JAULA EXTERIOR PISTA DE BOLAS

The analysis begins with a brief look at the raw returns of investment banks and hedge funds. Table 1 shows the median return, standard deviation, skewness, and excess kurtosis, and extremely good (95% percentile) and poor (5% percentile) returns. To reduce survivorship bias in the reported numbers, I limit the time span to the period from 1990 to 2008. A quick scan of returns and risk may offer some valuable insights about the characteristics of the two sectors. First, the median monthly return and standard deviation of investment banks are substantially higher than those of hedge funds and market returns. The difference is partially explained by the fact that the average leverage of big investment banks is more than 25 (King, 2008), compared to recent estimates of the average leverage of hedge funds of less than 2 (Ang et al., 2011) and average market leverage of about 1.618_{. Second, as can be seen through excess kurtosis, investment banks}

are substantially more susceptible to tail risk than firms in the market index and hedge funds. Alternatively, the hedge fund index is substantially better shaped in terms of conventional risk measures (standard deviation) as well as tail risks. For every unit of standard deviation19_{, hedge}

fund investors earn a monthly median rate of 0.55%, while market portfolio investors earn 0.318%,

18 _{Leverage = Total Asset / Total Equity Capital} 19 _{(Monthly Median)/(Monthly Standard Deviation)}

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and investors in investment banks earn 0.315%. More importantly, the tail risk of hedge funds is substantially lower than the other two. Figure 5 shows the compounding return of the four major hedge fund styles along with the investment bank index, market index, and value weighted hedge fund index. Investment banks have performed significantly better than other indices at the expense of greater variability in returns and substantial tail risk.

I also include the summary statistics for each individual investment bank used in the analyses and all hedge funds whose prime brokerage is the corresponding investment bank. For example, all hedge funds whose prime brokerage is Goldman Sachs have a monthly median return of 0.4%, while the return for Lehman Brothers’ affiliated hedge funds is substantially higher (1.4%). Interestingly, the standard deviation of the surviving investment banks (Morgan Stanley and Goldman Sachs) is not significantly different with that of failed investment banks, suggesting that their failure cannot be attributed to their ex-ante conventional risk taking. Perhaps the most interesting observation is that hedge funds associated with the two of the failed investment banks (Lehman Brothers and Merrill Lynch) have significantly higher risk measures than hedge funds affiliated with the two surviving investment banks (Goldman Sachs and Morgan Stanley). These numbers suggest that hedge fund risk taking (rather than investment bank risk taking) might contribute to the failure of the investment banks.

Table 2 shows the correlation matrix of hedge fund and investment bank raw returns. Panel A presents the correlation at the index level, while Panel B shows the correlation of each investment bank with its affiliated hedge funds. As can be seen from Panel A, the hedge fund index is significantly correlated with both the market and investment banks (corr = 0.81 and 0.7, respectively). This number is greatest for the “event” style hedge fund and lowest for the “equity

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neutral” style (that simultaneous takes a short and long position in the equity market). Interestingly, raw returns of event style and relative value style funds share the most commonality with the investment bank index (𝜌= 0.9 and 0.76, respectively), suggesting that they are the dominant type of business activities for investment banks. At the individual firm level, Panel B shows that the correlation between each investment bank and its affiliated hedge funds is much smaller than at the index level. For example, the correlation between Bear Stearns and its affiliated hedge funds is 0.44, compared to an index level correlation of 0.7. More interestingly, the correlation of an investment bank’s returns with its affiliated hedge funds is not necessarily greater than its correlation with non-affiliated hedge funds. For example, the correlation between Goldman Sachs and its affiliated hedge funds is 0.37, while the correlation between Goldman Sachs with the affiliated hedge funds of Bear Stearns is even higher (0.59). This may suggest that, at the lowest decile, a greater correlation between each investment bank and its affiliated hedge funds might not be the result of shared risk factors (e.g., sharing common asset portfolios). In an unreported table, I repeat Table 2 for the filtered returns (instead of raw returns); the correlation of each individual investment bank with its affiliated hedge funds becomes insignificant at any confidence level, confirming that asset pricing models used to filter raw returns are effective.

Table 3 provides the first evidence for the existence of contagion between the two sectors. The test is conducted on a pooled panel of individual investment banks paired with their affiliated hedge funds. For every percentile, I present the number of common days (relative to total days in that percentile) that the returns of both investment banks and hedge funds are below the stated

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percentile20_{. I include numbers for both residual and total returns (raw return). For example, for}

20% of days, both banks and hedge funds experience returns greater than the top 5% percentile returns, while for 30% of days, both banks and hedge funds experience returns lower than the bottom 5% percentile returns. This implies that co-movement between the two sectors is more frequent when returns are in the lowest percentile than the highest. Interestingly, in moving upward toward higher percentiles, the difference between the lowest and the top percentile declines, suggesting that contagion between the two sectors only occurs during periods of extremely low returns. Finally, total returns show little difference in any percentile, suggesting that the common risk factors that drive returns for both sectors act relatively uniformly across high and low percentiles of returns.