Sunday, 26 June 2016
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Feb 16 2009 | 12:00am ET
By Aleksey Matiychenko -- It is no secret that 2008 was one of Wall Street’s toughest years and perhaps the most challenging year ever for hedge funds. Evidence of this can be found in the dismal performance of the Barclay’s Hedge Fund and Fund of Funds indices, both down almost 21%. Many funds blew up, including Sailfish, Peloton and others. As the year closed, serious allegations of fraud were levied against one of the most famous and respected hedge fund managers, Bernard Madoff.
While it’s too early to count all of the casualties, even the preliminary numbers paint a rather bleak picture. Using Barclay’s Global Data Feeder database we estimate that about 18% of hedge funds either shut down or stopped reporting performance. Experts agree that the number of funds that go out of business will continue to increase throughout 2009.
The widespread fallout from 2008 will provide firms that managed to survive with many learning opportunities, including the importance of proper risk management.
Effective risk management requires that firms establish the culture, policies and procedures that are specific to their operating model. Fund of funds, family offices and other hedge fund investors share a common need for a strong risk measurement infrastructure. Though there is no single blueprint for building a strong risk management process, there is one common need: accurate risk measurement.
Back to the Basics
Quantitative hedge fund analysis starts with taking the pulse of the fund by calculating some simple risk / reward measures. This analysis serves as a starting point in estimating the relative attractiveness of the investment. Few investors will invest with a fund that has consistently lost money over an extended period. There may be compelling reasons to consider such an investment, though one must have at least a starting picture.
Figure 1 illustrates industry standard statistics calculated within PackHedge™. Our analysis begins with a review of the top two tables. The table in the upper left hand corner, titled “Returns”, provides us with a snapshot of information covering the fund’s entire track record. The table in the upper right hand corner, titled “Annual Returns”, allows us to view the fund’s return consistency by looking at the annualized returns across several calendar years.
Figure 1 (click on picture to enlarge)
At first glance, the above fund appears to be an attractive investment. During the period from January 2001 through December 2007 the fund produced an annualized return of 43.5%, with annualized volatility of 16.25%. The fund generated significant returns in four of the last five years and achieved positive, though modest returns in 2007. While volatility appears high at 16.25% annualized, common skill ratios such as Sharpe, Sortino and Sterling indicate that the level or risk may be justified by high returns.
One type of statistic shown in Figure 1 deserves special attention – VaR (Value at Risk). VaR has received plenty of negative publicity in recent months. The media has been full of articles blaming risk management failures of the past 12 months on inadequate risk systems and methodology. Of all risk measures, VaR has received more than its share of the blame.
A common criticism of VaR has been that it does not measure the tail risk correctly. The criticism is correct to the extent that the VaR method could have predicted the dramatic losses seen in the markets. The fallacy of the argument is that VaR is not designed to predict tail events. VaR is defined as the “maximum/minimum loss that an investment is likely to suffer at most/least x% of the time.” VaR does not contain any information of the magnitude of the loss beyond the confidence interval, and thus cannot be used to estimate the tail loss.
Figure 1 shows three measures of VaR calculated within PackHedge™. Historical VaR is based on the actual performance of the fund. VaR Normal calculates the maximum loss assuming the fund’s returns follow the Normal Distribution. Click Here To Download Full Article