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A review from the
Federal Reserve Bank
of Chicago

L,all tor

1995 Conference or
Bank Structure
& Competition

Does inflation reduce productivity?............................................................2
A rgia Sbordone and K enneth K u ttn e r

What would be the long-run economic benefit of
reducing the rate of inflation? Conversely, what would
be the cost of allowing inflation to rise above the
current rate? The authors find that the cyclical
relationship between inflation and productivity appears
to be driven by the state of the economy and the
actions of monetary policy, while the estimated longrun effects of inflation on productivity are highly
sensitive to the identification scheme used to distin­
guish inflation from productivity shocks.

A review of regulatory mechanisms
to control the volatility of prices.............................................................. 15
V irg in ia G race France, Laura Kodres,
and Jam es T. M oser

Regulatory proposals to control price volatility in
the futures market generally recommend one of
three approaches: margin setting, price limits, or
transaction taxes. This article reviews the evidence
for each of these mechanisms.

Index for

1 9 9 4 ..............................................................................................29

Call for papers. ............................................................................................30


IMovember/December 1994 Volume XVIII, Issue 6

Editorial direction

the Research Department of the Federal Reserve
Bank of Chicago. The views expressed are the
authors’ and do not necessarily reflect the views of
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Articles may be reprinted provided source is
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Janice Weiss, editor
David R. Allardice, regional studies
Anne Weaver, administration

Nancy Ahlstrom, typesetting coordinator
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Lynn Busby-Ward, John Dixon, graphics
Kathryn Moran, assistant editor

ISSN 0164-0682

Does inflation reduce

A rg ia S b o rd o n e an d K e n n eth K u ttn e r

What would be the long-run
economic benefit of reducing
the rate of inflation, or elimi­
nating it entirely? Converse­
ly, what would be the cost of
allowing inflation to rise above its current rate?
Answering these key monetary policy ques­
tions requires an assessment of inflation’s real
effects. The case for zero inflation rests on
the presumption that the real costs of inflation
are substantial, while arguments for de-emphasizing the zero-inflation goal presume the
Many economists have argued that high
rates of inflation create distortions that lead to
inefficient resource allocation and hence lower
productivity. Feldstein (1982), for example,
has contended that given the existing tax struc­
ture, inflation lowers the real return on capital,
discouraging capital formation. Other oftencited efficiency losses are associated with the
unproductive activities required to cope with
ever-rising prices. These include the costs of
changing posted or printed prices, and the
“shoe-leather” costs associated with holding
less cash.1
The empirical evidence on the inflationproductivity relationship has been inconclu­
sive. While many studies have sought a link
between average inflation and real growth rates
across countries, the results are mixed and tend
to be sensitive to the inclusion of additional
variables as determinants of productivity
growth.2 A recent paper by Rudebusch and
Wilcox (1994) documented a strong inverse
relationship between inflation and productivity
in the U.S. More importantly, it added a causal

interpretation and policy conclusion: Reducing
inflation, it argued, would increase productivi­
ty. These results and conclusion received a
great deal of attention in the business press and
were cited by Federal Reserve Chairman Alan
Greenspan in congressional testimony in May.
Our article examines the postwar evidence
on the relationship between inflation and pro­
ductivity in the U.S., paying particular atten­
tion to two questions that the existing literature
has not resolved. One is whether the negative
correlation documented by Rudebusch and
Wilcox is a long-run phenomenon or simply
reflects cyclical co-movements. The second
question is what assumptions are required to
interpret the correlation as a causal relationship
and conclude that a permanent decrease in
inflation would bring about a permanent in­
crease in productivity.
In the first section we describe the statisti­
cal properties of inflation and productivity and
corroborate the negative correlation at cyclical
and long-run horizons. We then take up the
interpretation of this correlation. Simple
“Granger causality” tests suggest a causal link
between inflation and productivity when only
those two variables are included in the analy­
sis. Controlling for other factors—monetary
policy, in particular—destroys that relation­
ship, however. In a four-variable vector au­
toregression (VAR) model, increases in the
federal funds rate cause productivity to fall,
while inflation lacks any predictive power.
Argia Sbordone is an eco no m ist and Kenneth
K uttner a senior econom ist and assistant vice
president at the Federal Reserve Bank o f Chicago.


The next section takes up the long-run
relationship between inflation and productivi­
ty, using a bivariate time-series model to esti­
mate the ultimate effect on productivity of a
permanent shock to inflation. An important
conclusion of this analysis is that the size and
sign of the estimated effect depend heavily on
the identifying assumptions used to distinguish
inflation shocks from productivity shocks.
This result illustrates the dangers in drawing
policy conclusions from bivariate correlations.
In addition, robustness checks show that the
strength of the long-run effects depends on the
inclusion of the oil-shock episodes.
The article also discusses the economics
that may underlie the strong negative cyclical
relationship between inflation and productivity
observed in the data. A box sketches the ele­
ments of a model that would exhibit this prop­
erty. In such a model, a monetary policy rule
that raises short-term interest rates in anticipa­
tion of future inflation can generate a negative
correlation at cyclical frequencies. This argu­
ment relies on procyclical productivity behav­
ior, and a lag between economic activity and
changes in the rate of inflation. Immediately
after a monetary contraction, therefore, infla­
tion remains high while output contracts;
meanwhile, labor hours fall less than output
because of labor hoarding. The fact that labor
productivity falls while inflation remains high
generates the negative correlation in the data,
rather than any causal link from inflation to
A look at the data

Figure 1 plots the quarterly series of infla­
tion and productivity used in this article. Infla­
tion is the annualized growth rate (approximat­
ed by the change in the logarithm) of the gross
domestic product (GDP) deflator for the non­
farm business sector. The productivity index is
the output-to-labor ratio, or average labor pro­
ductivity, in the non-farm business sector.3
Figure 2 displays the trend and cyclical
components of inflation and productivity
growth, obtained by passing the series through
a band-pass filter that allows respectively only
the low-frequency components (panel A) and
the business cycle frequencies (panel B).4
Panel A shows that inflation and productivity
both exhibit a great deal of low-frequency
variation. This also appears in the descriptive
statistics reported in table 1, which shows that



productivity was generally lower and inflation
higher in the two decades following the 1973
oil shock. In addition, inflation and productivi­
ty appear to be strongly negatively related; the
correlation coefficient is -0.36 in the unfiltered
data and -0.47 for the cyclical components (not
reported in the table).
Causality te sts

The negative correlation between inflation
and productivity apparent in figure 2 and docu­
mented in table 1 is quite robust—so much so
that it is tempting to jump immediately to the
conclusion that inflation causes productivity to
fall. But since correlation does not imply cau­
sality, such a conclusion might be premature.
Low productivity growth might cause inflation
to increase, for example, rather than the other
way around. Alternatively, the correlation
might represent a common response to some
third factor rather than an underlying structural
relationship between inflation and productivity.
One way to go beyond the simple correla­
tion reported above is, following Rudebusch
and Wilcox, to employ tests for Granger causal­
ity. Although the name suggests that these tests
can determine whether inflation is the underly­
ing cause of productivity fluctuations, their
notion of causality is much narrower. All these
tests can do is determine whether current infla­
tion is useful for forecasting future changes in
productivity—clearly quite distinct from the
idea of logical causality.
Table 2 corroborates the RudebuschWilcox finding that inflation indeed strongly
“Granger causes” productivity growth in a
simple bivariate relationship. The significant
F-statistic for inflation in the productivity re­
gression indicates that inflation contains infor­
mation useful for predicting future productivity
growth, supporting a causal link running from
inflation to productivity. The negative sum of
the coefficients, reported below the F-statistic,
is consistent with the negative relationship
visible in figure 2. Although the effect is statis­
tically significant, the R2 statistic indicates that
lagged productivity growth and inflation to­
gether account for only 6 percent of the vari­
ance in productivity growth.
This finding, along with the insignificant
F-statistic for productivity in the inflation equa­
tion, appears to support the contention that
inflation reduces productivity. What it does not
indicate, however, is whether the bivariate



An overview of inflation and productivity
A. Inflation
percent, annualized rate (quarterly data)

B. Productivity
log level (quarterly data)

C. Productivity growth
percent, annualized rate (quarterly data)

Granger causality represents a structural rela­
tionship or is merely an artifact of the short-run
co-movements of inflation and productivity
over the business cycle.


Sources o f cyclical co-movements

A great deal of theoretical and empirical
research has gone into exploring mechanisms
that can produce a negative correlation



Inflation and productivity growth
A. Trend components
percent, annualized rate

B. Cyclical components
percent, annualized rate

between the movements in inflation and in
productivity that occur over the business cycle.
One plausible mechanism involves the Federal

Descriptive statistics
1947:Q1 1994:02

1947:Q1 1973:03




Standard deviation




P rod uctivity gro w th



Standard deviation





C orrelation



Reserve’s reaction to anticipated inflation and
the way in which businesses respond to transi­
tory cyclical downturns.
Suppose, for instance, that the
Fed acts to tighten monetary poli­
cy in response to impending infla­
tion, and that inflation thereafter
1973:04declines more slowly than real
activity. Faced with costs associ­
ated with adjusting their work
force, firms will reduce employ­
ment only gradually and may
retain more workers than they
really need—a phenomenon
known as labor hoarding. The
result is a fall in output and labor
productivity, coinciding with high
inflation. In this scenario, which


ality tests are based. Two obvious
candidates for cyclical controls are
Inflation and productivity in a bivariate VAR
the growth rate of real GDP, which
Right-hand side variables
captures the level of overall eco­
nomic activity, and the federal
funds rate, whose movements are
P rod uctivity
related to changes in the Federal
3 .3 5 ***
Reserve’s monetary policy. Table 3
Sum o f coefficients
-0 .2 8 ** *
reports results from a VAR that
includes these two additional
The table shows that including
Sum of coefficients
GDP and the federal funds rate
Note: Each equation in the VAR contains four lags of productivity
destroys the inflation-productivity
growth and inflation. The VAR sample period is estimated on
link that was present in the bivari­
quarterly data from 1950:Q1 through 1994:Q2.
♦♦♦Significant at the .01 level.
ate VAR. In the four-variable sys­
♦♦Significant at the .05 level.
tem, the Granger causality .F-statis­
♦Significant at the .10 level.
tic for inflation in the productivity
equation is a statistically msignificant 0.57. By contrast, the F-statis­
is a feature of models similar to the one
tic for the federal funds rate is significant at the
sketched in the box, the negative correlation
.01 level, and increases in the interest rate are
between inflation and productivity is only an
associated with declining productivity. More­
artifact of the monetary rule and the timing of
over, with output and the interest rate included,
the regression now accounts for 20 percent of
output and price responses over the cycle.
One way to control for common cyclical
the variance of productivity growth. The fact
factors is to include additional control variables
that the federal funds rate is itself Grangerin the regression on which the Granger caus­
caused by each of the other three variables


Inflation and productivity in a four-variable VAR
Right-hand side variables
P rod uctivity




Funds rate


Sum o f coefficients


3 .8 7 ***






-0 .2 6 ** *





5.2 3***

Sum of coefficients




Real GDP




Sum o f coefficients
Funds rate
Sum o f coefficients


4 7 2 ** *


-0 .2 6 *

-0 .2 0 ** *


6 .1 5 ***


0 .1 6 ***


Note: Each equation in the VAR contains four lags of productivity growth, inflation, real GDP growth, and the
federal funds rate. The VAR sample period is estimated on quarterly data from 1950:Q1 through 1994:Q2.
♦♦♦Significant at the .01 level.
♦♦Significant at the .05 level.
♦Significant at the .10 level.



suggests that monetary policy is set in large
part in response to economic activity and infla­
tion, and it is this mechanism that generates the
observed correlation. Clearly, omitting vari­
ables that capture the state of the economy and
the stance of monetary policy means overlook­
ing a very important part of the picture.
These Granger causality tests can provide
only circumstantial evidence on the underlying
structural relationship between inflation and
productivity. Nevertheless, the results suggest
that the negative correlation observed in the
data reflects the interaction of monetary policy
and real economic activity more than a direct
causal link between inflation and productivity.


Tests for unit roots
ADF t-statistics

Inflation (n)



Change in in fla tio n (An)



Log p ro d u ctivity (z)



P rod uctivity g ro w th (Az)



0.01 critical value



0.05 critical value



0.10 critical value



Note: The results are based on regressions
containing six lags, estimated on quarterly data
from 1950:Q1 through 1994:Q2.

In fla tio n and p roductivity in
the long run

An alternative way to eliminate or at least
minimize the effects of common cyclical co­
movements in inflation and productivity is to
focus on the long-run relationship between the
two series. The analysis will, therefore, build
on an econometric model designed to capture
long-run effects. The goal will be to determine
whether inflation shocks are associated with
permanent changes in labor productivity, and
if so, whether this relationship is structural, in
the sense that a permanent reduction in infla­
tion would lead to a long-run increase in pro­
As King and Watson emphasize (1992,
1994), an analysis along these lines is possible
if inflation and productivity can both be char­
acterized as integrated processes. The salient
feature of such processes is that unanticipated
movements or “shocks” tend to persist indefi­
nitely as a result of a unit root in the variables’
time-series representation.5 Because the effects
of shocks last indefinitely, such series never
revert to an underlying mean or deterministic
trend; instead, they appear to exhibit a random­
ly varying or stochastic trend.
Table 4 presents Augmented DickeyFuller (ADF) tests for unit roots in inflation
and productivity. The x^ column reports test
statistics from a regression that includes a
constant term; the x( statistics are from a re­
gression that includes constant and trend terms.
The test statistics for inflation and productivity
are only barely significant at the .05 and .10
levels, respectively indicating that shocks to
inflation and productivity tend to be highly
persistent. This suggests treating both series as



integrated processes in order to model the
long-run effects of permanent changes.
A general econometric model describing
the interaction between inflation and produc­
tivity can be constructed by allowing the
change in the inflation rate, Arc, to depend on
the current rate of productivity growth, Az ,
and lags of both variables. Similarly, produc­
tivity growth may depend on current inflation
and lags of both variables. Formally, the mod­
el can be represented by a pair of equations,
(1) A7t = X Az + ~



7 t7 t

Ant-J .

7= 1


+ £0Cy Azt-J £n
. . ™ .+ t


(2) A tz = X A n1 + ' Z a j Ani .





+ £ a zz Azt-J. + ez,

where the e* and £;' disturbance terms represent
inflation and productivity shocks, respectively.
An obvious interpretation of the inflation
shock is to ascribe it to monetary policy; simi­
larly, the interpretation of the productivity
shock is as a random shift in the production
function.6 The “impact multipliers”, ^ .a n d
X_K, capture within-quarter feedback from
productivity to inflation, and from inflation to
productivity, respectively.
To evaluate the real costs of inflation, the
key parameter in this model is the long-run


Labor hoarding and measured labor productivity
In this box we briefly describe how a negative
correlation between inflation and productivity can
be generated by firms’ “labor hoarding” behavior.
The model sketched here follows Sbordone (1993).
We assume that firms face costs of adjusting
labor hours and therefore increase labor utilization
(as opposed to hours) when they experience changes
in their demand conditions. The aggregate produc­
tion function,

unobservable, we can model effort variations by
solving a dynamic cost-minimization problem,
where the costs include a convex cost of adjusting
labor hours from one period to the next. The result
is that the optimal level of effort depends on how
current growth of hours compares to expected
future growth. Specifically,

(3) Yt = F(K',eHtQt),

where £ represents the expectation conditional on
information available at time t, et is the deviation of
effort from its steady state value, and the parameter
(3 is a function of the cost of adjusting hours rela­
tive to the cost of effort. In words, firms increase
labor utilization when they expect future growth in
hours will fall below current growth; conversely,
they reduce labor utilization when they expect
future growth in hours to be higher than current
growth. In the latter case, in fact, firms may want
to start hiring immediately because the marginal
cost of increasing labor is lower once the reduction
of future adjustment costs is taken into account.
In this context, any variable that affects the forecast
of hours may induce variations in productivity
through the induced adjustment of effort.
Because effort is a stationary variable in this
model, all productivity fluctuations are transitory
regardless of whether the demand shock is tempo­
rary or permanent. A temporary shift in demand is
met with effort changes only; a permanent shift is
met with effort changes in the short run and full
adjustment of hours in the long run. Any perma­
nent changes in productivity would, therefore, have
to come from changes in the capital-to-output ratio,
as shown in equation 4. Two mechanisms through
which inflation may affect the capital accumulation
process are discussed in the body of this article.

expresses output, Y, as a function of capital, K, and
effective labor, which is the product of labor utiliza­
tion (effort), e; labor hours //; and labor-augmenting
technological change 0 . The time subscript t de­
notes current values of all the variables.
Taking logarithms and first differences yields
/ FrK \
1 FU \
1 ^ — ] A ln £ +
\ Y /
' 'l Y 1
+ Ain e+ Ain 0 ),
where FK and FHdenote the partial derivatives of the
production function with respect to K and H. As­
suming constant returns to scale, labor productivity
can be written as
(4) Az = (Ayt - A h l ) = sK(Akl - A h l)

+ sH(A lne() + e;\
where lowercase letters indicate natural logarithms
and ez= sH(Ain 0 ,).
It is easy to see from this expression that a
procyclical pattern in effort can induce procyclical
behavior in labor productivity. Because effort is

response of productivity to a permanent change
in inflation. This response, denoted yzji, can be
expressed in terms of the parameters of the
structural model:

yz n

A K J= a 21
,Z +T.p. j7
l - l f .a
j= i


Later, we will test the hypothesis that y_n = 0,
that is, that a permanent change in inflation has
no long-run effect on productivity. An analo­
gous hypothesis involves the reverse of this


e , = - p ( E,Ahl+l- A h ,),

effect—that inflation exhibits no long-run re­
sponse to a change in labor productivity, which
is denoted yn_, and defined analogously to y.^.
Unfortunately, the econometric model in
equations 1 and 2 is not identified. This means
that recovering the structural parameters from
a reduced-form VAR of productivity growth
and inflation on lags of the two variables is
impossible, given the simultaneous feedback
from inflation to productivity, and vice versa.
Further restrictions or identifying assumptions
are necessary.7


Although the model is written only in terms of
real variables, it can generate productivity fluctua­
tions as a response to changes in inflation, or any
monetary variable, to the extent that these variables
help to predict the behavior of future growth in
hours. In this case, in fact, they will generate fluc­
tuations in effort.
Why might inflation forecast growth in hours?
Suppose agents know that when inflation rises,
monetary policy tightens in anticipation of further
inflation. In this case, a positive shock to inflation
signals that current growth in hours is going to be
lower than what is expected in the next period, and
therefore implies a decrease in effort. This gener­
ates a decline in output that is larger than the de­
cline in labor hours, causing productivity to fall. In
this scenario, the timing of the monetary rule and
the “hoarding” of labor produce a negative correla­
tion between inflation and productivity without any
fundamental causal link.
To go from the productivity equation 4 to
equation 2 of the text, one can solve for effort in
terms of current and past inflation variations, and
project the capital-to-hours ratio on past productivi­
ty growth and inflation, to obtain


Az = X An + Y a j An . + Y a j Az . + e; ,


where the a and X coefficients are functions of the
adjustment cost parameter and the parameters of
the projections of hours and the capital-to-hours
ratio. This equation allows for transitory effects of
inflation on productivity through variation in labor
effort, as well as permanent effects through changes
in the capital-to-hours ratio.

Identification in this model requires two
restrictions. The first is relatively innocuous:
that the inflation and productivity shocks En
and E z are uncorrelated with one another. The
interpretation of this restriction is that although
inflation and productivity are related through
the X and a parameters, their random compo­
nents come from distinct sources such as mon­
etary policy and technology. Even with this
assumption, recovering the four structural
parameters, ynz, y_n, Xnz, and XzKfrom the re­
duced-form estimates requires one additional



Lacking clear implications from economic
theory about the sign and size of these parame­
ters, we assume a value for one of them, and
then estimate the remaining three as a function
of the first. This allows us to examine the
way in which the conclusions about the longrun relationship between inflation and produc­
tivity depend on the form of the assumptions
used to identify the model. This approach
has the additional advantage of nesting other
standard empirical specifications within a com­
mon framework. The standard recursive or
Choleski decompositions of the reduced-form
VAR, for example, correspond to zero restric­
tions on the impact multipliers: X_n = 0 for
inflation ordered first, and XK = 0 for produc­
tivity first. Setting the long-run multipliers,
y and y , equal to zero is another way to
identify the model.8 In each case, the system
of equations can be estimated by instrumental
variables, where the appropriate instruments
depend on the identifying assumptions.
Details on this estimation procedure appear in
Watson (1994).
Table 5 displays the estimates of the X and
y parameters in equations 1 and 2 under the
alternative zero restrictions discussed above.
Figure 3 plots the corresponding time paths of
the productivity response to an inflation shock
of sufficient magnitude to increase the annual­
ized inflation rate by 1 percent in the long run,
and the 90 percent confidence bound for the
long-run response. The results use the infla­
tion and productivity measures described
above and are based on regressions with six
lags of each variable running from 1950:Q1
through 1994:Q2.
One important result is that both sets of
short-run restrictions, shown in the first two
rows of table 5, yield statistically significant
negative estimates of the long-run response of
productivity to inflation, y . Under the as­
sumption that inflation has no within-quarter
effect on productivity (X.^ = 0), its long-run
effect is a significant -1.86, which suggests
that a shock that reduces the inflation rate by
1 percent in the long run will increase produc­
tivity by just under 0.5 percent. The estimated
long-run effect of inflation on productivity is
even larger if we assume instead that produc­
tivity has no within-quarter effect on inflation
(Xn_= 0). In this case, a permanent 1 percent
reduction in inflation would increase produc­
tivity by roughly 0.8 percent. The marginally


contemporaneous relationship
between the two variables. One
Parameter estimates under alternative zero restrictions
plausible restriction that allows us
E stim ated param eter
to do this is to set y equal to zero.
R estrictions
The restriction makes economic
sense; it is equivalent to assuming
Xz = 0
that permanent changes in produc­
tivity have no long-run effects on
Xn = 0
the rate of inflation (although they
may lead to changes in the price
y =0
level). The estimates of y.n, and
the two within-quarter X coeffi­
y n= 0
cients, appear in the third line of
table 5.
Interestingly, although the
Note: Standard errors are in parentheses. The results are based on
regressions containing six lags, estimated on quarterly data from
point estimate of y^ is negative
1950:Q1 through 1994.Q2.
under this identification scheme, it
is smaller (in absolute value) than
it was under the short-run restrictions, and not
significant positive estimate of y generated by
statistically significantly different from zero.
this restriction is puzzling from an economic
As shown in panel C of figure 3, the estimated
point of view, however, and casts doubt on the
response of productivity to inflation is initially
plausibility of the restriction.
positive, becomes negative after one quarter,
Panels A and B of figure 4 show that this
but is never statistically significant. Panel C of
result generalizes to a range of Xs other than
figure 4 shows that to obtain a statistically
zero. It turns out that any value of X in the
significant negative value of under this
-0.4 to +0.8 range is consistent with a statisti­
long-run identification restriction requires
cally significant negative response of produc­
setting the long-run effect of productivity on
tivity to inflation, as is any value of X^k less
inflation (y;tz) to a positive number greater
than 0.2. However, as panel B shows, large
than 0.06; we have no reason to believe that
positive values of X_Kare consistent with posi­
this is a plausible range. One factor contribut­
tive values of yzK
ing to this result is the fact that the standard
It is widely recognized that short-run re­
errors associated with the parameter estimates
strictions such as these are often inappropriate
are much larger under the long-run restrictions
for identifying the economic phenomena of
than under the restrictions on X, reflecting the
interest. Although it is clear from figure 4 that
imprecision with which the long-run multipli­
the negative long-run relationship between
ers are estimated.
inflation and productivity holds up for a wide
The fourth possible identification scheme
range of Xs, the economic interpretation of
imposes a restriction on yzx. Setting it to zero
these impact multipliers is not clear. Take the
suggests that productivity is unaffected by
case of Xzn, for example, which represents the
inflation in the long run. Even though this
contemporaneous effect of an inflation shock
restriction merely assumes an answer to the
on productivity. While economic theory sug­
central question raised in the article, the esti­
gests that inflation may reduce productivity in
mates of the other parameters yield additional
the long run (by inhibiting capital formation,
insights into the results; these estimates appear
for example), it is generally silent on the shortin the fourth line of table 5.
run effects. Particularly in light of the cyclical
Although the model obtained by setting y
interactions between inflation, monetary poli­
is, like the other models, exactly identified and
cy, output, and productivity highlighted earlier,
hence unable to test statistically, the parameter
there is no reason to believe the short-run ef­
estimates it generates are sufficiently unusual
fect is zero—or any other specific number, for
to suggest that the restriction is inappropriate.
that matter.
The strangest is the estimated XzK Here, the
This argues for using an alternative restric­
statistically significant positive estimate says
tion to identify inflation’s long-run effect on
that inflation shocks increase productivity
productivity in order to remain agnostic on the





Effects of inflation shocks on productivity under alternative parameter restrictions
A. Xzn —0

B. Xm = 0

percent change, quarterly

percent change, quarterly

C- Ynz= 0

D- Yzn = 0

percent change, quarterly

percent change, quarterly

Note: Shaded areas indicate 90 percent confidence bounds.

contemporaneously, and, as shown in panel D
of figure 3, the response is uniformly positive
over the first year. While this does not repre­
sent a formal rejection of the model, such a
large, positive short-run response is hard to
rationalize theoretically.9
Suppose, then, we set a non-zero value for
y_n. Would the other parameters’ estimates
seem more reasonable? The rationale for this
exercise is that we have some theoretical justi­
fication for the hypothesis that the long-run
effect can go either way. Tobin (1965) argued
that inflation leads investors to reallocate their
portfolios away from money and into capital,
which reduces the real rate of interest, increas­
es investment, and raises labor productivity.
Taking an opposing view, Feldstein (1982)
contended that given the U.S. tax structure, an
increase in inflation depresses capital accumu­



lation, leading to a long-run decline in labor
Suppose we believe Tobin’s argument.1
Our search over possible parameter values for
y_n indicates that for values greater than 1.5, we
still get positive and significant estimates of
X_n. In that case, however, we obtain signifi­
cant negative values of both the long-run and
short-run multipliers of productivity to infla­
tion.1 Using negative values for yzK in keep­
ing with Feldstein’s arguments, does not
change the results obtained under the restric­
tion y_n = 0. Choosing a value of y_Kless than
-0.5, however, makes all other parameter esti­
mates insignificant.
The results reported in table 6 show that
the estimated long-run effect of inflation on
productivity, y_n, is rather sensitive to the
changes in sample and lag length. Each of the



Long-run effect of inflation on productivity
A. 95% confidence interval for yZ as a function of X%

B. 95% confidence interval for yZ as a function of Xz



C. 95% confidence interval for yIn as a function of yn

D. 95% confidence ellipse when yZ = 0


X ln


three right-hand columns corresponds to im­
posing one of the three zero restrictions dis­
cussed above. The first row gives the results
for the six-lag model estimated over the entire
sample from 1950:Q 1 through 1994:Q2, which
merely replicates the results already reported in
table 5.
The second and third rows of table 6 dem­
onstrate the results’ sensitivity to subsample.
Specifically, under the
= 0 restriction, the
point estimate of y_n is positive (albeit statisti­
cally insignificant) in the subsample from
1950:Q1 through 1973:Q3, and significantly
negative only in the subsample from 1973:Q4
through 1994:Q2. Similarly, the estimates of
yz n obtained under both sets of short-run re*
strictions are significant at only the . 10 level in
the early subsample. These results are consis­
tent with the view that the high-inflation oil
shock years are largely responsible for the


strength of the statistical association between
high inflation and sluggish productivity growth.
The results are also sensitive to the chosen
lag length. When four lags are used, the size of
the estimated long-run effect of inflation on
productivity is less than half of what it was with
six lags. Moreover, with eight lags, the estimate
of y. is insignificant at the .05 level under both
the A —0 and the y = 0 restrictions.
Overall, these results suggest that higher
rates of inflation are associated with lower pro­
ductivity in the long run. The strength of this
conclusion depends, however, on the identifying
assumption used to differentiate inflation shocks
from productivity shocks. Under the plausible
assumption that productivity does not affect (or
reduce) inflation in the long run, the evidence
connecting inflation to lower productivity is
very weak. Furthermore, the effect appears to
be strongest in the years following the 1973 oil


sign of an underlying structural
Estimates of
for alternative samples and lag lengths
The results from both econo­
R estriction im p osed
metric techniques used in this
article demonstrate the fragility of
Sam ple
the empirical link between infla­
1950:Q1 - 1994:Q2
tion and productivity. In Granger
causality tests, including the feder­
1950:Q1 - 1973:Q3
al funds rate eliminates inflation’s
effect on productivity, which sug­
1973:Q4 - 1994:Q2
gests that a major part of the corre­
lation is cyclical in nature. Esti­
1950:Q1 - 1994:Q2
mates of inflation’s long-run effect
on productivity in a dynamic
1950:Q1 - 1994:Q2
structural model are highly sensi­
tive to the identification scheme
used to distinguish inflation from
Note: Standard errors are in parentheses.
productivity shocks.
These findings illustrate a
shock, and its magnitude and statistical signifi­
more general point about the pitfalls of draw­
cance depend heavily on the number of lags
ing policy conclusions from reduced-form
included in the regressions.
statistical relationships. Without a solid theo­
retical framework, it is impossible to tell
whether the negative time-series correlation
It is very easy to detect a negative correla­
implies a policy trade-off or merely reflects the
tion between inflation and productivity in the
way in which output, inflation, and productivi­
data. It is much more difficult to conclude that
ty jointly depend on the state of the economy
higher inflation causes productivity to fall.
and the actions of monetary policy.
The results presented above argue for caution
in interpreting the observed correlation as a

Ynz =


'Fischer (1993) surveys these costs and evaluates them for
a cross section o f countries.
2See Levine and Renelt (1992).
’These are the same series analyzed by Rudebusch and
Wilcox (1994). Alternative measures o f productivity and
price level yield similar results.
4The low-pass filter that extracts the trend component is
designed to pass components with periodicity longer than
8 years, while the filter to obtain the cyclical components
passes the components with periodicity between 6 quar­
ters and 8 years. Both filters are approximated with a
moving average from t - j to t + j , where j = 20 quarters
and the weights are normalized to sum to 1.
Tor an introduction to the use o f integrated processes in
econometric modeling, see Stock and Watson (1988).
6The box shows how to derive equation 2 from a produc­
tion function.

two variables, namely to v ”= A n t - E(_, A n i and i);= Az(
- E; Az . The identifying assumptions allow us to map
these innovations into the structural disturbances £*
and £j.
8Blanchard and Quah (1989) used this type o f restriction
to identify aggregate supply and demand shocks.
9We should note, however, that the confidence ellipse for
the two impact multipliers (shown in panel D o f figure 4)
suggests a high probability of observing values o f \ xn
very close to zero, together with a significantly negative
impact o f productivity on inflation.
1 The results o f King-Watson (1994), which reject the
Fischerian neutrality hypothesis, are consistent with this
Tobin effect.
"For example, setting y_n = 1.6 yields
= 1-56 (standard
error = 0.47),
= -0.7 (standard error = 0.14), and
= -0.11 (standard error = 0.05).

’Technically, the VAR describes only the response of
inflation and productivity to innovations in each o f the




Blanchard, Olivier J., and Danny Quah, “The
dynamic effects of aggregate demand and sup­
ply disturbances,” American Economic Review,
Vol. 79, 1989, pp. 655-673.
Feldstein, Martin, “Inflation, tax rules, and
investment: Some econometric evidence,”
Econometrica, Vol. 50, 1982, pp. 825-862.

American Economic Review, Vol. 82, No. 4,
1992, pp. 942-963.
Rudebusch, Glenn D., and David W. Wilcox,
“Productivity and inflation: Evidence and
interpretations,” Board of Governors of the
Federal Reserve System, manuscript, 1994.

Fischer, Stanley, “The role of macroeconomic
factors in growth,” Journal o f Monetary Eco­
nomics, Vol. 32, 1993, pp. 485-512.

Sbordone, Argia M., “Cyclical productivity in
a model of labor hoarding,” Federal Reserve
Bank of Chicago, working paper, No. 93-20,

King, Robert G., and Mark W. Watson,
“Testing long-run neutrality,” Federal Reserve
Bank of Chicago, working paper, No. 92-18,

Stock, James H., and Mark W. Watson,
“Variable trends in economic time series,”
Journal o f Economic Perspectives, Vol. 2,
1988, pp. 147-174.

_______________ , “The postwar U.S. Phillips
curve: A revisionist econometric history,”
Carnegie-Rochester Conference Series on
Public Policy, 1994 forthcoming.

Tobin, James, “Money and economic
growth,” Econometrica, Vol. 33, 1965, pp.

Levine, Ross, and David Renelt, “A sensitivity
analysis of cross-country growth regressions,”

Watson, Mark W., “Vector autoregressions
and cointegration,” Handbook o f Economet­
rics, Vol. 4, 1994.



A review of regulatory mechanisms
to control the volatility of prices

V irg in ia G race F ran ce, Laura Kodres,
and Ja m e s T. M o ser

The stock market crash of
1987 renewed claims that cash
market problems can stem
from the trading of futures
contracts. The crash also led
to proposals for increased regulation to control
price volatility. These proposals have anteced­
ents in the Populist movement of the 1890s.
Farmers of that period complained that wheat
futures trading caused high prices at planting
time and low prices at harvest. The tradition of
curing cash market problems by regulating the
futures markets was well established by World
War I. In 1917, the New York Cotton Ex­
change was pressured into incorporating price
limits into its cotton-futures contracts as a
solution for price volatility following the Ger­
man threat of submarine attacks on freight
shipments into European ports.
After the war, Congress passed a tax on
futures transactions that was aimed at solving
the problem of low wheat prices. Low grain
prices during the early years of the Great De­
pression led New Deal interventionists to pres­
sure the futures markets to drop the trading of
options on futures—then called privileges—
and to institute price limits. In addition, con­
tract specifications, including margins on fu­
tures contracts, were placed under regulatory
oversight. Later, a bout of volatility in onion
prices led to an absolute prohibition of trading
in onion futures. This prohibition remains in
effect today despite evidence developed by
Roger Gray that futures contracting probably
lowered rather than raised the volatility of
onion prices.1


Today’s attention focuses on stock price
volatility. As in earlier years, the proposals
gamering most of the attention seek to control
stock price volatility by regulating futures
markets, particularly stock-index futures con­
tracts. This article reviews the evidence on
three mechanisms that have been proposed to
control price volatility. The first is to increase
margin levels. Proponents of this mechanism
argue that higher margins would discourage
destabilizing speculation. A second proposed
mechanism is to set price limits or “circuit
breakers” in futures markets. Proponents of
this approach claim it would allow markets to
cool off. A third proposed mechanism is to
impose a tax on each transaction of a futures
contract. Casual descriptions of transactions
taxes refer to them as solving volatility by
throwing sand in the gears of the futures mar­
ket. In the sections that follow, we assess the
existing research on each of these three meth­
ods and their underlying rationales.
Margins and v o la tility

There is an immense literature on the ef­
fects of margin regulations on trading in finan­
cial assets, most of which deals with the effects
of margins for stock positions. For political as
well as economic reasons, the debates over
V irg inia Grace France is an assistant professor in
the finance de partm ent of the U n iversity o f Illinois
at U rbana-Cham paign. Laura Kodres is an econo­
m ist at the Board o f G overnors o f the Federal
Reserve System. Jam es T. M oser is a senior
research econom ist and research o ffice r w ith the
Federal Reserve Bank of Chicago. This paper is
based on a panel discussion by the authors at a
m eeting of the M id w est Finance A ssociation.


margins on futures and margins on stock have
become intertwined. First, we will look at
stock margin studies.
Evidence from stock markets

Since 1974, Regulation T has required
stock purchasers to make initial deposits of 50
percent of the total price of their purchase.
Figure 1 plots stock market volatility and Reg­
ulation T margin requirements historically.
The data are ambiguous on the relationship
between the two. If one compares the Great
Depression years with the postwar period when
margins were federally regulated, it is clear
that margins were generally higher and volatil­
ity was less after the war than during the
1930s. This suggests that higher margins re­
duce volatility. Yet studies by Officer (1973)
and Schwert (1989a, 1989b) point out that
volatility was also low before the Great De­
pression. Though it is hard to pin down pre­
cisely why volatility shifts, it probably has
more to do with general macroeconomic condi­
tions than with margins. The postwar decline
in volatility may simply reflect a return to
normal levels after the turmoil of the 1930s.
In 1984, the Federal Reserve Board of
Governors assessed the existing research on
margins and concluded that Regulation T re­
quirements had no reliable, economically use­
ful impact on volatility. As a result, Regula­
tion T margin requirements have been left


unchanged since 1974. Yet subsequent studies
by Hardouvelis (1988, 1990) found that mar­
gins did in fact have an important economic
impact on volatility. His analysis suggested
that if margin requirements were increased
from, say, 50 percent to 60 percent, the average
variability of the stock market would decrease
by 7 percent or 8 percent—a huge effect rela­
tive to prior studies.
This study lent indirect support to the
conclusions of the Brady Commission (1988)
on the crash of 1987, which called for the har­
monization of margins across the stock and
derivatives markets. Extrapolating largely
from previous studies of stock margins, it
called for futures margins that averaged 10
percent before the crash to be raised closer to
the 50 percent required for stocks.
A number o f economists re-examined

Hardouvelis’s data.2 The main criticism, par­
ticularly highlighted in the influential paper by
Hsieh and Miller (1990), was that Hardouvelis
was picking up a spurious relationship. Since
margins change only infrequently, the time
series has a great deal of persistence, as does
volatility. Given two persistent series, regress­
ing the levels of one on the levels of the other
can falsely suggest a significant relationship
when there is in fact none. Empirical tests that
correct for this problem did not find any signif­
icant impact of margins on volatility. Howev­
er, Regulation T margin requirements have


been changed only 22 times, so there may not
be enough observations to show any statistical
effect. Second, Regulation T can directly af­
fect only positions held in margin accounts.
The amount of margin debt is perhaps 1 per­
cent or 2 percent of the value of stocks listed
on the New York Stock Exchange.
Evidence from futures markets

In the last few years, the focus of research
on margins has switched to the futures markets.
The futures margin that brokers collect from
customers is generally viewed as an adequate
performance bond for any reasonable price
movement.3 Empirical studies have tested the
adequacy of the minimum margins set by the
exchanges; in some cases, the actual margin
demanded by a broker is substantially greater
than the minimum.4
Clearing firms also put up a certain
amount of margin with the clearinghouse.
Margin deposits are not the only protection
provided to the clearinghouse, since clearing
firms also face stringent capital requirements.
The adequacy of margins at the clearinghouse
level has been given little empirical study since
the data are not usually available; however,
Bemanke (1990) studied the operation of the
clearinghouses and the margin system during
the 1987 crash.
Margins on futures are, of course, vastly
different in purpose and administration from



stock margins. However, a relationship be­
tween margins and volatility might be easier to
detect in futures markets, for two reasons.
First, futures margins are set individually for
each contract by the exchanges. Thus there are
many more changes in futures margins than in
stock margins. Second, futures margins apply
to all market participants, not just a small per­
centage as with stocks.
Generally speaking, as a percentage of
contract-settlement value, futures margins are
smaller than stock margins. However, that
does not necessarily mean that futures margins
provide inadequate protection against default
as compared to stock margins. Ginter (1991)
examined the amount of margin deposit neces­
sary to protect against default on stock index
futures and on the underlying stocks. Because
an index is less volatile than its component
stocks, stock index futures have lower volatili­
ty, all else being equal. Thus, an adequate
prudential margin on an index future could be
lower than on the underlying stocks. Also,
futures contracts are settled at least once a day,
whereas trades in stock are settled only after
five days. That also implies that the margin on
a futures contract does not have to be as large.
Given these two factors, it turns out that some
stocks are margined less adequately than fu­
tures and some more adequately, depending
upon the volatility of the stock. Fenn and
Kupiec (1993) explicitly model the trade-off


between length of settlement interval and mar­
gin adequacy and point out that the ability to
call for emergency settlement significantly
increases the effective protection of the futures
margin system.
In futures markets, there is a direct causal
link between margins and volatility, but it runs
from volatility to margins, not vice versa. Fu­
tures exchanges commonly use a risk-based
margin system in which margins are set high
enough to cover the largest loss experienced by
a position if prices move within a certain
range. The price range is increased when vola­
tility increases or is expected to increase; thus
the margin is a direct function of price volatili­
ty. This causal link is usually referred to as the
prudential exchange hypothesis.5
Is there also a causal link from margin
requirements to volatility? There are two theo­
ries about how such a link might arise. Higher
margins might change the composition of trad­
ers. According to this view, when margin
requirements increase, certain traders are driv­
en out of the market. Without these traders
there is less volatility, either because they were
less risk-averse than average or because they
were less well informed. One of the first stud­
ies of the effect of margins on futures was done
by Hartzmark (1986), who examined how
changing margin requirements would be likely
to affect the composition of traders. He dis­
covered that it was by no means clear which
groups of traders would be driven out by high­
er margins. Thus it is not clear that raising
margins would actually lessen volatility.
Another theory hinges on the effects of
margins on market activity. When margins
increase, the cost of using the market also
increases. If this drives out enough traders, the
depth of the market may be affected; that is,
the market may be unable to absorb large or­
ders without large price increments. Thus,
increasing margins might increase volatility
because any given order flow moves the price
more. These effects might be detected through
a decrease in volume or open interest, even if
the volatility effects are masked.
Many empirical studies of futures margins
focus on effects on volume and open interest as
well as on volatility itself. Hartzmark (1986)
found that volume and open interest dropped
when margins were increased. Fishe and Gold­
berg (1986) and Fishe, Goldberg, Gosnell, and


Sinha (1990) studied a group of Chicago Board
of Trade contracts in the 1970s and 1980s.
Generally speaking, these studies found that
when the margin requirement increased, there
seemed to be a small decrease in open interest
in some of the near-term contracts, but there
were no detectable effects on volatility.
Kupiec (1990) studied the Standard and
Poor’s (S&P) 500 stock index futures contract
during the period 1982 to 1988. There were
only nine changes in the dollar amount of the
margin requirement over that period, but if
margin is expressed as a percentage of the con­
tract value, then the effective margin require­
ment changes daily. According to Kupiec, an
increase in effective margin requirements did
not seem to lead to a decrease in volatility. In
fact, if anything, there seemed to be a short-run
effect in the opposite direction: an increase in
margin requirements increased volatility the
next day, while having no long-run effect.
Moser (1991) studied the relationship be­
tween margin requirements and futures and
cash price volatility in the deutsche mark and
soybean futures contracts. He found that in­
creases in price volatility tended to be followed
by increases in margin requirements. However,
he found no consistent relationship between
increases in margin requirements and subse­
quent volatility.
In a separate study, Moser (1992) tried to
distinguish empirically between the prudential
effect (in which margins increase in anticipation
of higher volatility) and the excess volatility
effect (in which an increase in margin would, in
fact, be causally decreasing excess volatility).
His data supported neither hypothesis. Looking
at the deutsche mark and S&P 500 contracts, he
found that past changes in margins were not
associated with future changes in the standard
deviations of returns. However, surprisingly
enough, changes in volatility did not consistent­
ly lead changes in margin requirements either.
Two studies by Bessembinder and Seguin
(1992, 1993) suggest that when examining the
market impact of regulations, it is helpful to
partition volume and open interest into their
expected and unexpected components. While
these researchers did not study margins directly,
their findings suggest that the impact of regula­
tory changes may differ depending on whether
the researcher is examining expected or unex­
pected changes in market depth, volume, or


open interest. This suggests a potentially fruit­
ful line of research on futures margins.
In short, raising margin requirements
does not appear to mitigate excess volatility in
either the stock or the futures markets. If re­
cent research has highlighted anything, it is
that the perceived gap in size between futures
and stock margins is largely illusory, and that
futures margins are large enough to adequately
protect market participants from contract
Price lim its and v o la tility

Virtually all exchanges are allowed to set
rules to remedy situations in which the integri­
ty, liquidity, or orderly liquidation of contracts
is threatened. In order to enhance the integrity
and long-run liquidity of their market, futures
exchanges have voluntarily chosen to impose
limits on potential price changes during any
given trading session. Such price limits have
been a feature of U.S. markets for some time.
In 1925 the Chicago Board of Trade formal­
ized their use in emergency situations. Over
time, “garden variety” price limits have been
adopted for most commodity futures contracts,
although limits remain less common for the
newer financial futures contracts.
While price limits have been an institu­
tional feature in futures markets for some time,
only recently have they gained front-page
coverage in the financial press. Known as
circuit breakers, price limits have received
renewed attention as a possible shutdown
switch to prevent excessive volatility.6 This
section discusses the traditional rationale for
price limits and then sketches a slightly differ­
ent rationale for the era following the 1987
crash. The recent modification in what we
expect price limits to do may change the way
policy tools work together (in particular, mar­
gins and price limits) and alter the evaluative
procedures that are required to determine the
effectiveness of these particular policies.
Traditionally, price limits have been deter­
mined in advance by an exchange. There is a
limit on the amount of change from the previ­
ous settlement price. If bids and offers match
within the bounds prescribed by the limit, then
trading takes place as usual. If not, trading
stops. But price limits are not a trading halt
per se, since they do not create a timeout from
the trading process. Trading can resume im­
mediately if both buyers and sellers agree to a



price within the limit bounds. The recently
implemented circuit breakers, including the
type now in place on the S&P 500 contract,
require that trading stop for a predetermined
period of time after being triggered by a large
price move.
Rationale fo r price limits

The traditional rationale for the adoption
of limits boils down to two basic concepts:
1) Price limits serve as a policy tool in con­
junction with margin calls to limit default
risk. A price limit establishes the maximum
margin call that could be made during a
given trading session and allows market
participants time to gather the funds to
make good on the margin call. Sometimes
prices may hit their limits for several days
in a row. The slower price adjustment then
allows losers a longer time period in which
to acquire the cash or other marginable
2) Price limits reduce the probability of an
overreaction to news. By not allowing
prices to move beyond a certain point, they
discourage mob psychology and force prices
to adjust slowly. Traditional limits “ex­
pand” on consecutive days to accommodate
the price effects of news over a longer peri­
od of time. Since there may be different
effects on hedgers’ futures and cash posi­
tions, futures contracts typically relax this
limit restriction during the delivery month
so that cash and futures prices can converge.
Since the 1987 crash, proponents of price
limits have stressed the second rationale: to
reduce the probability of an overreaction.
However, the concern today is not merely
about the effects of an overreaction, defined as
a movement in price that overshoots the equi­
librium value and then subsequently returns to
its true value. The concern is also about the
effects of high volatility, that is, unpredictable
rapid movements both up and down. Miller
(1990) refers to this as episodic volatility.
Some of the reasons for this alleged excess
volatility are different now than they were in
the pre-1987 environment. The overreaction
that price limits were supposed to prevent in
the earlier period stemmed from fundamental
news such as crop reports, weather announce­
ments, or changes in federal agricultural policy
supports. In the current environment, volatility


is thought to stem from “noise” traders or cer­
tain types of trading strategies, not necessarily
from fundamental information. Strategies
generating positive feedback trading, most
notably dynamic hedging, are thought to be
responsible for this new type of volatility.
Since the current environment is also charac­
terized by faster execution and information
flows, any effects of these volatility-producing
strategies are going to be felt more quickly.
Thus, the more recent price limit circuit break­
ers look more like price-contingent trading
halts and are meant to provide a cool-down
period during which people can collect their
thoughts. Notice that these limits are not al­
ways connected to margin calls so that their
explicit connection to default risk protection is
no longer clear.
Some analysts, including Miller, argue that
the newer circuit breakers allow clearing firms
to remove insolvent traders, thereby providing
an element of default protection. However,
clearing firms have always had the ability to go
down to the floor and remove insolvent traders.
So it is not clear that circuit breakers offer
anything new in this respect.
Theoretical research

Prior to the post-crash interest in price
limits, very few behavioral models had been
developed to explain the use of price limits.
Perhaps the most widely cited paper was
Brennan’s (1986). In his model, price limits
are used in conjunction with margin to control
default risk. In essence, limits hide the true
price. This may reduce the probability of de­
fault because some individuals who would
have defaulted do not know the extent of their
losses and thus wait until they are more sure of
the price before taking action. Brennan con­
cludes that limits should be more effective in
controlling default risk in markets in which the
cash price is not easily obtained, such as agri­
cultural markets where the cash markets are
less liquid. Conversely, limits should be less
effective for financial markets where cash
markets are well developed. Brennan notes
that almost all financial futures are without
limits, and almost all commodity futures con­
tain limits, generally confirming his model’s
Given that the current debate surrounding
limits seems to be centered in the financial
markets, perhaps we need a new set of models


or other explanations to accommodate them.
The newer set of models focuses on the bene­
fits of price limits and trading halts given the
adverse effects that risk has on the participants
of fast-moving markets.
Greenwald and Stein (1991) use the micro­
structure of the stock market to provide a role
for trading halts. In their model, circuit break­
ers allow individuals to wait and see who else
shows up to trade, and thus help individuals
share what they call transactional risk. Trans­
actional risk arises because not all expected
buyers and sellers come to the market to place
orders when prices are moving quickly. This
model explains stock market behavior better
than futures market behavior but nevertheless
shows that circuit breakers can reduce the
transactional risk present in stock markets.
Kodres and O’Brien (1994) more explicit­
ly examine the role of price limits in volatile
markets. Their analysis develops the circum­
stances under which price limits can improve
the welfare of market participants. They ob­
serve that in volatile markets there is price risk
between the time an individual decides to trade
and the time that the order is actually executed.
Like Greenwald and Stein, Kodres and
O’Brien argue that price limits can be Paretoimproving because they allow risk to be shared
among market participants. While many con­
ditions make some participants better off, fairly
few conditions make at least one person better
off without making anybody else worse off,
that is, the Pareto criterion. In fact, the study
finds that all traders must be hedgers or must
always trade on the same side of the market for
a Pareto improvement to result from imposing
price limits. This means that traders taking
long positions must want to do so at both the
low and high price limits; similarly, traders
taking short positions must also want to do so
at both high and low price limits.
Unlike the previous models, the models of
Greenwald and Stein (1991) and Kodres and
O’Brien (1994) accommodate the newer ratio­
nale for limits: reducing volatility caused by
sudden price moves. Several more recent mod­
els are in their infancy, but they address the
idea of a trading halt in the stock market and
not in derivative markets. Theoretically, then,
price limits can be explained as a response to
default risk or the risks involved in executing
transactions in fast markets.


Empirical evidence

The next important question is, do price
limits perform well either in reducing default
risk or in helping to reduce execution risks and
the attendant volatility? While all of the above
models have broad testable implications, the
unobservability of true prices makes the mod­
els ill-suited for empirical testing. So far, most
of the empirical work has centered on one of
two areas: 1) the effect of limits on price pat­
terns, or 2) econometric problems posed by
using truncated data resulting from the limits.
Khoury and Jones (1984) performed one
of the earliest empirical examinations of the
effects of price limits. They used a sample
period in which no limits were hit and separat­
ed prices into three tiers: those close to the
upper limit, those close to the lower limit, and
those not close to either limit. This construc­
tion permitted prices having unequal temporal
spacing. They calculated time-series correla­
tions for each of their three tiers of data. They
found little difference among the correlation
coefficients and concluded that the price be­
havior around limits was no different than
price behavior between limits. The unequal
temporal spacing of the data implied that the
prices in each range could only partially repre­
sent trades that took place consecutively.
Thus, perhaps it is not surprising that the timeseries correlations within each tier were indis­
While the lack of continuity in prices was
part of the research design, the problem in the
case just described—a nonconsecutive se­
quence of prices—is common to all examina­
tions of price limits. Consider what happens
around a limit. Any time a limit is hit, trades
that would have occurred can no longer do so
and are excluded from the data. As a result,
the data are truncated. Truncation of timeseries data alters the time-series characteristics
of the data. Thus, if we wish to examine
whether prices react differently around a limit,
we have two choices. Either we use the exist­
ing truncated data, or we make “guesstimates”
about what the prices would have been had
there not been a limit. Either approach re­
quires assumptions and/or econometric proce­
dures that could be restrictive and bias the
Ma, Rao, and Sears recently published two
empirical studies using truncated data (1989a,



1989b). The authors used event-study method­
ology to examine the price behavior around
limits, as well as the related volume and vola­
tility. They found that T-bond futures prices
“stabilize” or reverse (in the case of lower
limits) after hitting limits, and that volatility is
lower afterwards. Further, they find high vol­
ume on the day of the limit and the next day,
with volume returning to normal on the second
day following the limit.
We find some of these results inconclu­
sive. The basic problem is that there are no
data associated with the time interval when the
limit is hit. The calendar time for each event
varies depending on the trading lapse; thus the
length of the event depends on when the mar­
ket started trading again. As Kuserk (1990)
points out, this methodology biases the results
in the direction of finding a reversal or flat
prices after the limit. Suppose that a limit was
hit during the day, but at market close the price
is within limits. This means that the price must
have “rebounded” away from the limit (rever­
sal) sometime during the trading day. If the
data set contains intraday limits, all of which
have this characteristic, the results may suggest
that on average, limits are “reflective,” or sta­
bilizing. Again, it is unclear what to do about
the missing “true” prices.
Kodres (1993) and Sutrick (1991, 1993)
make (educated) guesses about the distribution
of unknown “true” prices when a limit is hit.
Kodres focuses on a correct test of the unbi­
asedness property in the foreign exchange
market, taking into account the truncated data.
While not examining the behavior around price
limits directly, Kodres implicitly assumes that
the true distribution of prices is not altered by
the existence of limits. Sutrick attempts to find
unbiased estimates of regression coefficients
and variance using data containing the limited
prices. He also assumes that the underlying
distribution is unchanged. His work, like that
of Kodres, does not focus on the effectiveness
of price limits as a policy tool, but on the
econometric problems encountered when using
limited futures prices.
Future research directions

Some very basic questions remain unan­
swered that future research needs to address:
1) Do price limits change the character of
prices around limits?


2) If price limits change price behavior, do
they do so in a way detrimental to the in­
tegrity of the market? If so, is it because
price limits are too tight or too loose?
3) Do price limits affect liquidity? What
happens to bid/ask spreads immediately
before and after a limit? What happens to
volume? Are there big orders on one side
that are broken up into smaller orders to be
4) Do local traders get out of the market and
let customers trade with other customers?
Do hedgers lose because they cannot estab­
lish positions, and do speculators win? In
particular, who is rationed out of the mar­
ket, and do they subsequently lose money
because of this rationing? No one has yet
examined who is affected by limits. This is
an important issue for establishing policy.
5) Do price limits reduce volatility? If so,
how? If not, why not?
6) Assuming price limits can be useful, what
is the optimal strategy for setting them so
as to obtain the most effective outcome?
7) When should exchanges change limits?
How can they be proactive and anticipate
an optimal time to do so?
8) Should other market structures change to
accommodate price limits or circuit break­
ers? For example, should opening proce­
dures after a limit has been hit be different
than for a regular opening?
9) Do price limits lower default risk? How
many defaults have occurred in markets
without limits versus those with limits,
when other factors are controlled?
Research directions that may help answer
some of these questions include the following:
Theoretically, we need a dynamic model in
order to see how limits affect trading behavior.
For example, how is demand for liquidity and
immediacy affected by limits? Do liquidity
providers stay away? Does the demand for
immediacy change when limits are imminent?
Do prices respond as if there is a magnet effect
or a repelling effect around limits? Further, we
need dynamic models with testable implica­
tions. Currently, the testable implications are
too broad and cannot distinguish among sever­
al of these issues.
Empirically, we need more and better
measures of what happens around price limits.

Specifically, we need to understand better the
type of volatility we are attempting to reduce
with price limits, and we need to construct
statistics that more accurately measure that
type of volatility. In this context, we must
keep in mind that when a limit is hit, there are
no true equilibrium prices to measure what
volatility would have been had the price limit
not been present. Thus, our measures are un­
doubtedly biased in some way.
We need to measure the costs of limits
more carefully. For example, in a limit-bound
market, liquidity is effectively zero. What
happens to the liquidity surrounding the limit?
How is long-run liquidity affected? Are poten­
tial participants more or less likely to use a
market in which limits are present? Exchange
officials and regulators believe that participants
are more likely to use a market with limits.
How do we consider the welfare of the partici­
pants that are locked out of the market during
the limit?
In general, both theoretical and empirical
work in this area should recognize that coordi­
nation among several primary and derivative
markets is being attempted. Therefore, an
evaluation of policy objectives requires an
understanding of how trading takes place in
different markets. For example, current re­
openings after price limits or circuit breakers
are different in the stock market and the futures
market. An evaluation of the effects of limits
must consider these different details and any
ancillary effects they cause. Finally, we need
to examine not only existing policies, but also
better policies as well as other market struc­
tures that can alleviate the problems now being
addressed by price limits or circuit breakers.
Transaction taxes and v o la tility

Transaction taxes are intended to raise the
cost of trading and thus to create a barrier to
entry for certain categories of trading activity.
The goal is to exclude trades that increase price
volatility by more than is warranted by changes
in relevant information. Implicit in this de­
scription is the idea that prices based on rele­
vant information provide appropriate signals as
to where capital investment is most productive.
Investment dollars placed in response to these
signals benefit society by increasing productiv­
ity where it is most highly valued. On the
other hand, trades not based on this informa­
tion might lead to prices that give inappropri­


ate signals; as a result, such trades divert capi­
tal investment from its best use. Black (1986)
refers to trades not based on information as
noise trades. Thus, transaction taxes are in­
tended to create an entry barrier to noise trades,
thereby increasing the informativeness of mar­
ket-determined prices.
A simple one-period model usefully dem­
onstrates how transaction taxes can serve as
entry barriers. Let purepresent the current
price of a stock. At the end of one period, this
stock will pay dividends of if an up state
occurs, and dDif a down state occurs. Since
the point to be made does not require discount­
ing cash flows, we can assume that the expect­
ed payoff for an investment is the expected
dividend minus the price of the stock. Now
consider a market composed of two investor
types: information traders whose dividend
expectations are based on information about
the firm’s prospects, which we denote as
E(d\I)\ and noise traders whose dividend ex­
pectations are not information-based, denoted
E(d\N). In a market comprised of a percent
noise traders and (1- a) percent information
traders, the consensus forecast of returns to
investing in the stock is
7t = (1- a) (E[d\I]-p0) + a(E[d\N] - p 0).
If no new stocks are issued, then the gains
realized by any individual are the losses in­
curred by another, so the sum of profits is zero
(7t=0) and the consensus price of the stock at
time 0 is
p0 = E[d\I] + a(E[d\N]-E[d\I}).
Thus, the stock price is determined on the basis
of the dividend expectations of the information
traders, plus a fraction of the deviation be­
tween the expectations of information and
noise traders. As the percentage of noise trad­
ers increases, the amount of noise impounded
into the stock price rises. The intent of trans­
action taxes is to lessen the noise component of
prices by reducing a.
This exercise highlights some of the as­
sumptions on which the transaction tax propo­
sition rests. First, the percentage of noise trad­
ers must decline as the amount of the transac­
tion tax rises. It is generally accepted that the
number of noise traders will decline when



transaction taxes rise. Note that the after-tax
return realized by noise traders declines as the
amount of tax rises. If the expected return is
not sufficient to meet the tax expense, the trad­
er will not make the investment. So it appears
reasonable to expect a decline in the number of
noise trades when transaction taxes increase.
From the taxing authority’s point of view, the
problem is with the incidence of the tax; that
is, the transaction tax cannot be imposed selec­
tively. The tax will also apply to information
traders who also make their investment deci­
sions on the basis of their expected after-tax
return, so that the number of information trad­
ers can be expected to decline as the amount of
transaction tax increases. Thus, although im­
position of a transaction tax does reduce the
number of noise traders, its impact on the num­
ber of information traders makes its effect on a
unclear. If information traders are more sensi­
tive to this tax than are noise traders, a can rise
when transaction taxes are increased.
A second problem makes predicting the
effect of a transaction tax even more difficult.
In the above reasoning, the members of each
trading group have identical expectations about
the future. While this depiction is unlikely to
be entirely true for either group, the term
“noise” implies dispersion so that these traders
are much less likely to have similar forecasts.
This lack of unanimity has two implications
that bear on the transaction tax proposition.
First, the diverse expectations of this group
imply that the trades of one member of the
group are likely to be offset by those of one or
more other members of the group. This dilutes
the impact any one noise trader can have;
therefore, noise traders as a group have little if
any net impact on prices. Stated differently,
the price impacts of trades from a group of
noise traders probably diversify away. Second,
and perhaps more subtly, the presence of a
trading group with diverse opinions produces a
degree of inertia in prices so that prices do not
change on the arrival of each trade. Price re­
sponses occur only when order arrivals are
recognized as new information. This resis­
tance to price changes helps insure that trades
made for liquidity purposes have little impact
on prices. These markets are said to be liquid,
a feature valued by investors: redemptions of
investments placed in liquid markets are less
likely to realize losses in the event of a sale


forced by cash needs. Absent liquidity ob­
tained by the presence of noise traders, liquidi­
ty is supplied at a price. As the price of liquid­
ity rises, the cost of capital increases. Thus,
transaction taxes that reduce the number of
noise traders can be expected to raise the cost
of obtaining liquidity and the cost of capital.
Kupiec (1991) develops an overlappinggenerations model to analyze transaction taxes.
Like the simple analysis presented above, Ku­
piec finds that the effect of a transaction tax
depends on the relative proportions of certain
trader types; thus its effect cannot be predicted.
Importantly, Kupiec adds a further dimension
to the effects that can be expected from trans­
action taxes. Noise traders are affected as
described above. In addition, the portfolio re­
balancing decisions of all traders are affected.
The effect on volatility depends on this lockin effect. If transaction taxes prevent portfolio
re-balancing based on information, noise trad­
ing becomes relatively more important. Thus,
a useful prediction of the effects of a transac­
tion tax depends on accurate assessments of
the tax’s effects on decisions to purchase and
to sell.
In summary, in order to reduce volatility,
the transaction tax must reduce the proportion
of noise traders without affecting the re-balancing decisions of information traders and
without significantly raising liquidity costs.
Any predictions about the effects of a transac­
tion tax must incorporate each of these influ­
ences. Without an analytical model encom­
passing these influences, empirical evidence is
likely to be the best predictor of the impacts
that can be expected from a transaction tax.
Evidence o f the effect on noise traders

Umlauf (1993) studied the experience
stemming from a Swedish transaction tax im­
posed in 1984. Initially set at 1 percent, the tax
was raised to 2 percent in 1986. Umlauf con­
firmed that trading volume declined following
imposition of the tax, a result previously found
by Lindgren and Westland (1990).7 Umlauf
also found an increase in volatility. However,
as this increase might have been due to the
condition of the Swedish economy, further
investigation is required. As demonstrated
above, the relevance of the decline in trading
activity depends on the extent to which noise
trading was affected. Umlauf showed that
ratios of weekly return variances to daily return


variances declined following imposition of the
tax. This result suggests an increase in fad
trading. Fad trading increases return variances
observed for short holding periods: As fads
dissipate, return variances for longer holding
periods decline. As fads represent a type of
noise trading, this implies that the Swedish tax
increased the proportion of noise trading.
An alternative interpretation of Umlauf s
variance ratio results is that positive feedback
trading increased—that is, buying after a stock
increase or selling after a stock decrease. As
this strategy adds no information to that al­
ready observed in the initial price response, it
is a form of noise trading. The strategy affects
return autocorrelations based on the length of
holding period examined. Autocorrelations of
short holding period returns become more
positive because successive trades reflect the
initial impact of new information on stock
prices. However, because the strategy increas­
es the odds that prices will overshoot their
correct values, it implies negative autocorrela­
tion in longer holding periods. This combina­
tion of effects implies a decline in variance
ratios. Thus, Umlauf s evidence implies that
noise trading increased either in the form of
fad trading or in positive feedback trading.
Evidence o f the effect on liquidity

Umlauf (1993) also investigated volatili­
ties for 11 firms whose shares subsequently
began trading in London while continuing to
trade in Sweden. Return volatility declined as
share classes began trading in London. This
result suggests that the tax increased the pro­
portion of noise trading. As it is unlikely that
traders in London are better informed on the
prospects of Swedish firms than traders in
Sweden, it is likely that the proportion of noise
trades in these stocks increased. Thus, the
reduction in return variance for these stocks is
consistent with improvements in liquidity.
The empirical work of Amihud and Mendelson (1990) demonstrates that stock returns
increase as the spread increases between the
bid and ask prices of stock. Interpreting the
bid-ask spread as the cost of obtaining liquidi­
ty, Amihud and Mendelson support the argu­
ment that higher liquidity costs imply higher
costs of capital. Thus, a transaction tax that
reduces the extent of noise trading is likely to
increase demand for liquidity and drive up its


cost. The resulting impact is likely to be an
increase in the cost of capital.

This article has reviewed evidence bearing
on three approaches that have been proposed to
control price volatility. The effects of margin
rules on volatility are most extensively re­
searched, but the evidence does not generally
support the conclusion that this mechanism can
usefully reduce volatility. Limited evidence
suggests that circuit breakers in the form of price
limits do reduce volatility. Analysis of transac­
tions taxes point to difficulties in implementing
this approach; in addition, the actual effect of
transaction taxes on volatility remains unclear.

Each of these proposed measures has the
potential to cause adverse consequences. Mar­
gin rules may reduce participation in futures
contracting, an effect that may increase volatil­
ity. Price limits may alter price changes as
limits are approached. “Magnet effects,” draw­
ing prices to the limit, might further increase
the speed of price changes and aggravate rather
than alleviate volatility. Under plausible con­
ditions, transaction taxes can increase volatility
rather than lowering it. Policy decisions on
these volatility-control mechanisms should
weigh the possibility of such adverse conse­
quences against the benefits anticipated by
their adoption.

'See Gray (1963).
2See, for instance, Hsieh and Miller (1990), Kupiec
(1989), Salinger (1989), and Schwert (1989a, 1989b).
3See, for instance, Figlewski (1984).
4Telser and Higinbotham (1977).
'See Moser (1992).
6It is important to note the difference between price limits
and circuit breakers. “Circuit breaker” is a broad term

referring to mechanisms by which financial markets can
be temporarily shut down to prevent system overload.
Moser (1990) identifies three types o f circuit breakers,
one o f which he names price limit circuit breakers. Thus,
price limits are only one o f several possible mechanisms
to prevent system overload.
7Ericsson and Lindgren’s (1992) estimates for a cross
section o f 23 markets concluded that a 1 percent reduction
in transaction taxes could be expected to double trading
volume. This magnitude o f effect on trading activity is
comparable to that experienced by the Swedish stock


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B A N K IN G , C R E D IT , A N D F IN A N C E

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Silas Keehn.................................................................................... ..........Jul/Aug


Interest rate shocks and the dollar
Charles L. Evans............................................................................. ..........Sep/Oct



Realignment in the auto supplier industry: The rippling
effects of Big Three restructuring
Paul D. Ballew and Robert H. Schnorbus....................................... ..........Jan/Feb


The impact of lean manufacturing on sourcing
Thomas H. Klier............................................................................. ..........Jul/Aug


Demographic changes, consumption patterns, and
the Midwest economy
Paul D. Ballew and Robert H. Schnorbus....................................... ..........Sep/Oct


To order copies of any of these issues, or to receive a list of other publications,
telephone 312-322-5111, or write to:
Public Information Center
Federal Reserve Bank of Chicago
P.O. Box 834
Chicago, IL 60690-0834




The Federal Reserve
ink of Chicago invites


itructure and Com

the W estin Hotel in


The 31st Annual Confei once
on Bank Structure and Competition
May 10-13, 1995

raise a variety o f public

The major theme of the

of government interven­

conference w ill be the
assessment of recent

tion. Aided by favorable
re g u la to ry decisions/

changes or innovations

banks have gotten more

affecting the financial

deeply involved w ith

The 1995 Conference
w ill focus on these and

policy questions.

related questions. In

These innovations

M Do the recent innovations
represent adaptations that
banks must make in order to
fulfill their traditional role of
processing information and

services sector.

mutual fund activities
by introducing their own

These innovations
p ro p rie ta ry fund s or
have resulted from
through managing or
both market forces and
selling third-party funds.

managing risk in the new
financial environment?

Finally/ as a re su lt of
For example/ use of

papers on

financial issues

associated with community
development; structural change
within the banking industry;
the unique role of commercial
banks in intermediation;
payments system issues; issues


O r are banks in the process

related to international financial

of fundamentally redefining

regulation; and other topics

their role?

related to the structure and


W hat impact w ill these

changes have on the a b ility of

legislative mandate.

addition, we welcome

regulation o f the financial
services industry.

institutions to provide interme­
d ia ry services?

recent legislation/ banks
both exchange-traded

If you would like to present
M Do these changes make us

a paper at the conference,

w ill soon be able to di­

better off as a society?

please submit three copies of

versify geographically in


and over-the-counter
financial derivative

a llow for improved risk man­

a more efficient manner
products has increased
than state law s previ­

agement, do they carry the
potential for increased risk if
used improperly?

dramatically as in stitu ­
ously allowed.
tions have sought more

W h ile the new tools may

the completed paper or an
abstract with your name, ad­
dress, and telephone number,
and those o f any coauthors,

December 21, 1994.

Address correspondence to:


Is government intervention

needed in the development of

Conference on Bank Structure
and Competition

efficient w a ys to man­

secondary loan markets?

age their risk positions.

M Does unchecked geo­

Federal Reserve Bank

graphic expansion carry with

o f Chicago

it the danger o f a significant

2 3 0 South LaSalle Street

Research Department

Secondary m arkets have

Chicago, Illin o is

tion and the exploitation of

developed fo r various

increase in market concentra­

6 0 6 0 4 -1 4 1 3

classes of loans/ in some

market power?

cases w ith the support


Is the recent lib era liza tio n

For additional information,

o f G/ass-Steagall sim p ly the

call Douglas Evanoff

dism antling o f an archaic

at 3 1 2 - 3 2 2 - 5 8 1 4 .

N e w Deal banking regula­
tion, o r does it elim inate a
necessary defense prevent­
ing system ic problem s from
infecting the entire fina nc ia l

P u b lic Info rm a tio n C e n te r

Federal Reserve Bank of Chicago
P.O. Box 834
Chicago, Illinois 60690-0834



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