The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Federal Reserve Bank of Chicago
Backtesting Systemic Risk Measures
During Historical Bank Runs
Christian Brownlees, Ben Chabot, Eric Ghysels,
and Christopher Kurz
July 2015
WP 2015-09
BACKTESTING SYSTEMIC RISK MEASURES DURING
HISTORICAL BANK RUNS*
Christian Brownlees† Ben Chabot‡ Eric Ghysels§ Christopher Kurz¶
July 2015
Abstract
The measurement of systemic risk is at the forefront of economists and policymakers concerns in the
wake of the 2008 financial crisis. What exactly are we measuring and do any of the proposed measures
perform well outside the context of the recent financial crisis? One way to address these questions is to
take backtesting seriously and evaluate how useful the recently proposed measures are when applied to
historical crises. Ideally, one would like to look at the pre‐FDIC era for a broad enough sample of
financial panics to confidently assess the robustness of systemic risk measures but pre‐FDIC era balance
sheet and bank stock price data were heretofore unavailable. We rectify this data shortcoming by
employing a recently collected financial dataset spanning the 60 years before the introduction of
deposit insurance. Our data includes many of the most severe financial panics in US history. Overall we
find CoVaR and SRisk to be remarkably useful in alerting regulators of systemically risky financial
institutions.
∗The views expressed in this article are those of the authors and do not necessarily reflect those of the
Federal Reserve Bank of Chicago or the Board of Governors or Federal Reserve System.
†Department of Economics and Business, Universitat Pompeu Fabra, , Ramon Trias Fargas 25‐27, Office
2‐E10, 08005, Barcelona, Spain, e‐mail: christian.brownlees@upf.edu
‡Financial Economist, Federal Reserve Bank of Chicago, 230 South LaSalle St., Chicago, IL 60604, e‐
mail: Ben.Chabot@chi.frb.org
§Department of Economics and Department of Finance, Kenan‐Flagler School of Business, University of
North Carolina, Chapel Hill, NC 27599, e‐mail: eghysels@unc.edu
¶Senior Economist, Board of Governors of the Federal Reserve System, 20th Street and Constitution
Ave. N.W., Washington, D.C. 20551, e‐mail: christopher.j.kurz@frb.gov
1
Introduction
Effective macro prudential supervision requires the identification and monitoring of systemically risky firms.
Measuring systemic risk has therefore been at the forefront of economists and policymakers concerns in the
wake of the 2008 financial crisis. This agenda prompted the creation of new agencies specifically designed
to analyze and monitor systemic risk (e.g. the OFR in the US, the ESRB in Europe) and has motivated a
large and growing literature devoted to the identification of systemically risky firms. The contributions to
systemic risk measurement are already quite sizable but no consensus best practice/unifying approach has
yet to emerged.1 One reason for the large number of competing risk measures is the lack of financial crisis
data. Ideally, one would discriminate between competing measures by looking at their relative performance
across a broad sample of financial panics but there have been few financial upheavals during the post-WWII
era when financial data is readily available. To confidently assess the robustness of each measure we require
a sufficiently broad sample of financial panics to fully gauge the robustness of competing systemic risk
measures.
Between the founding of the national banking system and the establishment of FDIC insurance, the United
States witnessed many financial panics similar in magnitude to the 2008 crisis. At first glance, the pre-FDIC
era would appear to be an ideal laboratory for an evaluation of systemic risk measures. Alas, bank balance
sheet and stock price data were heretofore unavailable. We rectify this data shortcoming by collecting a
new dataset of bank balance sheets spanning the 60 years before the introduction of deposit insurance.
We combine these new balance sheet data with a dataset containing the price and holding period returns of
banks trading over-the-counter in New York City. Our combined stock and balance sheet panel spans several
financial panics comparable to the 2008 crisis such as the panics of 1873 and 1884, the Barings Crisis of
1890, the subsequent panics of 1893 and 1896, the panic of 1907, and the real estate crash of 1921. These
heretofore unknown data allow us to estimate and evaluate systemic risk measures across a large sample of
financial crises.
The data available in the late 19th and early 20th century is not at par with todays standard practice. As a
consequence, many of the currently used systemic risk measures require data which are not historically
available. Our analysis is therefore confined to two of the popular systemic risk measures namely:
CoVaR (Adrian and Brunnermeier (2011)) and SRisk (Brownlees and Engle (2012))CoVaR (Adrian and
Brunnermeier (2011)) and SRisk (Brownlees and Engle (2012)) which can be computed using data available
in our historical sample period.
Even after limiting our focus to CoVaR and SRisk we still face data limitations. For example, CoVaR and
SRisk are typically computed with daily financial data, whereas our historical balance sheet and bank return
data is collected at a 28-day frequency. Therefore, our paper also involves innovative econometric research
1
For example, (Bisias, Flood, Lo, and Valavanis 2012) summarize thirty-one proposed measures of systemic risk. Brunnermeier
and Oehmke (2012) and Hansen (2013) also survey and the recent literature on systemic risk measures.
2
so as to apply the recently developed methods to the historical data. In particular, we take advantage of
the fact that the DJIA is available daily throughout much of our sample. We employ mixed frequency data
(or MIDAS) techniques to resolve the mismatch of data sampling frequencies of individual bank stocks and
DJIA market returns. A Component MIDAS model paired with a shrinkage estimation approach allows us
to efficiently recover the return dynamics of the banks in the panel. The model is inspired by the recent
mixed frequency volatility models of Ghysels, Santa-Clara, and Valkanov (2005) and Engle, Ghysels, and
Sohn (2013).
Using these historic data, we evaluate the ability of the systemic risk measures to identify risky firms
across a number of financial crises. Overall, pre-crisis measures of CoVaR and SRisk are remarkably
useful in alerting regulators of systemically risky financial institutions. Specifically, financial crises tended
to be preceded by aggregate deposit outflows disproportionately withdrawn from banks with high ex-ante
systemic risk rankings. Moreover, when similarly large aggregate withdrawals occurred by were uniformly
or disproportionately drawn from banks with low ex-ante systemic risk rankings crises did not occur.
Intuitively, a bank is systemically risky if its distress is likely to result in distress in many other banks.
We formalize this intuition by modeling the hazard that a given banks deposit growth or stock return is
below its 5th percentile as a function of other banks deposit growth or stock return being below their 5th
percentile. We allow the hazard to vary with the ex-ante systemic risk ranking of other banks and find a
strong monotonically relationship. When banks with high ex-ante systemic risk rankings suffer a tail event
other banks are far more likely to also suffer a tail event as well.
The rest of the paper is organized as follows. Section 2 describes the historical data, section 3 presents
the market-based measures of systemic risk we employ and the econometric methodology used to estimate
systemic risk using sparse historical data, section 4 contains our preliminary empirical findings, and section
5 concludes.
2
History and Data
Our data is drawn from the reports of the New York Clearing House Association (NYCH) during what is
commonly referred to as the national banking era - the period between the establishment of the national
banking system and the adoption of FDIC insurance. The National Banking Acts (NBA) of 1863 and
1864 reorganized United States banking into a nationwide system of federally chartered banks. The NBA
unified the national currency, established a federal regulator in the Office of the Comptroller of the Currency
and, by providing regulatory incentives to pool excess reserves in central reserve cities, encouraged the
development of a nationwide inter-bank money market centered in New York City. As a result many of the
most systematically important banks in the United States were located in New York and members of the
NYCH.
3
The New York Clearing House was a voluntary self‐regulatory association of New York City banks which
stored specie, facilitated exchange and clearing and monitored the liquidity of member institutions.
National banking era bankers understood that asymmetric information about the health of an individual
clearing house member could transform a run on a single member into a system‐wide panic. The NYCH
therefore attempted to minimize information asymmetries by requiring its member banks to publish
weekly condensed balance sheet statements. These statements which appeared in the Saturday
morning New York Times, Wall Street Journal and Commercial and Financial Chronicle, reported the
average weekly and Friday closing values of each bank’s loans, deposits, excess reserves, specie, legal
tenders, circulation and clearings. NYCH statements were carefully scrutinized by investors and
unexpected changes in leverage or liquidity could set off a stock market rally or decline2.
We use the weekly NYCH balance sheet statements to construct a panel of individual bank balance
sheets sampled every 28 days between January 1866 and December 1925. The sample dates were
chosen to correspond to the sample dates of another hand collected data set containing the stock price
and cash flow information for every bank trading over the counter in New York City. The data was
primarily collected from the New York Times and Wall Street Journal, but formatting changes, omitted
variables, and missing tables necessitated the occasional use of alternative sources. Those include the
Commercial and Financial Chronicle, the Daily Indicator, and Statements from both the Superintendent
of NY State and the Office of the Comptroller of the Currency. In some cases, missing data could not be
located, as the New York Clearinghouse did not publish individual member information during periods of
financial stress. As noted in (Gorton 1985), during banking panics, the clearinghouse organization pooled
liabilities, uniting member banks under the Clearinghouse Committee. During these times the New York
Clearinghouse only published aggregate balance sheet information3.
One hundred and forty‐four individual banks and trusts appear in the NYCH statements between 1866
and 1925. After accounting for mergers and name changes, this number shrinks to 126. A time series
plot of number of banks and trusts can be found in Figure 1. As seen in Figure 1, the New York
Clearinghouse published information on about 60 members in 1865, a number that slowly moves down
to nearly 40 members, including trusts, by the end of our sample4. In the analysis to follow, we focus on
the NYCH Banks with both sufficient balance sheet and stock return data to estimate measures of
systemic risk.
The bank balance sheet information is combined with previously collected stock return information. The
stock data consists of the price, shares outstanding, and dividends of bank stocks trading
2
The New York Stock Exchange was open on Saturdays throughout our period of study.
The periods for which individual balance sheet data was not published include the Panic of 1873 (10/73‐11/73),
the Barings Crisis (12/90‐2/91), the Panic of 1893 (7/93‐10/93), the Panic of 1907 (11/07‐1/08), and at the start of
the First World War (8/14‐11/14). In addition, we were unable to locate the balance sheet for the week ending
April 29, 1892 from any possible source.
4
Trust companies were allowed to join the clearinghouse in 1911.
3
4
over-the-counter in New York City. The stock data was hand collected from the closing quotations published
in the Commercial and Financial Chronicle. The price, share and dividend data allow us to compute the
market value and 28-day holding period return for each bank stock trading between 1866 and 1925. Trusts
and some NYCH member banks which were tightly held did not appear on the stock quotations list. Merging
the balance sheet data to the equity returns data leaves us with a sample of 82 total banks that appear in on
both lists. As shown in Figures 1 and 2, the number of banks tends to trend downward over time as the
average size (in terms of capitalization) increased. Of note, is the jump in capitalization around 1900 after
the passage of the Gold Standard Act of 1900.
3
Systemic Risk Measurement
Systemic risk measurement is a challenging problem and several competing approaches have been put
forward in the literature. As emphasized by Bisias et al. (2012) who provide a thorough survey of systemic
risk measures, it is unlikely that a single measure of systemic risk is able to characterize the dimensionality
and complexity of the entire financial system. In this work, we focus on two market based measures
of systemic risk: the CoVaR of Adrian and Brunnermeier (2011) and the SRISK of Acharya, Pedersen,
Philippon, and Richardson (2010) and Brownlees and Engle (2012). An appealing feature of CoVaR and
SRISK for our analysis is that these measures have only mild data requirements and that we are able to
construct them using our dataset which, because of the historical nature of our analysis, has a number of
constraints.
3.1
CoVaR and SRISK
CoVaR and SRISK associate systemic risk with the shortfall of financial system conditional on the
realization of a systemic event. This is typically justified on the grounds that when financial system is under
severe distress it will stop functioning properly and this in turn will have negative spillover effects on the
real economy. There are important differences between the two approaches, in particular on the definition
of the systemic event. CoVaR conditions on the distress of a single institution while SRISK conditions on
the distress in the entire system.
Before introducing the definitions of CoVaR and SRISK used in this work we need to set appropriate
notation. We are concerned in measuring systemic risk in a panel of n financial firms indexed i = 1, ..., n
over T periods t = 1, ..., T . We denote by ri t the compound return of bank i on period t and by rm t the
corresponding value weighted compound return of the entire financial system over the same period. The
computation of the indices also requires balance sheet information for the financial institutions in the panel.
In what follows we denote by Wi t the market value of equity of firm i, by Di t its book value of debt and by
Ai t = Wi t + Di t the its (market) value of assets.
5
Adrian and Brunnermeier (2011) define the CoVaR of firm i as the Value–at–Risk of the entire financial
system conditional on institution i being distress, that is
q
Pt (rm t < CoVaRp,q
i t |ri t = VaRi t ) = p ,
where the distress of firm i is defined as the return of firm i being at its Value–at–Risk VaRqi t . Adrian
and Brunnermeier (2011) propose to measure the systemic risk contribution of firm i on the basis of the ∆
CoVaR, which is defined as the difference between the CoVaRs of firm i conditional on its returns being at
the Value–at–Risk and at the median, that is,
p,0.50
∆CoVaRadji t = CoVaRadjp,q
.
i t − CoVaRadji t
(3.1)
Importantly, note that we depart here from the Adrian and Brunnermeier (2011) convention and call the
systemic risk measure in (3.1) Adjusted ∆ CoVaR. We use this nomenclature to emphasize that this ∆
CoVaR measure does not take into account the size of the institution into account (hence is adjusted to its
size). We also define a Dollar version of ∆ CoVaR that takes the size of firm i into account, as in Adrian
and Brunnermeier (2011). More precisely we define the Dollar ∆ CoVaR as
∆CoVaRi t = Wi t−1 ∆CoVaRAdji t .
In our application the confidence level p of the CoVaR is set to to 1%.
SRISK (Acharya, Pedersen, Philippon, and Richardson (2010), Brownlees and Engle (2012)) associates
the systemic risk contribution of firm i with its expected capital shortfall conditional on a severe market
downturn. Following Brownlees and Engle (2012), we define the capital buffer of firm i as the difference
between the market value of equity minus a prudential fraction k of the market value of assets, that is
Wi t − kAi t . The parameter k is the prudential capital fraction, that is the percentage of total assets the firm
holds as reserves because of regulation or prudential management. When the capital buffer is negative then
the firm experiences a capital shortfall. Thus, we define the capital shortfall as the negative capital buffer
CSi t = kAi t − Wi t = k(Di t + Wi t ) − Wi t .
Acharya, Pedersen, Philippon, and Richardson (2010) argue that capital shortfalls are systemic when they
occur when the system is in distress. This motivates to measure systemic risk using the conditional
expectation of the capital shortfall conditional on a systemic event. Let the systemic event be {rm t < C}
6
where C denotes the threshold loss for a systemic event. Then the SRISK index is defined as
SRISKi t = Et (CSi t |rm t < C) ,
= k Et (Di t |rm t < C) − (1 − k) Et (Wi t |rm t < C) ,
= k Di t − (1 − k) Wi t (1 + MESi t ) ,
(3.2)
where MESi t is the so called Marginal Expected Shortfall, the expectation of the firm equity return
conditional on the systemic event, that is
MESi t = Et (ri t |rm t < C) .
Notice that the last equality of (3.2) follows from assuming that in the case of a systemic event debt cannot
be renegotiated hence Et (Di t |rm t < C) = Di t . Also, formula in equation (3.2) contains an approximation
error due to the fact that we are using compound rather than arithmetic returns. We also introduce a size
adjusted version of the SRISK index
SRISKAdji t = k LVGi t − (1 − k) MESi t − 1 ,
where LVGi t denotes the leverage ratio (Di t + Wi t )/Wi t . In this work we set the prudential fraction
parameter k to 10% and the systemic loss threshold C to −20%.
It is is also useful to introduce appropriate CoVaR and SRISK aggregates to measure the overall degree of
systemic risk in the financial system. We define aggregate and average CoVaR respectively as
∆CoVaRt =
n
X
∆CoVaRi t
i=1
and
Pn
i=1 ∆CoVaRi t
.
∆CoVaRt = P
n
i=1 Wi t−1
We define Aggregate and Average SRISK anagolously as
SRISKt =
n
X
SRISKi t
i=1
and
Pn
SRISKi t
SRISKt = Pi=1
.
n
i=1 Wi t−1
We define both the aggregate and average systemic risk indices since the financial sector has been evolving
drastically in our sample period. Thus inspecting both the aggregate and average CoVaR or SRISK indices
allows us assess if, for instance, an increase in systemic risk is due to an increase of risk or to the growth of
7
the size of the financial sector.
3.2
Econometric Specification
The computation of CoVaR and SRISK requires to estimate indices synthetizing the dependence between
the market and banks’ returns. To this extent, for each financial institution we introduce a dynamic bivariate
model which allows for time–varying volatility and correlation, that is
"
rt =
rm t
ri t
#
Ft−1 ∼ N (0, Ht )
where Ht denotes time time–varying covariance matrix.
Modeling time–varying covariance matrices is challenging in general - and in particular with the sparse
data we have. In order to remain frugal in terms of parametric specification, we resort to a distributed lag
covariance estimator inspired by MIDAS models - namely use single-parameter Beta polynomial (see e.g.
Ghysels, Sinko, and Valkanov (2007) and ). To be more precise, the covariance at time t is modeled as
Ht =
12
X
0
wj (φ)rt−j rt−j
(3.3)
j=1
where wl (φ) is the MIDAS Beta polynomial weight B(1, φ). It is worth noting that Colacito, Engle, and
Ghysels (2011) show this yields positive semi-definite Ht . In the left panel of Figure 3 we plot the weighting
schemes for various values of φ, ranging from 1 to 6, with the former yielding a flat or equal weighting
scheme. The higher the value of φ the steeper the decline in the weights, i.e. putting more weight on the
most recent observations. In particular we note from the left panel that for φ = 6: lag 1 has weight ≈ 0.5
The MIDAS-type weighting coefficient φ is estimated by maximizing the quasi likelihood function of each
bivariate system. We estimate the optimal φ using a 5–year rolling recursive estimation scheme. The right
panel displays the sample path of estimated φ̂ obtained from 5-year rolling samples of data throughout
history. The shaded vertical lines are the financial crises during our sample. We note that memory is shortlived during financial crisis, since φ̂ peaks around the time of stress in the banking sector.
We use this approach to extract time varying market volatility σm t , bank volatility σi t , and market/bank
correlation ρi t . Note that all time varying moments used in the subsequent analysis are computed using past
information only and therefore do not have any look ahead bias. In Figure 4 we report the estimates using
the covariance specification appearing in equation (3.3), more specifically the individual volatility estimates
and the correlations. The left panel pertains to the volatilities. The shaded area covers the interquartile
cross-sectional range of volatility estimates throughout the sample. The right panel contains the estimated
correlations. Volatility often peaks around bank panics, although not exclusively. For example we observe
8
some interesting volatility behavior around the turn of the 19th and 20th century. These estimates will be
the input to the systemic risk measures defined in the previous subsection.
4
Empirical Findings
The pre-FDIC era provides us with a number of financial panics to investigate the ability of the systemic risk
measures to identify risky banks. We find that most panics are preceded by a deterioration of the balance
sheets of systemically important banks. Specifically, most pre-FDIC panics were preceded by deposit
withdrawals disproportionately drawn from the banks with the highest ex-ante systemic risk rankings. To
illustrate this fact we employ our balance sheet data to construct an entry-corrected index of aggregate
deposits. We use the deposit index to date episodes of major withdrawals from clearinghouse banks and
compute the cross-sectional rank correlations between individual bank deposit growth and ex-ante systemic
risk ranking.
4.1
Deposit Index
We use the balance sheet reports to construct a measure of bank funding stress. The most natural measure
of funding stress is the flow of deposits into and out of New York Clearinghouse banks. Define DepGrowtht
as the percentage change in deposits from time t − 1 to time t.
DepGrowtht =
N Y CHAggregateDepositst
.
N Y CHAggregateDepositst−1
We construct a time series of DepGrowtht sampled every fourth Friday between Jan 1866 and December
1925. The series is corrected for entry and exit by computing the growth rate between time t and t + 1 using
all banks in existence at both dates. The index therefore reflects the change in deposits of surviving banks
and does not mechanically fall when a bank fails and exits the clearing house or mechanically increase when
a new bank is chartered.
The 28-day sampling frequency was selected to correspond with dates for which one of the authors has
previously collected the price, shares outstanding and dividends of New York banks. The stock data was
hand collected from the over-the-counter bid and ask quotations published in the Commercial and Financial
Chronicle.
4.2
The Pre-FDIC Banking Panics
In order to examine the ability of systemic risk measures to predict financial panics we require a consensus
of exactly when financial panics occurred. This may seem like a trivial matter, but pre-FDIC bank deposits
9
were extremely volatile and no consensus list of panics has emerged.5 Although there are a number of
episodes of large deposit withdrawals and financial stress that only a minority code as financial panics each
of these authors agree that major panics occurred in 1873, 1884, 1890, 1893, and 1907. For each of these
consensus panics we ask whether a hypothetical regulator armed with systemic risk rankings would have
been able to detect danger before the panic occurred. In most cases we observe that panics were preceded by
deposit withdrawals concentrated in the banks that ex-ante systemic risk measures deemed most systemic.
4.2.1
The Panic of 1873
The post-Civil War railroad boom went bust in September 1873. In particular, The financial panic of 1873
was set off by the bankruptcy of the bank of Jay Cooke and Company, which was deeply involved in
the financing of the second transcontinental railroad. The panic was preceded by a sharp 11.25 percent
decline in aggregate deposits between our sample dates of Aug 9th and September 6th 1873. Deposits were
disproportionately withdrawn from banks that were the most systemically risky according to Aug 9th CoVar
and Srisk rankings. The 1st and 3rd riskiest banks suffered declines of 20 percent and 24 percent respectively
which rank in the 1st and 2nd percentile of one-month deposit declines in our sample. These banks were
not alone. Many of the riskiest banks ranked suffered disproportionately large declines in deposits. Table
1 reports the behavior of aggregate deposits around the panic of 1873 and the rank correlations between
ex-ante systemic risk rankings and subsequent deposit growth. Large withdrawals from the riskiest banks in
the month before the panic are reflected in the large significant negative correlation between deposit growth
rates before the panic and ex-ante systemic risk rankings.
4.2.2
The Panic of 1884
The panic of 1884 occurred in late May 1884 and was preceeded by another railroad-related downturn.
The panic was preceded by a relatively mild 6.4 percent decline in the aggregate deposit index over the
56-day sample period preceding the panic. But this decline was once again concentrated in banks with the
highest pre-panic systemic risk rankings. While the aggregate deposit index declined a mere 6 percent,
the bank with the riskiest delta CoVar ranking on March 7th 1884 lost 11 percent of its deposits between
March 7th and May 2nd and the 2nd through 4th riskiest banks suffered 28, 22 and 17 percent deposit
declines respectively! Table 2 reports the behavior of aggregate deposits around the panic of 1884 and the
rank correlations between ex-ante systemic risk rankings and subsequent deposit growth. Like the panic of
1873 the rank correlation between deposit growth in the months before the panic and ex-ante systemic risk
rankings are negative and significant. Although few deposits left the NYCH on average the riskiest banks
suffered large deposit outflows in the months before the panic.
5
Kemmerer (1910), Sprague (1910), DeLong and Lawrence (1986), Gorton (1988), Bordo and Wheelock (1988), Wicker (2000),
and Jalil (2015) have each examined the data and attempted to date pre-FDIC banking panics.
10
4.2.3
The Panic of 1890
Unlike the panics of 1873 and 1884 the panic of 1890 was a European panic that spread to the United States
as foreign banks withdrew deposits in the wake of the Barings Crisis in London. The Panic culminated
with the issuance of clearing house certificates in late November 1890. The months preceding the panic
were characterized by slow deposit outflows rather than sharp declines. The aggregate index only declined 5
percent in the 112 days before the panic and the declines were relatively uniform with respect to the ex-ante
delta CoVar and Srisk rankings.
4.2.4
The Panic of 1893
The panic of 1893 was a culmination of the stress introduced into financial markets and the overall economy
by the Barings Crisis a few years earlier. Of note, the Panic of 1893 was by some measures the most severe
panic of the pre-FDIC national banking era. The panic was preceded by a large decline in aggregate clearing
house deposits. The aggregate deposit index declined 15 percent in the 140 days preceding the panic. Like
the panics of 1873 and 1884 this decline was concentrated in the banks with the highest ex-ante systemic
risk rankings. The bank with the riskiest delta CoVar on Feb 3rd 1893 suffered a 25 percent decline in
deposits in the 140 days before the panic while the 4th and 5th riskiest banks each lost about 22 percent
of their deposits. Table 4 reports the behavior of aggregate deposits around the panic of 1893 and the rank
correlations between ex-ante systemic risk rankings and subsequent deposit growth. Across all systemic
risk rankings the rank correlation between pre-panic deposit growth and ex-ante systemic risk ranking is
negative and significant.
4.2.5
The Panic of 1907
The panic of 1907 is unlike any other panic in our sample. After a period of financial stress earlier in the
year, a failed attempt to corner the copper market led to a run on Knickerbocker Trust and an overall crisis
of confidence for the financial trust sector. Although the aggregate deposit index declined 9 percent in the
112 days before the panic there was no negative cross-sectional rank correlation between pre-panic deposit
growth and the ex-ante systemic risk measures. The 6th riskiest bank did suffer a devastating 59 percent
decline in deposits over this period but 8 of the remaining 10 riskiest banks either enjoyed deposit inflows
during this period of suffered less outflow than the aggregate index. Of course this panic was centered in the
shadow banking sector of the era Trust companies. As depositors ran to withdraw money from trusts they
deposited these funds into New York Clearinghouse banks. As a result a regulator using our systemic risk
metrics to monitor deposit flows into clearinghouse banks would have mistakenly thought that the system
was relatively stable.
11
4.3
Dogs That Don0 t Bark and Deposit Declines that Don0 t Result in Panics
The deposit index and systemic risk measures appear to be a useful tools for forecasting the pre-FDIC
consensus financial panics. An historical regulator armed with CoVar and Srisk measures of systemic
risk would have been alerted to 3 of the 5 panics with a simple rule such as beware deposit outflows
disproportionally drawn from the most systemically risky banks. A measure that flashes danger before
60
Nonetheless, some words of caution are in order. Pre-FDIC deposits were notoriously volatile and weve said
nothing about the distribution of deposit withdrawals in the deposit declines that did not lead to panics. If all
(or a majority) of deposit declines are disproportionately drawn from the riskiest banks the advice to beware
deposit outflows disproportionally drawn from the most systemically risky banks is likely to successfully
predict the panics but also deliver a number of false positives. To investigate the utility of the systemic
risk measures we look at the ten largest declines in the deposit index that do not coincide with one of the
consensus panics. Some of these deposit declines are associated with financial stress denoted as a panic
by one or more of the previously cited papers and many are large declines in deposits that nonetheless
uncorrelated with financial stress.
Table 6 reports the dates of major declines in the deposit index, the cross-sectional correlation of deposit
growth rates and ex-ante measures of systemic risk and the proportion of papers that code this episode as
a financial panic. The most striking fact about the deposit declines in Table 6 is their magnitude. The ten
largest declines not associated with a consensus panic ranged from 16.7
What would a hypothetical regulator armed with the systemic risk measures have thought about these large
declines? Would a rule like beware deposit outflows disproportionally drawn from the most systemically
risky banks have triggered false positives? A look at Table 6 tells us that of the six deposit declines that all
authors coded as non-panic only one (July-Oct 1881) exhibited a significantly negative rank correlation
between deposit growth and a systemic risk measure. A regulator who worried about large deposit
withdrawals disproportionally drawn from the systemically risky banks would not have been concerned
with the majority of these withdrawal episodes.
Of the four deposit declines that coincide with a minority of authors coding the decline as a panic each
has a significant negative correlation between deposit growth and at least one of the ex-ante CoVar or Srisk
systemic risk measures. The regulator that worried about disproportional withdrawals from the banks that
rank high ex-ante in CoVar or Srisk would have anticipated a financial panic in each of the episodes that at
least one paper codes as a panic and would have concluded that despite the large deposit outflows there was
no reason to be alarmed in five of the six episodes where every author concludes there was no panic!
12
4.4
Discrete time hazard models of tail events
Intuitively, a bank is systemically risky if its distress is likely to result in distress in many other banks. We
formalize this intuition by modeling the hazard that a given banks deposit growth or stock return is below its
5th percentile as a function of other banks deposit growth or stock return being below their 5th percentile.
A word of caution is in order. Even if our systemic risk rankings contained no useful information about
the relative systemic importance of each bank, it would not be surprising to find that knowledge about a tail
event at one bank predicts simultaneous tail events at other banks. Our sample of banks are all drawn from
the same industry and location and are surely subject to common shocks. On the other hand, if our systemic
risk rankings do carry useful information about the relative systemic importance of each bank we would
expect a tail event at a bank with a relatively high systemic risk ranking to have a disproportionately large
effect on the hazard of tail events at other banks. We adopt a specification that reflects this idea by allowing
the hazard to vary with the ex-ante systemic risk ranking of other banks.
We observe a panel of bank stock returns and deposit growth rates. Define the stock return (deposit growth)
tail dummy as:
• dit = 1 if bank is time t stock return (deposit growth) is below the 5th percentile of observed bank i
stock returns (deposit growth)
• dit = 0 otherwise
We wish to model the hazard that dit = 1. We start with the assumption that the data are generated via a
continuous time process with a proportional hazard
h(t, Xit ) = h0 (t) exp (Xit β)
(4.4)
where h0 (t) = γρt(ρ−1) is a baseline Weibull hazard (where t is the time elapsed since the last tail event)
and Xit are bank-specific covariates which shift the hazard relative to baseline hazard model. The Weibull
specification allows for duration dependence in the baseline hazard. If ρ > 1, the baseline hazard increases
with the time since last tail event; if ρ < 1, the baseline hazard decreases with time since last tail event; and
if ρ = 1, the baseline hazard becomes the exponential model with constant hazard. In all of our specifications
we cannot reject no duration dependence (ρ = 1) and only report the results for the constant baseline hazard
below.
When the data are generated by (4.4), Prentice and Gloeckler (1978) derive the discrete time hazard with
time-varying covariates. The probability that bank i suffers a tail event at time t, denoted Pit :
Pit = 1 − exp {−(h0 (t) exp (Xit β))}
13
(4.5)
Given I banks, the log likelihood is defined as:
Ti
Ti
I X
X
X
Pit
ln L =
)+
ln (1 − Pit )}
{
Dit ln (
1 − Pit
t=1
i=1 t=1
where Ti is the number of time series observations for bank i. We estimate the parameter vector β via MLE.
We adopt a specification that reflects the hypothesis that a tail event in a systemically important bank should
have a disproportionate impact on the hazard that other banks suffer a tail event. Specifically, we estimate
via maximum likelihood with six variables in Xit :
1. # of banks 1-5 in tail = number of banks with top 5 Systemic Risk Measure (SRM) at time t − 1 that
have a tail dummy = 1 at time t
2. # of banks 6-10 in tail = number of banks with a SRM ranked 6 through 10 at time t − 1 that have a a
tail dummy = 1 at time t
3. # of banks 11-15 in tail = number of banks with a SRM ranked 11 through 15 at time t − 1 that have
a tail dummy = 1 at time t
4. # of banks 16+ in tail = number of banks with a SRM ranked 16 or above at time t − 1 that ha a tail
dummy = 1 at time t
5. LVGi t−1 : Leverage Ratio Di t−1 /Wi t−1
6. Liqi t−1 : Liquidity Ratio (legal tender liquid assets relative to deposits for bank i at time t − 1)
The first four variables are counts of banks (other than bank i) that have tail events at time t grouped
according to their systemic risk measure (SRM) at time t - 1. This specification allows the hazard of bank
i to vary with the ex-ante SRM of other banks suffering tail events. If ex-ante systemic risk rankings carry
no information about the relative likelihood that a tail event in one bank effects the hazard of tail events in
other banks we would expect the coefficients on each group of banks to be equal. On the other hand, if tail
events in banks with high relative systemic risk rankings are more likely to cause tail events in other banks
we would expect the hazard model coefficients to monotonically decline as we moved down the systemic
risk groupings in X.
The hazard model coefficients are reported in Table 7. There is considerable evidence that the systemic
risk rankings contain valuable information about the likelihood that a tail event in a given bank is likely
to coincide with tail events in other banks. For both stock returns and deposit growth there is a strong
monotonically decreasing relationship between the fitted hazard and the systemic risk grouping of banks
suffering tail events. Regardless of systemic risk measure, a tail event in a bank with an ex-ante Top 5
or Top 10 systemic risk ranking results in a much larger increase in the hazard than tail events in banks
14
ranked outside the Top 10. In fact, with only one exception (rankings based on SRISKadj), the relationship
between ex‐ante systemic risk rankings and hazard is monotonically decreasing as one moves from the
Top5 to Top6‐10, Top11‐15 and 16+ groupings. The estimated hazard model coefficients are consistent
with the hypothesis that distress at a bank with high ex‐ante systemic risk is likely to result in distress in
other banks.
5 Conclusion
The financial crisis of 2008 inspired a large body of research with the aim of identifying systemically
important financial institutions. Importantly, much of the resultant measures of systemic risk, both
market‐ and fundamental‐based, provide valuable information on the run‐up in risks prior to the recent
financial crisis. That said, a financial crisis occurs roughly every 20 years. And, as a result it is extremely
important to examine the usefulness of measures of systemic risk outside of recent episodes and not wait
for the next period of financial stress to assess the efficacy of these measures. Consequently, we examine
the pre‐FDIC panics of the United States and find both CoVar and Srisk to be remarkably useful in alerting
regulators to financial conditions likely to result in financial crisis. Bank panics of the pre‐FDIC era were
often preceded by a deterioration of bank balance sheets as deposits were withdrawn from the money
center banks that made up the NYCH. When these fleeing deposit were disproportionately withdrawn
from banks that had high ex‐ante CoVar or Srisk rankings, financial panics were likely to follow. On the
other hand, large deposit outflows that were uniformly spread among all clearing house members or
concentrated in banks that had low ex‐ante systemic risk rankings were unlikely to lead to panics. A
hypothetical regulator armed with systemic risk rankings could distinguish between benign deposit
outflows and outflows likely to result in panic by paying careful attention to the systemic risk ranking of
banks suffering the largest withdrawals.
We formalize systemic risk spill‐overs by modeling the hazard that a given banks deposit growth or stock
return is below its 5th percentile as a function of other banks deposit growth or stock return being below
their 5th percentile. The hazard model estimates reveal there is considerable evidence that the systemic
risk rankings contain valuable information about the likelihood that a tail event in a given bank is likely to
coincide with tail events in other banks. The estimated hazard model coefficients are consistent with the
hypothesis that distress at a bank with high ex‐ante systemic risk is likely to result in distress in other
banks.
15
References
ACHARYA , V. V., L. H. P EDERSEN , T. P HILIPPON ,
systemic risk,” Discussion Paper NYU Stern.
AND
M. P. R ICHARDSON (2010): “Measuring
A DRIAN , T., AND M. K. B RUNNERMEIER (2011): “CoVaR,” Discussion Paper Federal Reseve Bank of
New York and Department of Economics, Princeton University.
B ISIAS , D., M. F LOOD , A. W. L O , AND S. VALAVANIS (2012): “A survey of systemic risk analytics,”
Office of Financial Research, Working Paper.
B ORDO , M. D., AND D. C. W HEELOCK (1988): “Price Stability and Financial Stability: The Historical
Record,” Fed of St. Louis Review, Sep/Oct, 41–62.
B ROWNLEES , C., AND R. E NGLE (2012): “Volatility, Correlation and Tails for Systemic Risk
Measurement,” Discussion Paper, Department of Finance, Stern School of Business, New York
University.
B RUNNERMEIER , M. K., AND M. O EHMKE (2012): “Bubbles, financial crises, and systemic risk,”
Discussion paper, National Bureau of Economic Research.
C OLACITO , R., R. F. E NGLE , AND E. G HYSELS (2011): “A component model for dynamic correlations,”
Journal of Econometrics, 164, 45–59.
D E L ONG , B. J., AND H. S. L AWRENCE (1986): “The Changing Cyclical Variability of Economic Activity
in the United States,” in The American Business Cycle: Continuity and Change, ed. by R. J. Gordon, pp.
679–719. Chicago University Press, Chicago.
E NGLE , R. F., E. G HYSELS , AND B. S OHN (2013): “Stock market volatility and macroeconomic
fundamentals,” Review of Economics and Statistics, 95, 776–797.
G HYSELS , E., P. S ANTA -C LARA , AND R. VALKANOV (2005): “There is a risk-return trade-off after all,”
Journal of Financial Economics, 76, 509–548.
G HYSELS , E., A. S INKO , AND R. VALKANOV (2007): “MIDAS regressions: Further results and new
directions,” Econometric Reviews, 26, 53–90.
G ORTON , G. (1985): “Clearinghouses and the origin of central banking in the United States,” Journal of
Economic History, 45, 277–283.
G ORTON , G. (1988): “Banking Panics and Business Cycles,” Oxford Economic Papers, 40, 751–781.
H ANSEN , L. P. (2013): “Challenges in Identifying and Measuring Systemic Risk,” Becker Friedman
Institute for Research in Economics Working Paper, 2012-012.
16
JALIL , A. J. (2015): “A New History of Banking Panics in the United States, 1825–1929: Construction and
Implications,” AEJ Macroeconomics, p. forthcoming.
K EMMERER , E. W. (1910): “Seasonal Variations in the Relative Demand for Money and Capital in the
United States,” in National Monetary Commission, S.Doc.588, 61st Cong., 2d session.
P RENTICE , R. L., AND L. A. G LOECKLER (1978): “Regression analysis of grouped survival data with
application to breast cancer data,” Biometrics, 34, 57–67.
S PRAGUE , O. (1910): “History of Crises Under the National Banking System,” in National Monetary
Commission, S.Doc.538, 61st Cong., 2d session.
W ICKER , E. (2000): Banking Panics of the Golden Age. Cambridge University Press: New York.
17
Figure 1: Count of Banks and Trusts
The blue and red-dashed lines in the figure display the total count of both banks and trusts that were members
of the New York Clearninghouse. Investment trusts entered the sample in 1911, although it can be seen that
one member organization was both a bank and trust prior to 1911.
18
Figure 2: Aggregate Capital
The blue line in the figure is the aggegate nominal captial stock for New York Clearinghouse member banks.
19
Figure 3: Econometric Specification of Conditional Second Moments of Individual Bank and Market returns
The left panel figure shows the B(1, φ) Weighting MIDAS-type weighting for φ= 1, 2, . . . , , 6. The higher φ the higher the importance of
more recent observations, with φ = 1 corresponding to equal weights. The right panel displays the sample path of estimated φ̂ obtained from
5-year rolling samples of data throughout history. The shaded vertical lines are the financial crises during our sample.
20
Figure 4: Estimated volatility and correlation for individual financial institutions
The figure shows the estimates using the covariance specification appearing in equation (3.3), more specifically the individual volatility
estimates and the correlations. The left panel pertains to the volatilities. The shaded area covers the interquartile cross-sectional range of
volatility estimates throughout the sample. The estimation is based on 5-year rolling sample window. The right panel contains the estimated
correlations.
21
Figure 5: Average CoVaR and SRisk
The red and black lines are the average delta CoVar and average SRisk measures as estimated from 1970 to
1925. The red bars reflect financial crises.
22
Table 1: Panic of 1873: Deposits and Systemic Risk: Rankings and Correlations
Panel A reports deposit index (column header Deposit Index) during the panic of 1873 and the reporting of Clearinghouse data (column header CH Data). Panel B reports
the cross-sectional rank correlation between ex-ante systemic risk measures and deposit changes for individual financial institutions. The dates to compute the deposit
growth (column header Dep growth) reports the dates over which the deposit changes are computed, whereas the systemic risk measure date appears in the column with the
header SRM.
Panel A: Deposits in the Banking Sector
Obs #
23
92
93
94
95
96
97
98
99
Date
12-28-72
01-25-73
02-22-73
03-22-73
04-19-73
05-17-73
06-14-73
07-12-73
Deposit
Index
CH
Data
Obs #
100.00
106.75
103.21
98.61
95.67
104.88
108.85
120.35
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
100
101
102
103
104
105
106
107
Date
08-09-73
09-06-73
10-04-73
11-01-73
11-29-73
12-27-73
01-24-74
02-21-74
Deposit
Index
CH
Data
119.66
106.20
93.54
Yes
Yes
no
no
no
Yes
Yes
Yes
95.73
116.86
121.57
Panel B: Cross-sectional Correlations
Dep growth
dates
99
100
99
100
101
101
105
105
SRM
date
99
100
99
100
∆ CoVar
rho p-val
-0.56
-0.50
0.05
-0.02
0.01
0.01
0.82
0.92
∆ CoVaradj
rho p-val
0.06
0.16
-0.15
-0.16
0.78
0.45
0.48
0.44
Srisk
rho p-val
-0.45
-0.46
-0.06
0.02
0.03
0.03
0.80
0.94
Sriskadj
rho p-val
-0.16
0.16
-0.05
-0.10
0.44
0.45
0.83
0.62
Table 2: Panic of 1884: Deposits and Systemic Risk: Rankings and Correlations
Panel A reports deposit index (column header Deposit Index) during the panic of 1884 and the reporting of Clearinghouse data (column header CH Data). Panel B reports
the cross-sectional rank correlation between ex-ante systemic risk measures and deposit changes for individual financial institutions. The dates to compute the deposit
growth (column header Dep growth) reports the dates over which the deposit changes are computed, whereas the systemic risk measure date appears in the column with the
header SRM.
Panel A: Deposits in the Banking Sector
Obs #
24
235
236
237
238
239
Date
12-15-83
01-11-84
02-08-84
03-07-84
04-04-84
Deposit
Index
CH
Data
Obs #
100
105.28
113.10
112.77
107.65
Yes
Yes
Yes
Yes
Yes
240
241
242
243
244
Date
05-02-84
05-30-84
06-27-84
07-25-84
08-22-84
Deposit
Index
CH
Data
105.58
92.15
91.34
98.36
98.64
Yes
Yes
Yes
Yes
Yes
Panel B: Cross-sectional Correlations
Dep growth
dates
237
238
239
238
240
240
240
242
SRM
date
237
238
239
238
∆ CoVar
rho p-val
-0.32
-0.41
-0.29
0.26
0.13
0.05
0.17
0.24
∆ CoVaradj
rho p-val
0.15
0.01
0.02
0.03
0.45
0.95
0.91
0.89
Srisk
rho p-val
-0.26
-0.48
0.43
-0.04
0.20
0.01
0.02
0.83
Sriskadj
rho p-val
0.40
0.11
-0.05
-0.32
0.03
0.57
0.80
0.09
Table 3: Panic of 1890: Deposits and Systemic Risk: Rankings and Correlations
Panel A reports deposit index (column header Deposit Index) during the panic of 1890 and the reporting of Clearinghouse data (column header CH Data). Panel B reports
the cross-sectional rank correlation between ex-ante systemic risk measures and deposit changes for individual financial institutions. The dates to compute the deposit
growth (column header Dep growth) reports the dates over which the deposit changes are computed, whereas the systemic risk measure date appears in the column with the
header SRM.
Panel A: Deposits in the Banking Sector
Obs #
25
321
322
323
324
325
Date
07-25-90
08-22-90
09-19-90
10-17-90
11-14-90
Deposit
Index
100
94.04
95.35
98.67
95.02
CH
Data
Obs #
Yes
Yes
Yes
Yes
Yes
326
327
328
329
330
Date
12-12-90
01-09-91
02-06-91
03-06-91
04-03-91
Deposit
Index
CH
Data
91.32
94.56
100.86
99.92
100.36
No
No
No
Yes
Yes
Panel B: Cross-sectional Correlations
Dep growth
dates
321
324
321
325
325
329
SRM
date
321
324
321
∆ CoVar
rho p-val
-0.10
-0.11
0.49
0.53
0.45
0.00
∆ CoVaradj
rho p-val
-0.20
-0.38
0.01
0.18
0.01
0.92
Srisk
rho p-val
-0.10
-0.15
0.43
0.49
0.31
0.00
Sriskadj
rho p-val
-0.16
-0.33
-0.08
0.28
0.02
0.59
Table 4: Panic of 1893: Deposits and Systemic Risk: Rankings and Correlations
Panel A reports deposit index (column header Deposit Index) during the panic of 1893 and the reporting of Clearinghouse data (column header CH Data). Panel B reports
the cross-sectional rank correlation between ex-ante systemic risk measures and deposit changes for individual financial institutions. The dates to compute the deposit
growth (column header Dep growth) reports the dates over which the deposit changes are computed, whereas the systemic risk measure date appears in the column with the
header SRM.
Panel A: Deposits in the Banking Sector
Obs #
26
352
353
354
355
356
357
358
Date
12-09-92
01-06-93
02-03-93
03-03-93
03-31-93
04-28-93
05-26-93
Deposit
Index
CH
Data
Obs #
100
101.24
109.37
102.02
96.82
95.29
96.69
Yes
Yes
Yes
Yes
Yes
Yes
Yes
359
360
361
362
363
364
Date
06-23-93
07-21-93
08-18-93
09-15-93
10-13-93
11-10-93
Deposit
Index
CH
Data
92.48
90.72
86.03
87.65
95.90
105.88
Yes
No
No
No
No
Yes
Panel B: Cross-sectional Correlations
Dep growth
dates
354
358
359
359
SRM
date
354
358
∆ CoVar
rho p-val
-0.38
-0.07
0.01
0.64
∆ CoVaradj
rho p-val
-0.35
-0.01
0.01
0.91
Srisk
rho p-val
-0.45
-0.10
0.00
0.46
Sriskadj
rho p-val
-0.33
-0.01
0.01
0.94
Table 5: Panic of 1907: Deposits and Systemic Risk: Rankings and Correlations
Panel A reports deposit index (column header Deposit Index) during the panic of 1907 and the reporting of Clearinghouse data (column header CH Data). Panel B reports
the cross-sectional rank correlation between ex-ante systemic risk measures and deposit changes for individual financial institutions. The dates to compute the deposit
growth (column header Dep growth) reports the dates over which the deposit changes are computed, whereas the systemic risk measure date appears in the column with the
header SRM.
Panel A: Deposits in the Banking Sector
Obs #
27
541
542
543
544
545
Date
06-07-07
07-05-07
08-02-07
08-30-07
09-27-07
Deposit
Index
CH
Data
Obs #
100
100.68
98.22
93.52
94.28
Yes
Yes
Yes
Yes
Yes
546
547
548
549
550
Date
Deposit
Index
CH
Data
91.80
96.82
95.00
97.75
101.53
Yes
No
No
No
Yes
10-25-07
11-22-07
12-20-07
01-17-08
02-14-08
Panel B: Cross-sectional Correlations
Dep growth
dates
542
543
545
542
546
546
546
550
SRM
date
542
543
545
542
∆ CoVar
rho p-val
0.33
0.26
0.40
0.44
0.04
0.10
0.01
0.00
∆ CoVaradj
rho p-val
0.05
0.03
0.01
0.07
0.73
0.84
0.97
0.63
Srisk
rho p-val
0.20
0.12
0.34
0.29
0.21
0.43
0.02
0.06
Sriskadj
rho p-val
-0.15
-0.13
-0.01
-0.22
0.31
0.39
0.95
0.14
Table 6: Deposit Declines During Non-Crises Periods
This table reports the dates of major declines in the deposit index, the cross-sectional correlation of deposit growth rates, measures of systemic risk, and the proportion of
academic papers would ascrbe each episode as a financial panic.
28
Start Date
End Date
Deposit Decline
07-16-70
10-08-70
-0.208
09-09-71
11-04-71
-0.167
07-15-71
11-04-71
-0.194
07-13-72
10-05-72
-0.236
07-10-75
12-25-75
-0.208
07-30-81
10-22-81
-0.174
08-16-95
03-27-96
-0.167
04-21-99
12-29-99
-0.196
08-04-05
12-22-05
-0.177
08-06-09
12-24-09
-0.182
corr:
p-val
corr:
p-val
corr:
p-val
corr:
p-val
corr:
p-val
corr:
p-val
corr:
p-val
corr:
p-val
corr:
p-val
corr:
p-val
Covar
Covaradj
Srisk
Sriskadj
Financial Crisis?
0.1374
0.6560
-0.1059
0.6968
-0.1412
0.6015
-0.0085
0.9719
0.2342
0.2484
-0.2652
0.2096
-0.3246
0.0249
-0.1858
0.1912
-0.7047
0.0000
-0.5539
0.0001
0.0714
0.8206
-0.0361
0.8888
0.0175
0.9475
0.1959
0.3805
0.2186
0.2721
-0.1817
0.3531
-0.1612
0.2303
-0.1412
0.3029
-0.366
0.0109
-0.3017
0.0444
0.2967
0.3247
0.0609
0.8114
-0.1476
0.5578
-0.0875
0.6979
0.2985
0.1385
-0.4923
0.0134
-0.4152
0.0024
-0.2796
0.0430
-0.6354
0.0000
-0.5202
0.0004
0.0934
0.7646
0.0526
0.8370
-0.0402
0.8758
0.0570
0.8012
0.1899
0.3413
-0.3842
0.0444
-0.1987
0.1381
-0.2514
0.0643
-0.2034
0.1651
-0.1893
0.2122
NO
% of papers that
date decline as panic
0
NO
0
NO
0
NO
0
NO
0
NO
0
Maybe
14.29
Maybe
42.86
Maybe
28.57
Maybe
16.67
Table 7: Hazard of Individual Bank Extreme Stock Market Decline
This table reports the MLE estimates for the discrete time hazard with time-varying covariates appearing in (4.5). The covariates
Xit are: (a) # of banks 1-5 in tail = number of banks with top 5 Systemic Risk Measure (SRM) at time t − 1 that have a tail dummy
= 1 at time t, (b) # of banks 6-10 in tail = number of banks with a SRM ranked 6 through 10 at time t − 1 that have a a tail dummy
= 1 at time t, (c) # of banks 11-15 in tail = number of banks with a SRM ranked 11 through 15 at time t − 1 that have a tail dummy
= 1 at time t, (d) # of banks 16+ in tail = number of banks with a SRM ranked 16 or above at time t − 1 that ha a tail dummy = 1
at time t, (e) LVGi t−1 : Leverage Ratio Di t−1 /Wi t−1 , (f) Liqi t−1 : Liquidity Ratio (legal tender liquid assets relative to deposits
for bank i at time t − 1)
Covariates
∆CoVaR
∆CoVaRadj
SRISK
SRISKadj
Constant
# of banks 1-5 in tail
# of banks 6-10 in tail
# of banks 11-15 in tail
# of banks 16+ in tail
LVGi t−1
Liqi t−1
Goodness of fit
N
−3.31∗∗∗
0.29∗∗∗
0.22∗∗∗
0.20∗∗∗
0.13∗∗∗
-0.00
-0.34
0.63
21209
−3.33∗∗∗
0.32∗∗∗
0.24∗∗∗
0.21∗∗∗
0.12∗∗∗
-0.00
-0.28
0.63
22597
−3.34∗∗∗
0.25∗∗∗
0.25∗∗∗
0.24∗∗∗
0.11∗∗∗
-0.00
-0.17
0.63
22212
−3.35∗∗∗
0.27∗∗∗
0.19∗∗∗
0.21∗∗∗
0.14∗∗∗
-0.00
-0.25
0.63
22591
Table 8: Hazard of Individual Bank Runs
This table reports the MLE estimates for the discrete time hazard with time-varying covariates appearing in (4.5). The covariates
Xit are: (a) # of banks 1-5 in tail = number of banks with top 5 Systemic Risk Measure (SRM) at time t − 1 that have a tail dummy
= 1 at time t, (b) # of banks 6-10 in tail = number of banks with a SRM ranked 6 through 10 at time t − 1 that have a a tail dummy
= 1 at time t, (c) # of banks 11-15 in tail = number of banks with a SRM ranked 11 through 15 at time t − 1 that have a tail dummy
= 1 at time t, (d) # of banks 16+ in tail = number of banks with a SRM ranked 16 or above at time t − 1 that ha a tail dummy = 1
at time t, (e) LVGi t−1 : Leverage Ratio Di t−1 /Wi t−1 , (f) Liqi t−1 : Liquidity Ratio (legal tender liquid assets relative to deposits
for bank i at time t − 1)
Covariates
∆CoVaR
∆CoVaRadj
SRISK
SRISKadj
Constant
# of banks 1-5 in tail
# of banks 6-10 in tail
# of banks 11-15 in tail
# of banks 16+ in tail
LVGi t−1
Liqi t−1
Goodness of fit
N
−3.35∗∗∗
0.36∗∗∗
0.32∗∗∗
0.28∗∗∗
0.15∗∗∗
−0.11∗∗∗
1.16∗∗∗
0.68
24782
−3.40∗∗∗
0.30∗∗∗
0.28∗∗∗
0.27∗∗∗
0.16∗∗∗
−0.12∗∗∗
1.41∗∗∗
0.68
26427
−3.35∗∗∗
0.36∗∗∗
0.28∗∗∗
0.24∗∗∗
0.15∗∗∗
−0.11∗∗∗
1.24∗∗∗
0.68
25983
−3.38∗∗∗
0.25∗∗∗
0.31∗∗∗
0.23∗∗∗
0.16∗∗∗
−0.12∗∗∗
1.40∗∗∗
0.68
26427
29
Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Examining Macroeconomic Models through the Lens of Asset Pricing
Jaroslav Borovička and Lars Peter Hansen
WP-12-01
The Chicago Fed DSGE Model
Scott A. Brave, Jeffrey R. Campbell, Jonas D.M. Fisher, and Alejandro Justiniano
WP-12-02
Macroeconomic Effects of Federal Reserve Forward Guidance
Jeffrey R. Campbell, Charles L. Evans, Jonas D.M. Fisher, and Alejandro Justiniano
WP-12-03
Modeling Credit Contagion via the Updating of Fragile Beliefs
Luca Benzoni, Pierre Collin-Dufresne, Robert S. Goldstein, and Jean Helwege
WP-12-04
Signaling Effects of Monetary Policy
Leonardo Melosi
WP-12-05
Empirical Research on Sovereign Debt and Default
Michael Tomz and Mark L. J. Wright
WP-12-06
Credit Risk and Disaster Risk
François Gourio
WP-12-07
From the Horse’s Mouth: How do Investor Expectations of Risk and Return
Vary with Economic Conditions?
Gene Amromin and Steven A. Sharpe
WP-12-08
Using Vehicle Taxes To Reduce Carbon Dioxide Emissions Rates of
New Passenger Vehicles: Evidence from France, Germany, and Sweden
Thomas Klier and Joshua Linn
WP-12-09
Spending Responses to State Sales Tax Holidays
Sumit Agarwal and Leslie McGranahan
WP-12-10
Micro Data and Macro Technology
Ezra Oberfield and Devesh Raval
WP-12-11
The Effect of Disability Insurance Receipt on Labor Supply: A Dynamic Analysis
Eric French and Jae Song
WP-12-12
Medicaid Insurance in Old Age
Mariacristina De Nardi, Eric French, and John Bailey Jones
WP-12-13
Fetal Origins and Parental Responses
Douglas Almond and Bhashkar Mazumder
WP-12-14
1
Working Paper Series (continued)
Repos, Fire Sales, and Bankruptcy Policy
Gaetano Antinolfi, Francesca Carapella, Charles Kahn, Antoine Martin,
David Mills, and Ed Nosal
WP-12-15
Speculative Runs on Interest Rate Pegs
The Frictionless Case
Marco Bassetto and Christopher Phelan
WP-12-16
Institutions, the Cost of Capital, and Long-Run Economic Growth:
Evidence from the 19th Century Capital Market
Ron Alquist and Ben Chabot
WP-12-17
Emerging Economies, Trade Policy, and Macroeconomic Shocks
Chad P. Bown and Meredith A. Crowley
WP-12-18
The Urban Density Premium across Establishments
R. Jason Faberman and Matthew Freedman
WP-13-01
Why Do Borrowers Make Mortgage Refinancing Mistakes?
Sumit Agarwal, Richard J. Rosen, and Vincent Yao
WP-13-02
Bank Panics, Government Guarantees, and the Long-Run Size of the Financial Sector:
Evidence from Free-Banking America
Benjamin Chabot and Charles C. Moul
WP-13-03
Fiscal Consequences of Paying Interest on Reserves
Marco Bassetto and Todd Messer
WP-13-04
Properties of the Vacancy Statistic in the Discrete Circle Covering Problem
Gadi Barlevy and H. N. Nagaraja
WP-13-05
Credit Crunches and Credit Allocation in a Model of Entrepreneurship
Marco Bassetto, Marco Cagetti, and Mariacristina De Nardi
WP-13-06
Financial Incentives and Educational Investment:
The Impact of Performance-Based Scholarships on Student Time Use
Lisa Barrow and Cecilia Elena Rouse
WP-13-07
The Global Welfare Impact of China: Trade Integration and Technological Change
Julian di Giovanni, Andrei A. Levchenko, and Jing Zhang
WP-13-08
Structural Change in an Open Economy
Timothy Uy, Kei-Mu Yi, and Jing Zhang
WP-13-09
The Global Labor Market Impact of Emerging Giants: a Quantitative Assessment
Andrei A. Levchenko and Jing Zhang
WP-13-10
2
Working Paper Series (continued)
Size-Dependent Regulations, Firm Size Distribution, and Reallocation
François Gourio and Nicolas Roys
WP-13-11
Modeling the Evolution of Expectations and Uncertainty in General Equilibrium
Francesco Bianchi and Leonardo Melosi
WP-13-12
Rushing into the American Dream? House Prices, the Timing of Homeownership,
and the Adjustment of Consumer Credit
Sumit Agarwal, Luojia Hu, and Xing Huang
WP-13-13
The Earned Income Tax Credit and Food Consumption Patterns
Leslie McGranahan and Diane W. Schanzenbach
WP-13-14
Agglomeration in the European automobile supplier industry
Thomas Klier and Dan McMillen
WP-13-15
Human Capital and Long-Run Labor Income Risk
Luca Benzoni and Olena Chyruk
WP-13-16
The Effects of the Saving and Banking Glut on the U.S. Economy
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti
WP-13-17
A Portfolio-Balance Approach to the Nominal Term Structure
Thomas B. King
WP-13-18
Gross Migration, Housing and Urban Population Dynamics
Morris A. Davis, Jonas D.M. Fisher, and Marcelo Veracierto
WP-13-19
Very Simple Markov-Perfect Industry Dynamics
Jaap H. Abbring, Jeffrey R. Campbell, Jan Tilly, and Nan Yang
WP-13-20
Bubbles and Leverage: A Simple and Unified Approach
Robert Barsky and Theodore Bogusz
WP-13-21
The scarcity value of Treasury collateral:
Repo market effects of security-specific supply and demand factors
Stefania D'Amico, Roger Fan, and Yuriy Kitsul
Gambling for Dollars: Strategic Hedge Fund Manager Investment
Dan Bernhardt and Ed Nosal
Cash-in-the-Market Pricing in a Model with Money and
Over-the-Counter Financial Markets
Fabrizio Mattesini and Ed Nosal
An Interview with Neil Wallace
David Altig and Ed Nosal
WP-13-22
WP-13-23
WP-13-24
WP-13-25
3
Working Paper Series (continued)
Firm Dynamics and the Minimum Wage: A Putty-Clay Approach
Daniel Aaronson, Eric French, and Isaac Sorkin
Policy Intervention in Debt Renegotiation:
Evidence from the Home Affordable Modification Program
Sumit Agarwal, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
Tomasz Piskorski, and Amit Seru
WP-13-26
WP-13-27
The Effects of the Massachusetts Health Reform on Financial Distress
Bhashkar Mazumder and Sarah Miller
WP-14-01
Can Intangible Capital Explain Cyclical Movements in the Labor Wedge?
François Gourio and Leena Rudanko
WP-14-02
Early Public Banks
William Roberds and François R. Velde
WP-14-03
Mandatory Disclosure and Financial Contagion
Fernando Alvarez and Gadi Barlevy
WP-14-04
The Stock of External Sovereign Debt: Can We Take the Data at ‘Face Value’?
Daniel A. Dias, Christine Richmond, and Mark L. J. Wright
WP-14-05
Interpreting the Pari Passu Clause in Sovereign Bond Contracts:
It’s All Hebrew (and Aramaic) to Me
Mark L. J. Wright
WP-14-06
AIG in Hindsight
Robert McDonald and Anna Paulson
WP-14-07
On the Structural Interpretation of the Smets-Wouters “Risk Premium” Shock
Jonas D.M. Fisher
WP-14-08
Human Capital Risk, Contract Enforcement, and the Macroeconomy
Tom Krebs, Moritz Kuhn, and Mark L. J. Wright
WP-14-09
Adverse Selection, Risk Sharing and Business Cycles
Marcelo Veracierto
WP-14-10
Core and ‘Crust’: Consumer Prices and the Term Structure of Interest Rates
Andrea Ajello, Luca Benzoni, and Olena Chyruk
WP-14-11
The Evolution of Comparative Advantage: Measurement and Implications
Andrei A. Levchenko and Jing Zhang
WP-14-12
4
Working Paper Series (continued)
Saving Europe?: The Unpleasant Arithmetic of Fiscal Austerity in Integrated Economies
Enrique G. Mendoza, Linda L. Tesar, and Jing Zhang
WP-14-13
Liquidity Traps and Monetary Policy: Managing a Credit Crunch
Francisco Buera and Juan Pablo Nicolini
WP-14-14
Quantitative Easing in Joseph’s Egypt with Keynesian Producers
Jeffrey R. Campbell
WP-14-15
Constrained Discretion and Central Bank Transparency
Francesco Bianchi and Leonardo Melosi
WP-14-16
Escaping the Great Recession
Francesco Bianchi and Leonardo Melosi
WP-14-17
More on Middlemen: Equilibrium Entry and Efficiency in Intermediated Markets
Ed Nosal, Yuet-Yee Wong, and Randall Wright
WP-14-18
Preventing Bank Runs
David Andolfatto, Ed Nosal, and Bruno Sultanum
WP-14-19
The Impact of Chicago’s Small High School Initiative
Lisa Barrow, Diane Whitmore Schanzenbach, and Amy Claessens
WP-14-20
Credit Supply and the Housing Boom
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti
WP-14-21
The Effect of Vehicle Fuel Economy Standards on Technology Adoption
Thomas Klier and Joshua Linn
WP-14-22
What Drives Bank Funding Spreads?
Thomas B. King and Kurt F. Lewis
WP-14-23
Inflation Uncertainty and Disagreement in Bond Risk Premia
Stefania D’Amico and Athanasios Orphanides
WP-14-24
Access to Refinancing and Mortgage Interest Rates:
HARPing on the Importance of Competition
Gene Amromin and Caitlin Kearns
WP-14-25
Private Takings
Alessandro Marchesiani and Ed Nosal
WP-14-26
Momentum Trading, Return Chasing, and Predictable Crashes
Benjamin Chabot, Eric Ghysels, and Ravi Jagannathan
WP-14-27
Early Life Environment and Racial Inequality in Education and Earnings
in the United States
Kenneth Y. Chay, Jonathan Guryan, and Bhashkar Mazumder
WP-14-28
5
Working Paper Series (continued)
Poor (Wo)man’s Bootstrap
Bo E. Honoré and Luojia Hu
WP-15-01
Revisiting the Role of Home Production in Life-Cycle Labor Supply
R. Jason Faberman
WP-15-02
Risk Management for Monetary Policy Near the Zero Lower Bound
Charles Evans, Jonas Fisher, François Gourio, and Spencer Krane
WP-15-03
Estimating the Intergenerational Elasticity and Rank Association in the US:
Overcoming the Current Limitations of Tax Data
Bhashkar Mazumder
WP-15-04
External and Public Debt Crises
Cristina Arellano, Andrew Atkeson, and Mark Wright
WP-15-05
The Value and Risk of Human Capital
Luca Benzoni and Olena Chyruk
WP-15-06
Simpler Bootstrap Estimation of the Asymptotic Variance of U-statistic Based Estimators
Bo E. Honoré and Luojia Hu
WP-15-07
Bad Investments and Missed Opportunities?
Postwar Capital Flows to Asia and Latin America
Lee E. Ohanian, Paulina Restrepo-Echavarria, and Mark L. J. Wright
Backtesting Systemic Risk Measures During Historical Bank Runs
Christian Brownlees, Ben Chabot, Eric Ghysels, and Christopher Kurz
WP-15-08
WP-15-09
6
@FedFRASER