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Common Mistakes of Hedge Fund Valuation Print E-mail

The typical mistakes in hedge fund valuation and risk assessment derive from neglecting their unique problems (see "The Unique Problems of Hedge Funds"). The distinct hedge fund valuation and risk management problems can be outlined as follows (view screenshots).

Using the mean-variance framework for hedge fund risk assessment. This is by far the most common yet devastating mistake. Since hedge funds exhibit a high degree of non-normality of their distributions of returns, the risk measures based on the normality assumption (ex. the standard deviation and its derivatives like the Sharp or Sortino ratios) cannot be used.
Using hedge fund indices as comparative benchmarks of individual fund performance. Hedge fund indices suffer from numerous biases (see "Biases of Hedge Fund indices") and drawbacks. In brief, hedge funds may behave in discordance with their corresponding indices, while similar indices from different vendors may be negatively correlated. For example, only 11.9% of the Long/Short equity funds evidence correlation of over 0.5 with their corresponding index, while 8.3% exhibit negative correlation.
Using spot statistics, i.e. ignoring their dynamic behavior. Any spot measures are hardly informative, because only depict the current time "risk shot”. The rolling time window graphs should be used instead to get a correct picture of the indicator’s trend over time.
Ignoring the statistical significance of calculated metrics (correlation, r-squared etc). Though the significance tests are a must for any academic research, this is rarely done in practice. The bottom line is that simple: if calculated statistics show low significance levels, their value is zero.
Taking into account too long historical return series. While long return series increase statistical validity of the computations, old historic observations are less relevant. Overcoming this issue can be done by two ways. First, taking into consideration short series enough for the statistical confidence tests. Second, applying more advanced techniques using declining weights on past data (ex. the Hybrid VaR approach).
Disregarding credit risks for multi-strategy funds. Analyzing historical performance data takes into account only market risks, though multi-strategy managers may be exposed to credit risks as well.
Considering a low correlation between two assets as a definite sign of their neutrality. This is absolutely wrong concept. While a high correlation value most likely means traceable relationship (linear for the Pearson method), the opposite conclusion is incorrect. A low correlation may be caused by a non-linear connection between assets that cannot be identified by the applied methods.