Investments, tenth edition

 Figure 26.6 

Monthly return on hedge fund indexes versus return on the S&P 500, 2008–2012.  Panel A,  

hedge fund index.  Panel B,  market-neutral funds.  Panel C,  short-bias funds.    

 Source: Constructed from data downloaded from   www.hedgeindex.com   and   finance.yahoo.com   .  

−12%

−10%

−8%

−6%

−4%

−2%

0%

2%

4%

6%

S&P 500 Return

Return on Broad Hedge Fund Index

A

−20%

−15%

−10%

−5%

0%

5%

10%

15%

−20%

−15%

−10%

−5%

0%

5%

10%

15%

−20%

−15%

−10%

−5%

0%

5%

10%

15%

S&P 500 Return

Return on Market-Neutral Funds

B

%

15

5

5

−20%

−15%

−10%

−5%

0%

5%

10%

15%

−20%

−15%

−10%

−5%

0%

5%

10%

15%

S&P 500 Return

Return on Short Bias Funds

C

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2 6

 Hedge 

Funds 

943

with black swans, deeply important developments that simply could not have been predicted 

from the range of accumulated experience to date. While we can’t predict which black swans 

to expect, we nevertheless know that some black swan may be making an appearance at any 

moment. The October 1987 crash, when the market fell by more than 20% in 1 day, might be 

viewed as a black swan—an event that had never taken place before, one that most market 

observers would have dismissed as impossible and certainly not worth modeling, but with 

high impact. These sorts of events seemingly come out of the blue, and they caution us to 

show great humility when we use past experience to evaluate the future risk of our actions. 

With this in mind, consider again the example of Long Term Capital Management. 

 

 In the late 1990s, Long Term Capital Management was widely viewed as the most 

successful hedge fund in history. It had consistently provided double-digit returns to 

its investors, and it had earned hundreds of millions of dollars in incentive fees for its 

managers. The firm used sophisticated computer models to estimate correlations across 

assets and believed that its capital was almost 10 times the annual standard deviation of 

its portfolio returns, presumably enough to withstand any “possible” shock to capital (at 

least, assuming normal distributions!). But in the summer of 1998, things went badly. 

On August 17, 1998, Russia defaulted on its sovereign debt and threw capital markets 

into chaos. LTCM’s  1-day  loss on August 21 was $550 million (approximately nine times 

its estimated  monthly  standard deviation). Total losses in August were about $1.3 bil-

lion, despite the fact that LTCM believed that most of its positions were market-neutral 

relative-value trades. Losses accrued on virtually all of its positions, flying in the face of 

the presumed diversification of the overall portfolio. 

 How did this happen? The answer lies in the massive flight to quality and, even 

more so, to liquidity that was set off by the Russian default. LTCM was typically a seller 

of liquidity (holding less liquid assets, selling more liquid assets with lower yields, and 

earning the yield spread) and suffered huge losses. This was a different type of shock 

from those that appeared in its historical sample/modeling period. In the liquidity crisis 

that engulfed asset markets, the unexpected commonality of liquidity risk across usually 

uncorrelated asset classes became obvious. Losses that seemed statistically impossible 

on past experience had in fact come to pass; LTCM fell victim to a black swan.