
The zero lower bound is imposed by rejecting any
draw for which any interest rate would be below zero
at any time; in this case, the whole vector of shocks
for that period is resampled. In addition, to reflect
current forward guidance about the level of short-term
rates (as well as current market expectations), we impose through rejection sampling that the fed funds rate
cannot rise above 25 basis points until at least 2015:Q2.2

35

For each simulated value of the six Treasury yields
that are included in the VAR, the entire yield curve is
interpolated using a quadratic spline. This allows for
the calculation of the swap rate associated with each
of the 30 possible maturities at each point in time.
To calculate the opportunity cost of holding variation margin, we assume that variation margin must
be posted in cash and is remunerated at the fed funds
rate, consistent with current practice at the major
clearinghouses. We approximate the federal funds
rate in each simulation by the equation
ffrt ≈ 1.072yt (0),
where the coefficient was estimated from an ordinary
least squares regression, with an R2 of 0.998. We assume
that, under normal circumstances, the opportunity cost
of holding cash is the Baa corporate bond yield. Since
a significant portion of insurers’ securities portfolios
consist of bonds that are generally safer and thus typically pay lower yields than Baa corporates, this is a
conservative assumption. However, occasionally in
our simulations some Treasury rates (or the fed funds
rate itself) may rise above the corporate bond rate, and
in that case we use the higher rate. Specifically, the
quarterly opportunity cost is then calculated as
VMt × (max[rt] – ffrt)/4,
where rt is the time-t vector of yields simulated from
the VAR.
To calculate initial margin, we first estimate the
covariance matrix of five-day changes in swap fair
values between 2004:Q1 and 2014:Q3, across a 10 x 10
grid of maturities spanning zero to 30 years and swap
rates spanning 0 percent to 10 percent. In each of the
same 10,000 simulations used to calculate variation
margin, we calculate the amount of each firm’s receiveand pay-fixed portfolio that falls within each of the
100 bins in the grid. Multiplying these weights by the
covariance matrix of swap value changes allows us to
approximate the five-day variance of each portfolio.3
The initial margin is assumed to be the 0.3 percent
quantile of a normal distribution with this variance
and a mean of zero.

36

NOTES
The amount of 30-year swaps originated each period is simply
equal to the stock of swaps maintained in the 30-year bin (that is,
the normal PDF evaluated at that point).
1

Since some parameter draws can imply nonstationary dynamics that
lead to explosive behavior, we also impose restrictions to ensure
that no projected rate exceeds its historical maximum. In addition,
we impose that the spread of the corporate bond to the seven-year
Treasury yield cannot be negative.
2

This calculation assumes that the initial margin that applies to a given
portfolio remains constant over time. In practice, central counterparty clearinghouses are likely to adjust margin requirements with
the level of rates, as the conditional covariance matrix of swap values
changes is not constant. Our calculation likely errs on the conservative side—estimating too much initial margin—because we forecast interest rates to rise, and the volatility of a given swap’s value
is generally decreasing in the level of rates.
3

1Q/2015, Economic Perspectives

REFERENCES

Bank for International Settlements, Basel Committee
on Banking Supervision, and Board of the International Organization of Securities Commissions,
2013, “Margin requirements for non-centrally cleared
derivatives,” Basel, Switzerland, September, available
at http://www.bis.org/publ/bcbs261.pdf.
Becker, B., and V. Ivashina, 2013, “Reaching for
yield in the bond market,” working paper, October.
Berends, K., R. McMenamin, T. Plestis, and R. J.
Rosen, 2013, “The sensitivity of life insurance firms
to interest rate changes,” Economic Perspectives,
Federal Reserve Bank of Chicago, Vol. 37, Second
Quarter, pp. 47–78, available at
https://www.chicagofed.org/publications/
economic-perspectives/2013/2q-berendsmcmenamin-plestis-rosen.
Cummins, J. D., R. D. Phillips, and S. D. Smith,
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industry,” Journal of Risk and Insurance, Vol. 68, No. 1,
March, pp. 51–91.
Festa, Elizabeth D., 2013, “New derivatives rules
raise life insurers’ collateral needs,”
LifeHealthPro.com, June 27, available at
http://www.lifehealthpro.com/2013/06/27/
new-derivatives-rules-raise-life-insurerscollater?page_all=1 .

Federal Reserve Bank of Chicago

González, L. O., S. F. López, and O. M. Cunill,
2011, “Hedging with derivatives and value creation:
An empirical examination in the insurance industry,”
Frontiers in Economics and Finance, Vol. 8, No. 1,
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Gürkaynak, R. S., B. Sack, and J. H. Wright,
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present,” Journal of Monetary Economics, Vol. 54,
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Heller, D., and N. Vause, 2012, “Collateral requirements for mandatory central clearing of over-thecounter derivatives,” Bank for International
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Reserve Bank of Chicago, No. 318, January, available
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