Fisher College of Business
Working Paper Series
Charles A. Dice Center for
Research in Financial Economics
Liquidity Shocks and Hedge Fund Contagion
Nicole M. Boyson, Northeastern University
Christof W. Stahel, George Mason University and the FDIC
René M. Stulz, Department of Finance, The Ohio State University,
NBER, and ECGI
Dice Center WP 2011-12
Fisher College of Business WP 2011-03-012
Revision Date: September 2011
Original Date: June 2011
This paper can be downloaded without charge from:
http://www.ssrn.com/abstract=1868569
An index to the working paper in the Fisher College of
Business Working Paper Series is located at:
http://www.ssrn.com/link/Fisher-College-of-Business.html
fisher.osu.edu
Liquidity Shocks and Hedge Fund Contagion
August 25, 2011
NICOLE M. BOYSON, CHRISTOF W. STAHEL, and RENÉ M. STULZ*
ABSTRACT
In Boyson, Stahel, and Stulz (2010), we investigate whether hedge funds experience worst return
contagion – that is, correlations in extremely poor returns that are over and above those expected from
economic fundamentals. We find strong evidence of contagion among hedge funds using eight separate
style indices for the period from January 1990 to October 2008: the probability of a worst return in a
particular index is increasing in the number of other indices that also have extremely poor returns. We
then show that large adverse shocks to asset and funding liquidity strongly increase the likelihood of this
contagion. In this paper, we further investigate contagion between hedge funds and main markets. We
uncover strong evidence of contagion between hedge funds and small-cap, mid-cap and emerging market
equity indices, high yield bonds, emerging market bonds, and the Australian Dollar. Finally, we show that
this contagion between hedge funds and markets is also significantly linked to liquidity shocks, especially
for small-cap domestic equities, Asian equities, high yield bonds, and the Australian Dollar.
*
Boyson is at Northeastern University, Stahel is at George Mason University and the FDIC, and Stulz is at The Ohio State
University, NBER, and ECGI.
Electronic copy available at: http://ssrn.com/abstract=1868569
The recent financial crisis has raised concerns among investors, policymakers, and world leaders
about possible performance linkages across markets. For example, in an article regarding debt markets in
Europe, Professor Nouriel Roubini of NYU stated that “[t]here’s financial contagion in Portugal, Spain,
and to a smaller degree even countries like Italy, Belgium, and others in the Eurozone.”1 Generally,
contagion means that a shock in one economy or one market spreads to other economies and markets in
ways that cannot be explained by fundamentals. For instance, an event of contagion is one where an
adverse shock in the stock market in one country leads to an adverse shock in another country’s stock
market that cannot be explained by changes in fundamental determinants of stock prices. While
identifying the existence of and transmission mechanism for contagion can be challenging, a better
understanding of how and why contagion propagates, which markets are affected, and its severity is
important for investors, policy makers, and scholars. Academics have conducted studies of contagion in
international markets (see for example, Bae, Karolyi, and Stulz (2003) and Bekaert, Harvey, and Ng
(2005)) as well as in financial institutions and hedge funds (see, for example, Adrian and Brunnermeier
(2008), Cifuentes, Ferrucci, and Shin (2005), and Dudley and Nimalendran (2011)). Hedge funds are
particularly relevant in terms of contagion, since managers often tout their funds as having low
correlations with broad markets, which should provide downside protection for investors in times of a
crisis. In this light, a number of hedge fund managers attempt to be “market neutral” or have a beta of
zero, while focusing on absolute rather than relative performance. If hedge funds are affected by
contagion during crises, funds that are market neutral outside of crises periods are unlikely to be so during
crises periods, and the diversification provided by hedge funds outside of crises overstates the extent to
which hedge funds provide diversification to portfolios when a crisis hits.
In Boyson, Stahel, and Stulz (2010), we show that hedge funds experience contagion in worst returns
and that this contagion is linked to asset and funding liquidity shocks. We define contagion as in Harvey,
1
See Culpan, Tim, December 1, 2010. “Roubini Sees European Financial Contagion Spreading Into Portugal,
Spain,” at bloomberg.com.
1
Electronic copy available at: http://ssrn.com/abstract=1868569
Bekaert, and Ng (2005) as “correlation over and above what one would expect from economic
fundamentals.” A “worst return” is a return belonging to the bottom 10th percentile of all returns for the
entire time-series of returns under study (January 1990 to October 2008). We collect returns for this time
frame for eight different single style hedge fund indices provided by Hedge Fund Research (HFR). The
styles included are Convertible Arbitrage, Distressed Securities, Event Driven, Equity Hedge, Equity
Market Neutral, Global Macro, Merger Arbitrage, and Relative Value Arbitrage.
Using quantile and multinomial logit regressions, and controlling for a number of common factors in
hedge fund performance, we show that the probability of a single style hedge fund experiencing a worst
return is increasing in the number of worst returns experienced by the other seven hedge fund styles. The
phenomenon we uncover is economically important: for example, the quantile regression approach shows
that the probability that the Event Driven hedge fund index experiences a worst return is 55% when the
equally-weighted index of all other hedge fund styles also experiences a worst return, compared to an
unconditional probability of 10% if there were no dependence among hedge fund style returns. We also
show that this dependence among returns increases dramatically when returns are low (below the 50th
percentile), but there is no corresponding increase when returns are high (see Figure 1 in Boyson, Stahel,
Stulz (2010)). In other tests, we find that when seven of the hedge fund style indices experience worst
returns, the probability that the eighth style index will also experience a worst return is greater than 70%,
compared to an unconditional probability of about 6%.
After documenting the existence of contagion among hedge funds, we investigate possible
explanations for this contagion. For theoretical motivation, we turn to a recent paper by Brunnermeier and
Pedersen (2009). In their model, an adverse shock to speculators’ funding liquidity (the availability of
funding) forces them to reduce their leverage and provide less liquidity to the markets, which decreases
asset liquidity (the ease with which assets trade). When the impact of the funding liquidity shock on asset
liquidity is strong enough, asset values become depressed, which leads to further tightening of funding
liquidity for speculators and causes a self-reinforcing liquidity spiral in which funding and asset liquidity
2
Electronic copy available at: http://ssrn.com/abstract=1868569
and asset values continue to deteriorate across various asset classes. While the predictions of their model
may be most significant during a severe financial crisis like the credit crunch that began in 2007, we study
periods of coincident poor performance in hedge funds since 1990 and show that large adverse shocks to
asset and hedge fund funding liquidity make hedge fund contagion more likely. Specifically, we show
that shocks to credit spreads, the TED spread, stock market liquidity, bank and prime broker stock
performance, and hedge fund flows are positively correlated with the existence of hedge fund contagion.
In this paper, we build on our prior analysis to examine contagion between hedge funds and main
financial markets. Such an investigation is important in helping understand which hedge fund strategies
are most affected by poor performance in the main markets. We conduct this examination using 10 hedge
fund indices instead of 8 and consider three broad-based main market indices for equities, bonds, and
currencies, and 15 sub-indices: 7 for equities, 3 for bonds, and 5 for currencies. Importantly, we link the
existence of contagion between hedge funds and main markets to liquidity shocks.
For the main market-hedge fund contagion analysis, we find strong evidence of contagion between
hedge funds and a number of main markets including the broad-based equity index and the sub-indices for
small-cap equities, mid-cap equities, Asian stocks, emerging market equities, high yield bonds, emerging
market bonds, and the Australian Dollar. In addition, we find that for six markets, there is strong evidence
linking the main market-hedge fund contagion to liquidity shocks. Using the expanded set of hedge fund
indices, we also find evidence of contagion among hedge funds, a result that is strongly consistent with
our previous work. Also consistent with our prior work, we find evidence of a significant linkage between
liquidity shocks and contagion among hedge funds.
Taking the current results together with our prior results, we have strong and comprehensive evidence
for the existence of contagion not just within the hedge fund universe, but between hedge funds and some
components of the main financial markets as well. Further, liquidity shocks appear to exacerbate both
main market-hedge fund contagion as well as contagion among hedge funds.
3
The remainder of the paper is organized as follows. Section I describes the data. Section II documents
the existence of contagion among hedge funds. Section III documents the existence of contagion between
hedge funds and main markets. Section IV links liquidity shocks to both types of contagion, and Section
V concludes.
I. Data
In this section, we first introduce our hedge fund data and then our data on the returns of the main
financial markets.
A. Hedge Fund Data
The hedge fund style returns we use are the monthly style index returns provided by Hedge Fund
Research (HFR). The returns are equally weighted and net of fees. The indices include both domestic and
offshore funds. These data extend from January 1990 to October 2008 for a total of 226 monthly
observations. We use ten single-strategy indices (Event Driven Distressed/Restructuring, Event Driven
Merger Arbitrage, Equity Hedge Market Neutral, Equity Hedge Quantitative Directional, Equity Hedge
Short Bias, Emerging Markets, Macro Systematic Diversified, Relative Value Fixed Income Convertible
Arbitrage, Relative Value Fixed Income Corporate, and Relative Value Multi-Strategy). These indices
include over 2,000 funds and exclude funds of funds. Since the publication of Boyson, Stahel, and Stulz
(2010), HFR has reclassified and expanded their eight previous indices. They explain the reclassification
as follows on their website: “The classifications reflect the evolution of strategic trends in the hedge fund
industry, cognizant of the reality that over market cycles the classification system is likely to continue to
evolve, as new opportunities attract investor capital. The objective of the system is to define pure strategy
and sub-strategy buckets which can be used to characterize pure strategy return at each level of analysis,
4
to be used for purposes of quantitative index construction.”2 HFR did not restate the performance of any
past index, but rather, merged the eight existing indices into the new indices, according to their new
classification system.
As with the prior eight indices, the new indices include all the funds in the HFR database, with no
required minimum track record or asset size. Additionally, these indices are not directly investible; that is,
they include some funds that are closed to new investors. To address backfilling and survivorship bias,
when a fund is added to an index, the index is not recomputed with past returns of the new fund.
Similarly, when a fund is dropped from an index, past returns of the index are left unchanged. While the
new classification system includes more than 10 new indices, we chose the 10 indices we use in this paper
based on the following criteria: first, all eight of the indices in the Boyson, Stahel, and Stulz (2010) paper
must be included in the reclassified indices we use, and second, the index data must extend back to
January 1990 so as to be comparable to the Boyson, Stahel, and Stulz (2010) paper.
Panel A of Table I reports summary statistics for the hedge fund indices. The Equity Hedge
Quantitative Directional (EHQD) index has the highest rate of return at about 1.15% per month, closely
followed by the Emerging Market Index (EMKT) with a return of 1.14% per month. The Equity Hedge
Short Bias Index (EHSB) has both the lowest monthly rate of return at about 40 basis points per month as
well as the highest standard deviation of 5.68% per month. Given the bull market during most of the
1990s and much of the 2000s, the poor performance of the short bias index is not surprising. Correlations
among all the indices are generally positive and statistically significant, consistent with the results of
Boyson, Stahel, and Stulz (2010). The one exception is the Equity Hedge Short Bias index which, as
expected, exhibits a negative and statistically significant correlation with the majority of the other indices.
2
See https://www.hedgefundresearch.com/index.php?fuse=indices_class&1295886859 for more information
regarding the new classification system and for detailed descriptions of the new indices.
5
B. Main Markets Index Data
We also gather data for a number of market indices. We use three main market factors: a broad-based
equity market index (the Russell 3000), a broad-based bond index (the Lehman Brothers bond index), and
a broad-based currency index (the change in the trade-weighted U.S. dollar exchange rate index published
by the Board of Governors of the U.S. Federal Reserve System). In addition, we separately investigate
sub-indices of the main markets. These include seven equity market sub-indices: the Russell 2000 for
small-cap domestic stocks, the Wilshire U.S. mid-cap index for mid-cap domestic stocks, the S&P 500
index for large-cap domestic stocks, the Datastream total return Asia index, the Datastream total return
Europe index, the MSCI Emerging Markets index, and the MSCI Emerging Markets Latin America Index.
For fixed-income, there are 3 sub-indices: the Datastream investment grade corporate bond index, the
Bank of America/Merrill Lynch high yield bond index, and the JP Morgan emerging markets bond index.
Finally, for currencies, we use exchange rate data from the Board of Governors of the Federal Reserve
System for the Australian dollar (AUS), the Swiss franc (SFR), the Euro (EUR), the British Pound (PD),
and the Japanese Yen (YEN). All exchange rates are defined as the dollar price of the foreign currencies.3
Panel B of Table I reports summary statistics for the main markets indices and sub-indices. Here, the
best performers are investment grade bonds (IGR) and Latin American equities (LAT) with monthly return
of about 1.36% for each. Both these indices also have rather high standard deviations. The worst
performer is the US Dollar Index (DLR), with a return of just three basis points per month. Correlations
between the indices seem reasonable, with fairly high correlations among the equity indices, and low
correlations between investment grade bonds (IGR) and most other indices. The US Dollar Index is, as
expected, significantly correlated with the individual exchange rates, which in turn are also correlated
among themselves. The Australian Dollar is positively correlated with the Russell 3000 and all the stock
market subindices. The Asian and European stock markets are each positively correlated with four out of
3
We convert the published rates from the Board of Governors for the Swiss franc and the Japanese yen. Also, we
use the ECU as the European currency before inception of the Euro.
6
five currencies. The Swiss franc, Euro and British Pound are all correlated with the investment grade
bond index (IGR), and the Australian Dollar exhibits a positive correlation with both high yield and
emerging bond market indices. Interestingly, the Japanese Yen is negatively correlated with these indices.
II. Tests for Contagion Among Hedge Funds
We first examine whether there is contagion among hedge fund returns. As a first look, Figure 1 plots
the number of hedge fund indices (of a possible 10) that experience worst returns in a given month for the
period January 1990 to October 2008. The periods with the most simultaneous extreme returns include the
period around the Long-Term Capital Management crisis as well as the recent financial crisis. In the
following, we use a lower 10% cutoff of the overall distribution of returns to identify worst returns among
hedge fund styles (i.e., returns that are in the bottom decile of all returns for an index’s entire time series).
Since there are 226 observations, we have 23 worst returns for each style. The analyses use a linear
probability model with regressions performed separately for each index, in which the dependent variable
is set to 1 if the hedge fund index of interest has a worst return, and zero otherwise.4 The independent
variables include a number of control variables that have been shown to affect hedge fund performance,
including the returns on the three broad-based main markets (stocks, bonds, and currencies), the monthly
change in the 10-year constant maturity Treasury yield, based on Fama and French (1993) and Fung and
Hsieh (2004), and the return on the Treasury Bill. We also use ABS (asset-based strategy) factors and a
size spread factor (Wilshire Small Cap 1750 monthly return minus Wilshire Large Cap 750 monthly
return), both from Fung and Hsieh (2004).5 The ABS factors are look-back straddles on bonds, currencies,
commodities, short-term interest rates, and equities. We use changes in the VIX index to capture stock
market volatility. Finally, we include a variable that represents the equally-weighted index of all hedge
4
Using more sophisticated regression approaches like binomial and multinomial logit models and explicitly
controlling for autocorrelation and non-normality in returns generate qualitatively similar results. See Boyson,
Stahel, and Stulz (2010).
5
We thank David Hsieh for making this data available.
7
fund returns excluding the returns on the hedge fund index in the dependent variable. This factor is
included to control for the overall correlations among hedge fund indices.
The independent variable of interest is called COUNT9 which is the number of other hedge fund
indices (excluding the index represented by the dependent variable) that have an extreme worst return in a
month. A positive and significant coefficient on COUNT9 provides evidence of contagion between the
hedge fund index of interest and all other hedge fund indices. Table II presents results. The most striking
result is that all ten indices exhibit evidence of hedge fund contagion as the coefficients on COUNT9 are
positive and statistically significant at the 5% or better level. This result is strongly consistent with the
results in Boyson, Stahel, and Stulz (2010), and provides additional evidence of hedge fund contagion,
given the larger number of hedge fund indices examined in this paper. The estimates are also
economically significant: a one standard deviation increase in COUNT9 increases the probability of
observing an extreme negative return by about 12%.
III. Tests for Contagion Between Hedge Funds and Main Markets
We next examine whether there is contagion between hedge fund returns and returns on the main
markets. As in the prior section, we use a lower 10% cutoff of the overall distribution of returns to
identify worst returns among hedge funds and main market indices. In addition to the control variables
described above, the independent variables also include indicator variables for each of the market indices,
set to 1 if the index has a worst return, and zero otherwise. For each of the 10 hedge fund indices, we
perform four different regression specifications for a total of 40 separate analyses. The first specification
includes indicator variables for worst returns in the three broad-based main markets. The second includes
indicator variables for each of the equity market sub-indices, excluding the Latin American index since it
is subsumed by the Emerging Markets index, as well as indicators for the broad-based bond and currency
indices. The third includes indicator variables for each of the bond sub-indices, as well as indicators for
8
the broad-based equity and currency indices, and the final set of regressions includes indicators for each
of the currency sub-indices, as well as indicators for the broad-based equity and bond indices.
Rather than tabulating 40 separate regressions, we perform four summary regressions that aggregate
the above analyses. We report the summarized results in Table III, while describing the detailed results in
the exposition. Each summary regression is a pooled time-series, cross-sectional regression across all ten
hedge fund indices over the entire time period. We include fixed effects to provide a separate intercept for
each of the hedge fund indices that controls for within-style variation for each hedge fund index. A
positive and statistically significant coefficient on a main market indicator variable implies that there is
contagion, on average, between hedge funds and the main market of interest.
The first regression specification in Table III includes all the control variables, indicator variables for
the three broad-based main markets, and the equally-weighted return on all hedge fund indices to control
for normal correlations among indices. For brevity, the control variables and individual intercepts on each
of the hedge fund indices are not reported. In Specification 1, there is strong evidence of contagion
between the stock market index and hedge funds. An examination of the 10 detailed regressions for each
hedge fund index (results not tabulated) indicates that 7 of the 10 separate indices (Event Driven Merger
Arbitrage, Quantitative Directional, Short Bias, Emerging Markets, Macro Systematic Diversified Index,
Relative Value Fixed Income Corporate, and Relative Value Multi-Strategy) have positive and significant
coefficients on the Russell 3000 indicator variable.
Turning to Specification 2, this analysis includes the six equity sub-indices (which exclude the Latin
American index as it is subsumed by the Emerging Markets index) as independent variables. Four of
these indices: Small-Cap Domestic, Mid-Cap Domestic, Asian Stock, and Emerging Markets show
evidence of contagion with hedge funds. Examining the 10 detailed regressions for this specification,
there is evidence of contagion between 4 of the 10 hedge fund indices (Event Driven Merger Arbitrage,
Relative Value Fixed Income Corporate, Relative Value Convertible Arbitrage, and Relative Value MultiStrategy) and the small cap stock market index. Four of the 10 indices (Event Driven Distressed
9
Securities, Event Driven Merger Arbitrage, Quantitative Directional, and Macro Systematic Diversified)
show evidence of mid-cap stock market contagion with hedge funds. By contrast, 2 of the 10 indices
(Event Driven Merger Arbitrage and Quantitative Directional) show evidence of contagion between large
cap stocks and hedge funds, while 2 of the 10 indices (Event Driven Distressed Securities and Relative
Value Multi-Strategy) show a negative relationship between poor performance in the index and poor
performance in large cap stocks, suggesting that these two indices may actually provide downside
protection against extreme moves in large cap stocks for their investors. Two of the 10 indices (Relative
Value Fixed Income Corporate and Relative Value Convertible Arbitrage) have contagion with the Asian
stock index, and finally, only the Emerging Markets index shows evidence of emerging markets
contagion with hedge funds. Taken together, we present fairly strong evidence of contagion between
hedge funds and small and mid-cap stock markets, weaker evidence of contagion with Asian stock
markets, and mixed evidence for contagion with large cap stocks: some indices show contagion but even
more indices indicate weak evidence of downside protection. These results are consistent with academic
work showing that hedge funds have significant exposure to small stocks (see, for example, Agarwal and
Naik (2004), and Billio, Getmanksy, and Pellizzon (2010)).
Specification 3 provides strong evidence of contagion between hedge funds and high yield bonds and
weaker evidence of contagion between hedge funds and emerging market bonds. Examining the detailed
regressions, 5 of the 10 indices (Event Driven Distressed Securities, Event Driven Merger Arbitrage,
Relative Value Fixed Income Convertible Arbitrage, Relative Value Fixed Income Corporate, and
Relative Value Multi-Strategy) provide evidence of contagion with high yield bonds. These results are
intuitive in that 2 of the 5 hedge fund indices have a fixed-income focus, and a third has a distressed focus.
Finally, only one index, Emerging Markets, shows contagion with the Emerging Markets bond index.
Specification 4 provides evidence of contagion between hedge funds and the Australian Dollar.
Specifically, the Emerging Market and the Equity Hedge Quantitative Directional indices exhibit
significant positive exposure. One possible explanation for this contagion is that it may reflect the
10
unwinding of carry trades by hedge funds during poor economic times. There is also weak evidence of
downside protection being provided by holding Japanese Yen in the aggregate regressions, but none of
the 10 separate index regressions have a statistically significant result for the Japanese Yen.
Given that these regressions are linear probability models and the explanatory variables are indicators,
the estimated coefficients can directly be interpreted as increases in the average probability of observing
an extreme negative return. Focusing on the largest significant coefficient in Specification 2, the estimate
suggests that if the Dow Jones Mid-Cap Equity Index has an extreme return, the probability of
experiencing an extreme return in the 10 hedge fund indices increases on average by 14.4%. Similarly for
specifications 3 and 4, the results suggest that the probabilities increase on average by 18.8% and 4.0%
with a large negative return in the BOA/ML Corporate High Yield Index and the Australian Dollar,
respectively.
In summary, the results from this section provide significant evidence of contagion between main
markets and hedge funds, particular with respect to the broad-based equity index, the broad-based
currency index, small and mid-cap stock markets, high yield bond markets, and the Australian Dollar.
IV. Channels of Hedge Fund Contagion
In this section, we explore the channels through which contagion takes place. In the first part of the
section, we consider the channels of contagion among hedge funds. In the second part, we investigate the
channels of contagion between hedge funds and the main markets.
A. Contagion across Hedge Funds
We are interested in identifying factors that explain the contagion we observe. Brunnermeier and
Pedersen (2009) provide a theoretical framework from which to conduct our investigation. They argue
that speculators, notably hedge funds, help smooth price fluctuations in markets that are caused by order
imbalances across buyers and sellers by providing liquidity. In order to provide liquidity, speculators
11
must finance their trades through collateralized borrowing from financiers, including commercial and
investment banks. As a result, speculators can face funding liquidity constraints, either through higher
margins or a decline in the value of the assets they hold, or both. For example, in the case of a liquidity
shock, a financier may raise its margin requirements, forcing the speculator to delever in a time of crisis,
reducing prices and market liquidity even further (a margin liquidity spiral).6 Concurrently, any large
positions that the speculator holds will lose value, which can lead to further margin calls at the now
higher margin rate, causing further deleveraging in a weak market (a loss liquidity spiral). As a result,
both funding liquidity and asset liquidity are diminished.
One implication of Brunnermeier and Pedersen (2009) is that liquidity has commonality across
securities because shocks to funding liquidity (capital constraints) affect all securities in which
speculators are marginal investors.7 Empirically, there is support for the hypothesis of commonality in
liquidity: Chordia, Sarkar, and Subrahmanyam (2005) document commonality in stock and bond market
liquidity, Acharya, Schaefer, and Zhang (2008) document an increase in co-movement in CDS spreads
during the GM/Ford downgrade period when dealers faced liquidity shocks, and Coughenour and Saad
(2004) show that commonality in liquidity across stocks is higher for those handled by a NYSE specialist
firm that faces funding constraints. Another implication is that since most market makers have net long
positions, liquidity will tend to dry up most quickly when markets perform poorly, and will have a
stronger relationship with asset prices during these times than during normal times.
To test the implications of Brunnermeier and Pedersen (2009) for hedge fund contagion, we identify
six contagion channel variables, that is, variables whose extreme adverse realizations are associated with
a tightening of asset and hedge fund funding liquidity, and then test whether large negative shocks to
6
Brunnermeier and Pedersen (2009) note that the liquidity shock can be caused by a shock to liquidity demand,
fundamentals, or volatility.
7
In addition, a large literature shows that shocks to liquidity and liquidity risk affect asset returns and that there is
co-movement in liquidity and liquidity risk across asset classes (e.g., Amihud and Mendelson (1986), Amihud
(2002), Pastor and Stambaugh (2003), Chordia, Sarkar, and Subrahmanyam (2005), and Acharya and Pedersen
(2005)).
12
these variables can explain the hedge fund contagion we document. These variables include the Baa–10year Treasury constant maturity yield spread (CRSPRD), the Treasury–Eurodollar (TEDSPRD) spread,
the liquidity measure of Chordia, Sarkar, and Subrahmanyam (CSS, 2005) (STKLIQ), a stock index for
commercial banks (BANK), a stock index for prime brokers (PBI) and, finally, hedge fund redemptions
(FLOW). The construction of these variables, their foundation in the literature, and their relationship to
liquidity is detailed in Table IV. Additional proxies could be used. For instance, Dudley and Nimalendran
(2011) find that futures margins constitute a useful proxy for funding liquidity.
Table V presents summary statistics for the liquidity proxies. For all variables, the number of
observations is the same as for the hedge fund indices. The correlations between the variables are positive
and significant among the credit spread, TED spread, and stock liquidity variable, and between the bank
and prime broker indices. The high correlation between the bank and prime broker indices is not too
surprising, since the firms in these indices are all in the financial services industry. The correlation
between hedge fund flows and the credit spread is negative and significant, indicating that flows are
worse when credit spreads are high, while the credit spread correlation with prime broker stocks is also
negative and significant, indicating that prime brokers perform poorly when credit spreads are high.
Finally, the CSS liquidity measure (for which high values imply illiquidity) is negatively correlated with
bank and prime broker stocks, indicating that these stocks perform poorly when stock markets are less
liquid.
To test whether large adverse shocks to liquidity can help explain hedge fund contagion, we create
indicator variables for each of the six contagion channel variables. These indicators are set to one if the
contagion channel variable has a realization in its lowest (highest) quartile over the time series and zero
otherwise for variables that are positively (negatively) related to liquidity shocks. Hence, the prime broker
index, bank index, and hedge fund flow indicator variables are set to one if the changes in their
corresponding contagion channel variables are in the bottom 25% of all respective values, while the CSS
13
stock liquidity measure, the credit spread and the TED spread indicator variables are set to one if the
realizations are in the top 25% of all respective values.
To perform this analysis, we create a dependent variable COUNT10 defined as the number of hedge
fund indices that experience worst returns in a given month and estimate a linear probability model.
Since contemporaneous as well as lagged liquidity shocks could affect hedge fund returns, we also
include lagged realizations of the indicator variables. Regressions are performed separately for each
contagion channel variable, for a total of six regressions. The six regressions in Table VI include the
relevant contemporaneous and lagged measures of the channel indicator variable. A positive and
significant coefficient on a contagion channel indicator variable means that the variable is associated with
an increased probability that hedge fund worst returns exhibit contagion. The regressions also include as
control variables the returns on the three main market indices, the Treasury Bill return, the small stock
factor, 10-year CMT, changes in VIX, the Fung and Hsieh (2004) ABS factors, and extreme return
indicators for each of the three main market indices.
Consistent with the results from Boyson, Stahel, and Stulz (2010), Table VI provides strong evidence
that liquidity shocks are correlated with hedge fund contagion. Notably, contemporaneous shocks to the
credit spread, the TED spread, the prime broker stock index, and hedge fund flows are positively linked to
hedge fund contagion. Additionally, lagged shocks to the TED spread, stock market liquidity, bank stocks,
and prime broker stocks are also positively linked to hedge fund contagion, implying that some liquidity
shocks affect hedge fund performance with a lag. Since the liquidity variables are not perfectly correlated,
the results also imply that liquidity shocks manifest themselves in a variety of ways that all appear to
affect hedge fund contagion.8 To give these statistically significant results an economic magnitude, the
8
We also rerun these analyses adding two more lags of the shock variables, and find that the twice-lagged shocks to
the prime broker index, bank stock index, and stock market liquidity are correlated with hedge fund contagion.
However, thrice-lagged shocks are never correlated with contagion. These results are available by the authors upon
request.
14
number of hedge fund styles experiencing an extreme return increases by 100% when any of the dummy
variables indicate tight funding or asset liquidity.
Figure 2 overlays the number of contagion channel variables that have extreme realizations in a
given period on Figure 1, which shows the number of hedge fund indices experiencing extreme returns
each month. The number of contagion channel variables is called LIQCOUNT6, which is the count of the
number of liquidity shocks across all 6 contagion channel variables in a given month, with a minimum
value of 0 and a maximum value of 6. The number of hedge fund styles that simultaneously experience an
extreme return are represented by increasingly larger circles. Consistent with the regression results, hedge
fund contagion is exacerbated when liquidity is low, that is, generally the larger LIQCOUNT6, the larger
the circles.
B. Contagion between Hedge Funds and Main Markets
In this section, we attempt to link shocks to liquidity channel variables to the contagion between main
markets and hedge funds that we document in Section III. To perform this test we create new variables
that measure the strength of the contagion between each main market index and sub-index and hedge
funds in general. These variables are created by interacting the COUNT10 variable (which is the number
of hedge fund indices that experience worst returns in a given month and ranges from 0 to 10) with
indicator variables for each main market index that are set to 1 if the main market index also experiences
a worst return and zero otherwise. For example, if during a particular month the COUNT10 variable has a
value of 4 and the Russell 3000 index experiences simultaneously a worst return, the value of the
interaction variable would be 4. If however the Russell 3000 index does not have a worst return, the
interaction variable is zero. Therefore, the interaction variable is zero in the absence of contagion and
increases with the extent of the contagion. This approach results in 18 new interaction variables, one for
each of the 3 broad-based main market indices, 7 for the stock market sub-indices, 3 for the bond market
sub-indices, and 5 for each currency index. The main independent variable is our summary indicator
15
variable for shocks to liquidity, called LIQCOUNT6. As noted earlier, this variable tabulates the number
of liquidity shocks to the six contagion channel variables in a particular month. A positive and significant
coefficient on this variable implies a correlation between liquidity shocks and hedge fund-main market
contagion. Results are presented in Table VII.
We focus on the main market indices and sub-indices that show evidence of contagion in Table III.
These include the Russell 3000 broad-based stock market index, the small-cap and mid-cap domestic
stock sub-indices, the Asian and emerging markets equity sub-index, the high yield bond index, and the
emerging market bond, the Australian Dollar. Hence, if in Table VII the coefficients on the liquidity
shock or lagged liquidity shock indicator variables are positive and significant for the broad-based market
indices and market sub-indices that exhibit contagion in Table III, we can interpret this result as evidence
that liquidity shocks are linked to contagion between hedge funds and main markets for these main
market indices in the sense that if liquidity is scarce, we are more likely to observe contagion between
hedge funds and the main markets.
The results in Table VII suggest that for six of the eight main market indices and sub-indices that
exhibit contagion with hedge funds in Table III, contagion is connected to liquidity shocks, including the
Russell 3000 broad-based stock market index, the small- and mid-cap domestic stock market index, the
Asian stock market index, and the high yield and emerging bond indices. In addition, the broad-based
currency index, the large-cap domestic and the European Stock markets, and the British Pound have
positive and significant coefficients indicating that liquidity shocks also exacerbate contagion in these
markets.9 The economic relevance of these results can again be directly inferred from the coefficients. For
example, for every additional liquidity variable that exhibits a large shock (a one tick increase in
9
We also perform a robustness check where we include two more lags of the liquidity shock indicator variable. For
the twice-lagged variable, we find that contagion is connected to liquidity shocks for European stock markets, the
small-cap domestic market, domestic mid-cap, and emerging markets. For the thrice-lagged liquidity variable, we
find that contagion is connected to liquidity shocks for the Euro and British Pound indices. Detailed results are
available from the authors upon request.
16
LIQCOUNT6), the number of hedge fund index worst returns occurring simultaneously with a worst
return in the Russell 3000 increases by about 40%.
V. Implications and Conclusions
In this paper, we use a linear probability regression model to study contagion among hedge funds and
between hedge funds and main markets for the period January 1990 to October 2008. We find strong
evidence for the existence of contagion both among different styles of hedge funds, confirming the results
of Boyson, Stahel, and Stulz (2010), and between hedge funds and main markets, especially for small cap
stocks, high yield bonds, the Asian stock market, and the Australian Dollar. We also find evidence that
this contagion is linked to liquidity shocks, including shocks to credit spreads and the TED spread, shocks
to stock market liquidity, poor performance in bank and prime broker stocks, and finally, shocks to hedge
fund flows.
These results have important implications for hedge fund managers and hedge fund investors. First,
investors in hedge funds that diversify across different hedge fund styles expecting protection against
poor performance might be disappointed since all hedge fund styles tend to experience poor performance
simultaneously. Second, investors that use hedge funds as diversification tools against poor returns in
main markets should also be careful. Specifically, if future crises are similar to past crises, hedge funds do
not provide diversification benefits for the broad stock market, for small-cap, mid-cap, Asian, or
emerging market stocks, for high yield bonds or Asian bonds, or for Australian Dollar, and in fact, tend to
move closely with these markets during crises. That said, certain hedge fund styles do appear to provide
some diversification against poor returns in main markets. Notably, the Equity Hedge Market Neutral
(results not tabulated) style has no significant exposure to any of the main markets.
The results linking liquidity shocks to contagion also have important policy implications. While our
tests do not allow us to conclude that liquidity shocks actually cause financial contagion, the correlation is
important in that crises that also involve large liquidity shocks are particularly worrisome in terms of
17
financial contagion, both within the hedge fund industry and between hedge funds and main markets.
These results are consistent with Bordo and Haubrich (2009), who find that recessions that also involve
credit crunches are more severe. Hence, if a central bank is considering intervening in financial markets
during an economic downturn, the success of the potential intervention could be affected by the perceived
impact on market and funding liquidity.
18
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Economics 77, 375-410.
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clinical study of the General Motors and Ford downgrade of 2005, working paper, London Business
School.
Adrian, Tobias, and Markus K. Brunnermeier, 2009, CoVar, working paper, Princeton University.
Agarwal, Vikas, and Narayan J. Naik, 2004, Risk and portfolio decisions involving hedge funds,
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Bae, Kee Hong, G. Andrew Karolyi, and René M. Stulz, 2003, A new approach to measuring financial
contagion, Review of Financial Studies 16, 717-764.
Bekaert, Geert, Campbell R. Harvey, and Angela Ng, 2005, Market integration and contagion, Journal
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Billio, Monica, Mila Getmansky, and Loriana Pelizzon, 2010, Crises and hedge fund risk, working
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Bordo, M. D. and J. G. Haubrich. 2009. Credit Market Turmoil, Monetary Policy and Business Cycles:
A Historical Overview. Working paper.
Boyson, Nicole M., Christof Y. Stahel, and Rene M. Stulz, 2010. Hedge Fund Contagion and Liquidity
Shocks, Journal of Finance 65(5), 1789-1816.
Brunnermeier, Markus K., and Lasse Heje Pedersen, 2009, Market liquidity and funding liquidity,
Review of Financial Studies 22, 2201-2238.
Campbell, John Y., and Glen B. Taksler, 2003, Equity volatility and corporate bond yields, Journal of
Finance 58, 2321-2350.
Chan, Kevin Nicholas, Mila Getmansky, Shane Haas, and Andrew W. Lo, 2006, Systemic risk and
hedge funds, in Mark Carey and René M. Stulz, ed. The Risks of Financial Institutions (University
of Chicago Press).
Chordia, Tarun, Asani Sarkar, and Avinidhar Subrahmanyam, 2005, An empirical analysis of stock and
bond market liquidity, Review of Financial Studies 18, 85-129.
Cifuentes, Rodrigo, Gianluigi Ferrucci, and Hyun Song Shin, 2005, Liquidity Risk and Contagion,
Journal of the European Economic Association, 3, 556-566.
Coughenour, Jay F., and Mohsen Saad, 2004, Common market makers and commonality in liquidity,
Journal of Financial Economics 73, 37-70.
19
Dick-Nielsen, Jens, Peter Feldhütter, and David Lando, 2009, Corporate bond liquidity before and after
the onset of the subprime crisis, working paper, Copenhagen Business School.
Dudley, Evan, and Mahendrarajah Nimalendran, 2011, Margins and hedge fund contagion, Journal of
Financial and Quantitative Analysis, forthcoming.
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bonds, Journal of Financial Economics 33, 3-56.
Fung, William, and David A. Hsieh, 2004, Hedge fund benchmarks: A risk-based approach, Financial
Analyst Journal 60, 65-80.
Goyenko, Ruslan Y., Craig W. Holden, and Charles A. Trzcinka, 2009, Do liquidity measures measure
liquidity?, Journal of Financial Economics 92, 153-181.
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the pricing of interest rate swaps, Journal of Financial Economics 55, 239-279.
Longstaff, Francis A., Sanjay Mithal, and Eric Neis, 2005, Corporate yield spreads: Default risk or
liquidity? New evidence from the credit default swap market, Journal of Finance 60, 2213-2253.
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Journal: Macroeconomics 1, 58-83.
20
Table I
Summary Statistics of Monthly Returns on HFR Indices and Market Factors: January 1990 to October 2008
Summary statistics for monthly returns for ten HFR hedge fund indices and eighteen market factors used in the paper are reported below. Panel A
reports data on hedge fund indices, which include Event Driven Distressed Securities (EDDS), Event Driven Merger Arbitrage (EDMA), Equity
Hedge Market Neutral (EHMN), Equity Hedge Quantitative Directional (EHQD), Equity Hedge Short Bias (EHSB), Emerging Markets (EMKT),
Macro Systematic Diversified (MACS), Relative Value Fixed Income Convertible Arbitrage (RVCA), Relative Value Fixed Income Corporate
(RVCI), and Relative Value Multi-Strategy (RVMS), and are described more fully in Section II. Panel B reports data on the main market indices
which include three broad-based indices: the Russell 3000 (R3K), the Lehman Brothers Bond Index (LBD), the change in the trade-weighted U.S.
dollar exchange rate index (DLR), seven stock indices: the Russell 2000 (SML), the Wilshire U.S. mid-cap index (MID), the S&P 500 index
(LGE), the Datastream total return Asia index (ASA), the Datastream total return Europe index (ERS), the MSCI Emerging Markets index (EMS),
the MSCI Emerging Markets Latin America Index (LAT), three bond indices: the Datastream investment grade corporate bond index (IGR), the
Bank of America/Merrill Lynch high yield bond index (HYD), the JP Morgan emerging markets bond index (EMB), and 5 currency indices: the
Australian dollar index (AUS), the Swiss Franc index (SFR), the Euro index (EUR), the British Pound index (PD), and the Japanese Yen index
(YEN). All market indices are from Datastream. Correlations between the variables, the autocorrelations, as well as Jarque-Bera test statistics for
normality are reported below the summary statistics. A * indicates significance at the 5% level.
Panel A
EDDS
EDMA
EHMN
Median
Standard deviation
1.019
1.850
0.755
1.251
0.664
0.922
EDDS
EDMA
EHMN
EHQD
EHSB
EMKT
MACS
RVCA
RVCI
RVMS
1.000
0.585*
1.000
0.309*
0.319*
1.000
EHQD
EHSB
1.150
0.383
3.977
5.684
Correlations
0.662*
0.577*
0.274*
1.000
-0.481*
-0.402*
-0.120
-0.864*
1.000
21
EMKT
MACS
RVCA
RVCI
RVMS
1.138
4.261
1.057
2.115
0.590
1.762
0.618
1.936
0.671
1.263
0.684*
0.498*
0.199*
0.735*
-0.569*
1.000
0.295*
0.302*
0.251*
0.660*
-0.409*
0.464*
1.000
0.647*
0.500*
0.331*
0.442*
-0.504*
0.489*
0.096
1.000
0.806*
0.572*
0.208*
0.602*
0.010
0.610*
0.220*
0.643*
1.000
0.755*
0.469*
0.360*
0.610*
-0.560*
0.649*
0.221*
0.760*
0.797*
1.000
Table I
Summary Statistics of Monthly Returns on HFR Indices and Market Factors: January 1990 to October 2008, continued
Panel B
Main Market Indices
R3K
LBD
DLR
SML
MID
LGE
ASA
ERS
EMS
LAT
IGR
HYD
EMB
AUS
SFR
EUR
PD
YEN
Median
Std. Dev.
0.72
4.25
0.55
1.10
0.03
1.91
0.66
5.41
0.86
5.03
0.54
4.18
0.13
6.13
0.75
4.74
0.68
6.90
1.37
8.85
1.36
6.79
0.58
2.23
0.56
1.10
0.18
2.71
0.09
2.44
0.05
2.30
0.20
2.74
0.18
2.71
R3K
LBD
DLR
SML
MID
LGE
ASA
ERS
EMS
LAT
IGR
HYD
EMB
AUS
SFR
EUR
PD
YEN
1.00
0.17*
1.00
0.15*
0.24*
1.00
0.83*
0.06
0.11*
1.00
0.84*
0.10
0.09
0.87*
1.00
0.99*
0.18*
0.15*
0.77*
0.79*
1.00
0.51*
0.07
0.40*
0.45*
0.46*
0.50*
1.00
0.76*
0.13
0.42*
0.66*
0.67*
0.75*
0.62*
1.00
0.62*
0.02
0.12
0.61*
0.59*
0.60*
0.48*
0.59
0.88*
1.00
0.16
0.99*
0.24*
0.05
0.08
0.17*
0.06
0.11
-0.01
0.01
1.00
0.61*
0.33*
0.17*
0.60*
0.53*
0.59*
0.37*
0.58*
0.56*
0.51*
0.32*
1.00
0.38*
0.16*
0.03
0.36*
0.33*
0.36*
0.13
0.29*
0.41*
0.41*
0.15*
0.34*
1.00
0.21*
-0.02
0.46*
0.22*
0.17*
0.20*
0.32*
0.39*
0.40*
0.30*
-0.02
0.36*
0.13*
1.00
-0.04
0.18*
0.60*
-0.09
-0.06
-0.03
0.09
0.12*
-0.03
-0.09
0.17*
-0.06
-0.01
0.28*
1.00
0.04
0.21*
0.64*
-0.02
-0.01
0.05
0.15*
0.24*
0.08
0.01
0.20*
0.08
0.03
0.42*
0.92*
1.00
0.07
0.21*
0.52*
-0.05
0.00
0.09
0.19*
0.28*
0.11*
0.06
0.20*
0.07
0.07
0.37*
0.71*
0.80*
1.00
-0.03
0.05
0.42*
-0.07
-0.04
-0.02
0.24*
0.02
-0.02
-0.08
0.05
-0.17*
-0.12*
0.12*
0.49*
0.39*
0.27*
1.00
Correlations
0.69*
0.00
0.19*
0.67*
0.65*
0.66*
0.63*
0.70
1.00
22
Table II
Contagion Among Hedge Funds
The event of a worst return in each hedge fund style is modeled as a linear probability model. The dependent variable is an indicator set to 1 if the
hedge fund index has a worst return and zero otherwise. The regressions include a number of control variables, described in Section II. COUNT9 is
the independent variable of interest, and represents the number of hedge funds, other than the hedge fund represented by the dependent variable,
that have worst returns in a given month. The indices modeled include Event Driven Distressed Securities (EDDS), Event Driven Merger Arbitrage
(EDMA), Equity Hedge Market Neutral (EHMN), Equity Hedge Quantitative Directional (EHQD), Equity Hedge Short Bias (EHSB), Emerging
Markets (EMKT), Macro Systematic Diversified (MACS), Relative Value Fixed Income Convertible Arbitrage (RVCA), Relative Value Fixed
Income Corporate (RVCI), and Relative Value Multi-Strategy (RVMS). Below the coefficients are the p-values in parentheses. Coefficients with ***,
**
, and * are statistically significant at the 1%, 5%, and 10% levels, respectively.
EDDS
Intercept
COUNT9
Adj. R2
Controls?
-0.032
(0.444)
0.153***
(0.000)
0.584
Yes
EDMA
0.160***
(0.004)
0.039**
(0.016)
0.404
Yes
EHMN
0.139**
(0.015)
0.083***
(0.000)
0.244
Yes
EHQD
-0.077**
(0.045)
0.055***
(0.000)
0.653
Yes
EHSB
-0.066
(0.260)
0.047**
(0.013)
0.215
Yes
23
EMKT
0.023
(0.651)
0.076***
(0.000)
0.357
Yes
MACS
-0.025
(0.674)
0.042**
(0.029)
0.181
Yes
RVCA
0.230***
(0.000)
0.046***
(0.008)
0.317
Yes
RVCI
-0.041
(0.373)
0.094***
(0.000)
0.495
Yes
RVMS
0.088*
(0.063)
0.086***
(0.000)
0.459
Yes
Table III
Contagion Between Hedge Funds and Main Markets
The event of a worst return in each hedge fund style is modeled as a pooled cross-sectional time series linear
probability model. The dependent variable is an indicator set to 1 if the hedge fund index has a worst return and zero
otherwise. The fixed effects regressions include a number of control variables, described in Section III, as well as a
separate intercept for each of the hedge fund indices. Specification 1 includes as independent variables worst return
indicator variables for the broad-based stock, bond, and currency markets. Specification 2 includes as independent
variables worst return indicator variables for the seven stock market subindices. Specification 3 includes as
independent variables worst return indicator variables for the three bond market subindices. Finally, Specification 4
includes as independent variables worst return indicator variables for the five currency subindices . All subindices
are described in detail in Section III. Below the coefficients are the p-values in parentheses. Coefficients with ***, **,
and * are statistically significant at the 1%, 5%, and 10% levels, respectively.
Spec.1:
Broad
Indices
Spec. 2:
Stock
Subindices
Spec. 3:
Bond
Subindices
Spec. 4:
Curr.
Subindices
Worst Return Indicator Variables
IND: Russell 3000 Index
IND: Lehman Brothers Bond Index
IND: Δ in FRB Dollar Index
0.216***
(0.000)
-0.018
(0.508)
0.042
(0.125)
IND: Russell 2000 Index
IND: Dow Jones Mid-Cap Equity Index
IND: S&P 500 Index
IND: Datastream Asian Equity Index
IND: Datastream Europe Equity Index
IND: MSCI Em. Markets Equity Index
0.097***
(0.005)
0.144***
(0.000)
0.004
(0.915)
0.056**
(0.020)
0.030
(0.351)
0.070***
(0.010)
IND: Inv. Grade Corp Bond
-0.020
(0.457)
0.188***
(0.000)
0.041*
(0.077)
IND: BOA/ML Corp. High Yield Index
IND: JPM Emerging Market Bond Index
IND: Australian Dollar
IND: Swiss Franc
IND: Euro
IND: British Pound
IND: Japanese Yen
R2
Includes Controls?
Fixed Effects?
0.193
Yes
Yes
24
0.212
Yes
Yes
0.215
Yes
Yes
0.040*
(0.089)
0.015
(0.546)
0.002
(0.933)
-0.007
(0.771)
-0.045**
(0.031)
0.196
Yes
Yes
Table IV
Contagion Channel Variables
This table presents detail on the contagion channel variables from Section IV.
Prior Literature Using this
Variable
Longstaff, Mithal, and Neis
(2005), Dick-Nielsen,
Feldhütter, and Lando
(2009)
Relationship
to Liquidity
Inverse
Increased spreads imply higher borrowing costs
and/or higher credit risk.
Gupta and Subrahmanyam
(2000), Campbell and
Taksler (2003), Taylor and
Williams (2009)
Inverse
Higher trading costs imply lower liquidity.
Other common liquidity measures include
Amihud (2002), Pastor and Stambaugh (2003),
and Acharya and Pedersen (2005); we choose
the measure based on recent work by Goyenko,
Holden, and Trzcinka (2009) suggesting that
bid-ask spreads are the most appropriate
measure of liquidity.
Chordia, Sarkar, and
Subrahmanyam (2005),
Goyenko, Holden, and
Trzcinka (2009)
Inverse
Shocks that decrease the financial strength of
hedge fund intermediaries could be transmitted
to hedge funds through increased margin
requirements as they curtail their lending.
Chan, et. al.
Direct
PBI: Monthly change in the equallyweighted stock price index of prime broker
firms including Goldman Sachs, Morgan
Stanley, Bear Stearns, UBS AG, Bank of
America, Citigroup, Merrill Lynch, Lehman
Brothers, Credit Suisse, Deutsche Bank, and
Bank of New York Mellon, adjusted for
mergers and including bankruptcy returns.
Data are from CRSP.
Shocks that decrease the financial strength of
hedge fund intermediaries could be transmitted
to hedge funds through increased margin
requirements as they curtail their lending.
N/A
Direct
FLOW: Monthly change in hedge fund
outflows as a percentage of assets under
management calculated from individual
hedge fund data from Lipper TASS and
matched to HFR index data based on style
description. We use both contemporaneous
FLOWt and one-month-ahead FLOWt+1
since many hedge funds have redemption
notice periods.
Redemption requests force hedge funds to
liquidate more assets than required to meet
redemptions if they are levered, and make it
harder for hedge funds to borrow. Redemption
requests may come about from poor
performance, shifts in sentiment, or other
reasons. An alternative to accepting redemption
requests is to put up redemption gates, which
are unpopular.
N/A
Direct
Variable and Source
CRSPRD: Change in BAA-10-year
Constant Maturity Treasury credit spread
from the Federal Reserve Board’s website.
Basis for Inclusion
Increased spreads imply higher borrowing costs
and/or counterparty risk.
TEDSPRD: Change in Treasury-Eurodollar
(TED) spread from the Federal Reserve
Board’s website.
STKLIQ: Change in average round-trip cost
of a trade on the NYSE within a month;
calculated as the monthly average of daily
changes of the NYSE stock market liquidity
after removing deterministic day-of-theweek effects and effects related to changes
in tick size. The daily changes are
calculated from daily cross-sectional valueweighted averages of individual stock
proportional bid-ask spreads.
BANK: Monthly change in the equally
weighted stock price index of large
commercial banks from Datastream.
25
Table V
Summary Statistics for Contagion Channel Variables: January 1990 to October 2008
Summary statistics for monthly data on six contagion channel variables are described below. The variables include: the monthly percent change
in the Baa-10-year CMT spread, the monthly percent change in the Treasury-Eurodollar (TED) spread, the monthly percent change in the
Chordia, Sarkar, and Subrahmanyam (2005) liquidity measure, the monthly percent change in hedge fund flows as a percentage of assets
(contemporaneous), the monthly returns from the Datastream Bank Index, and the monthly returns from the Prime Broker Index. Further
description of these variables is in Section III. The number of observations is 226. Correlations between the variables are reported below the
summary statistics. A * indicates significance at the 5% level.
Mean
Standard deviation
Baa-10y Treasury CMT Spread
TED Spread
CSS Liquidity Measure
Contemp. Hedge Fund Flows
Bank Index
Prime Broker Index
Baa-10-year
CMT
Spread
1.073
10.731
1.000
CSS Liquidity
Measure
TED Spread
0.016
0.183
Correlations
0.359*
1.000
26
Contemporaneous
Hedge Fund Flows
Bank Index
0.001
0.761
1.067
2.249
1.023
6.061
0.365*
0.255*
1.000
-0.244*
-0.126
-0.105
1.000
-0.130
-0.011
-0.291*
0.065
1.000
Prime
Broker
Index
1.660
7.426
-0.262*
-0.070
-0.367*
0.065
0.866
1.000
Table VI
Liquidity Shocks and Contagion Across Hedge Funds
The co-occurrence of extreme monthly negative returns in hedge fund style indices is modeled as the
outcome of a variable (COUNT10) that takes a value of 0 to 10 and represents the number of hedge fund
index returns that experience worst returns in a given month. A monthly return is classified as a “worst
return” if it belongs to the bottom 10% of all returns of that style. The regressions also include the
continuous contagion channel variables and indicator variables corresponding to contemporaneous negative
quartile realizations of the contagion channel variables. The contagion channel variables and their
corresponding indicator variables include: the change in the Baa-10-year CMT Credit Spread (CRSPRD), the
change in the Treasury-Eurodollar spread (TEDSPRD), the change in the Chordia, Sarkar, Subramanyam
(2005) Liquidity Measure (STKLIQ), flows from other hedge funds (FLOW), the monthly change in the
Datastream Bank Index (BANK), and the monthly change in the Prime Broker Index (PBI). The p-values are
shown below the coefficients in parentheses. Coefficients with ***, **, and * are statistically significant at the
1%, 5%, and 10% levels, respectively.
CRSPRD
Constant
Liquidity Shock Indicator
Variable
Liquidity Shock Indicator
Variable, lagged
Adj. R2
Includes Controls?
Fixed Effects?
TEDSPRD
STKLIQ
FLOW
BANK
PBI
0.809***
(0.003)
0.933***
(0.000)
0.763***
(0.006)
0.898***
(0.000)
0.858***
(0.001)
0.745***
(0.004)
0.752***
(0.006)
0.763***
(0.000)
0.304
(0.198)
0.542**
(0.015)
0.400
(0.114)
0.435*
(0.090)
0.156
(0.460)
0.512**
(0.011)
0.479**
(0.025)
0.258
(0.238)
0.778***
(0.000)
0.791***
(0.000)
0.506
Yes
No
0.535
Yes
No
0.504
Yes
No
0.512
Yes
No
0.527
Yes
No
0.527
Yes
No
27
Table VII
Liquidity Shocks and Contagion Between Hedge Funds and Main Markets
Contagion between hedge funds and main markets is linked to liquidity shocks using a linear probability model. The dependent variable is
COUNT10 interacted with an indicator variable set to one if a main market or main market sub-index has a worst return, and zero otherwise.
These variables are denoted as “i” followed by the name of the index. For example, iRUS3000 is the interaction of COUNT10 and an indicator
variable for a worst return in the Russell 3000 index. The regressions include a number of control variables, described in Section IV. The index
and sub-index interaction variables include three broad-based market indices: the Russell 3000 (iR3000), the Lehman Brothers Bond Index
(iLBBD), the change in the trade-weighted U.S. dollar exchange rate index (iDLR), seven stock market indices: the Russell 2000 (iSML), the
Wilshire U.S. mid-cap index (IMID), the S&P 500 index (iLARGE), the Datastream total return Asia index (iASST), the Datastream total return
Europe index (iEUST), the MSCI Emerging Markets index (iEMST), the MSCI Emerging Markets Latin America Index (iLATIN), three bond
market indicies: the Datastream investment grade corporate bond index (iIGRD), the Bank of America/Merrill Lynch high yield bond index
(iHYLD), the JP Morgan emerging markets bond index (iEMBD), and five currency indices: the Australian dollar index (iAUST), the Swiss Franc
index (iSWFR), the Euro index (iEURO), the British Pound index (iPOUND), and the Japanese Yen index (iYEN). The independent variable of
interest is LIQCOUNT6, which is a variable that takes a value from 0 to 6 and counts the number of contagion channel variables that experience a
liquidity shock in a particular period. Both contemporaneous and one-month lagged measures of LIQCOUNT6 are included in the regressions.
Below the coefficients are the p-values in parentheses. Coefficients with ***, **, and * are statistically significant at the 1%, 5%, and 10% levels,
respectively.
Panel A: Broad-Based Markets and Equity Subindices
Broad-Based Indices
iR3000
iLBBD
iDLR
Intercept
LIQCOUNT6
LIQCOUNT6,
lagged
Adj. R2
Includes
Controls?
Fixed Effects?
iSML
iMID
iLGE
Equity Subindices
iASST
iEUST
iEMST
iLATIN
0.381*
(0.091)
0.124*
(0.086)
0.116*
0.432***
(0.007)
0.015
(0.773)
-0.005
0.191
(0.196)
0.081*
(0.097)
0.061
0.317
(0.173)
0.070
(0.342)
0.175***
0.200
(0.426)
0.136*
(0.089)
0.054
0.413*
(0.055)
0.050
(0.462)
0.117**
0.1354
(0.561)
0.025
(0.734)
0.140**
0.294
(0.193)
0.055
(0.445)
0.152**
0.176
(0.444)
0.109
(0.138)
0.060
0.588***
(0.003)
0.043
(0.493)
0.000
(0.054)
0.522
Yes
(0.907)
0.411
Yes
(0.117)
0.343
Yes
(0.005)
0.503
Yes
(0.414)
0.373
Yes
(0.041)
0.521
Yes
(0.025)
0.387
Yes
(0.012)
0.500
Yes
(0.325)
0.450
Yes
(0.999)
0.494
Yes
No
No
No
No
No
No
No
No
No
No
28
Table VII
Liquidity Shocks and Contagion Between Hedge Funds and Main Markets, continued
Panel B: Bond and Currency Subindices
Fixed Income Subindices
iIGRD
iHYLD
iEMBD
Intercept
LIQCOUNT6
LIQCOUNT6, lagged
Adj. R2
Includes Controls?
Fixed Effects?
0.432***
(0.007)
0.015
(0.773)
-0.005
(0.907)
0.411
Yes
No
0.232
(0.291)
0.105
(0.150)
0.212***
(0.000)
0.524
Yes
No
0.188
(0.357)
0.035
(0.600)
0.107*
(0.054)
0.430
Yes
No
29
iAUST
0.221
(0.224)
-0.012
(0.844)
0.052
(0.279)
0.500
Yes
No
Currency Subindices
iSWFR
iEURO
iPD
0.054
(0.616)
-0.007
(0.832)
0.038
(0.187)
0.075
Yes
No
0.238
(0.128)
-0.037
(0.475)
0.048
(0.248)
0.384
Yes
No
0.355**
(0.049)
-0.078
(0.184)
0.096**
(0.045)
0.368
Yes
No
iYEN
-0.019
(0.807)
-0.006
(0.806)
-0.006
(0.767)
0.015
Yes
No
Figure 1. Number of Hedge Fund Styles that have Worst Returns by Month. The number of hedge
fund styles that experience a worst return each month is plotted, by month, for the period January 1990 to
October 2008.
30
Figure 2. Hedge Fund Contagion and Liquidity Shocks. The number of contagion channel variables
experiencing extreme realizations (LIQCOUNT6) is plotted, by month, for the period January 1990 to
October 2008, on the left axis. The number of hedge fund style indices that experience a worst return
month are depicted circles. The larger a circle the more styles experience such an event simultaneously.
31