Evaluating hedge fund performance: a stochastic dominance approach

We introduce a general and flexible framework for hedge fund performance evaluation and asset allocation: stochastic dominance (SD) theory. Our approach utilizes statistical tests for stochastic dominance to compare the returns of hedge funds. We form hedge fund portfolios by using SD criteria and examine the out-of-sample performance of these hedge fund portfolios. Compared to performance of portfolios of randomly selected hedge funds and mean-variance e¢ cient hedge funds, our results show that fund selection method based on SD criteria greatly improves the performance of hedge fund portfolio

Exploiting skewness to build an optimal hedge fund with a currency overlay

This paper documents an investigation into the use of portfolio selection methods to construct a hedge fund with a currency overlay. The fund, which is based on number of international stock and bond market indices and is constructed from the perspective of a Sterling investor, allows the individual exposures in the currency overlay to be optimally determined. As well as using traditional mean variance, the paper constructs the hedge funds using portfolio selection methods that incorporate skewness in the optimisation process. These methods are based on the multivariate skewnormal distribution, which motivates the use of a linear skewness shock. An extension to Stein's lemma gives the ability to explore the mean-variance-skewness efficient surface without the necessity to be concerned with the precise form of an individual investor's utility function. The results suggest that it is possible to use mean variance optimisation methods to build a hedge fund based on the assets and return forecasts described. The results also suggest that the inclusion of a skewness component in the optimisation is beneficial. In many of the cases reported, the skewness term contributes to an improvement in performance over and above that given by mean variance methods

Systemic Risk and Hedge Funds

Systemic risk is commonly used to describe the possibility of a series of correlated defaults among financial institutions---typically banks---that occur over a short period of time, often caused by a single major event. However, since the collapse of Long Term Capital Management in 1998, it has become clear that hedge funds are also involved in systemic risk exposures. The hedge-fund industry has a symbiotic relationship with the banking sector, and many banks now operate proprietary trading units that are organized much like hedge funds. As a result, the risk exposures of the hedge-fund industry may have a material impact on the banking sector, resulting in new sources of systemic risks. In this paper, we attempt to quantify the potential impact of hedge funds on systemic risk by developing a number of new risk measures for hedge funds and applying them to individual and aggregate hedge-fund returns data. These measures include: illiquidity risk exposure, nonlinear factor models for hedge-fund and banking-sector indexes, logistic regression analysis of hedge-fund liquidation probabilities, and aggregate measures of volatility and distress based on regime-switching models. Our preliminary findings suggest that the hedge-fund industry may be heading into a challenging period of lower expected returns, and that systemic risk is currently on the rise.

Copulas and bivariate risk measures : an application to hedge funds

With hedge funds, managers develop risk management models that mainly aim to play on the effect of decorrelation. In order to achieve this goal , companies use the correlation coefficient as an indicator for measuring dependencies existing between (i) the various hedge funds strategies and share index returns and (ii) hedge funds strategies against each other. Otherwise, copulas are a statistic tool to model the dependence in a realistic and less restrictive way, taking better account of the stylized facts in finance. This paper is a practical implementation of the copulas theory to model dependence between different hedge fund strategies and share index returns and between these strategies in relation to each other on a "normal" period and a period during which the market trend is downward. Our approach based on copulas allows us to determine the bivariate VaR level curves and to study extremal dependence between hedge funds strategies and share index returns through the use of some tail dependence measures which can be made into useful portfolio management tools.Hedge fund strategies, share index, dependence, copula, tail dependence, bivariate Value at Risk

Assessing and Valuing the Non-Linear Structure of Hedge Fund Returns

Several studies have put forward the non-linear structure and option-like features of returns associated with hedge fund strategies. The authors provide a statistical methodology to test for such non-linear features with the returns on any benchmark portfolio. They estimate the portfolio of options that best approximates the returns of a given hedge fund, account for this search in the statistical testing of the contingent claim features, and test whether the identified non-linear features have a positive value. The authors find that not all categories of funds exhibit significant non-linearities, and that only a few strategies as a group provide significant value to investors. Individual funds may still provide value in an otherwise poorly performing category.Econometric and statistical methods; Financial institutions

Spillover effects among financial institutions: a state-dependent sensitivity value-at-risk approach : [Version September 2012]

In this paper, we develop a state-dependent sensitivity value-at-risk (SDSVaR) approach that enables us to quantify the direction, size, and duration of risk spillovers among financial institutions as a function of the state of financial markets (tranquil, normal, and volatile). Within a system of quantile regressions for four sets of major financial institutions (commercial banks, investment banks, hedge funds, and insurance companies) we show that while small during normal times, equivalent shocks lead to considerable spillover effects in volatile market periods. Commercial banks and, especially, hedge funds appear to play a major role in the transmission of shocks to other financial institutions. Using daily data, we can trace out the spillover effects over time in a set of impulse response functions and find that they reach their peak after 10 to 15 days

Leverage-induced systemic risk under Basle II and other credit risk policies

We use a simple agent based model of value investors in financial markets to test three credit regulation policies. The first is the unregulated case, which only imposes limits on maximum leverage. The second is Basle II and the third is a hypothetical alternative in which banks perfectly hedge all of their leverage-induced risk with options. When compared to the unregulated case both Basle II and the perfect hedge policy reduce the risk of default when leverage is low but increase it when leverage is high. This is because both regulation policies increase the amount of synchronized buying and selling needed to achieve deleveraging, which can destabilize the market. None of these policies are optimal for everyone: Risk neutral investors prefer the unregulated case with low maximum leverage, banks prefer the perfect hedge policy, and fund managers prefer the unregulated case with high maximum leverage. No one prefers Basle II.Comment: 27 pages, 8 figure

Detecting Switching Strategies in Equity Hedge Funds

Equity hedge funds are thought to effectively operate market timing by implementing switching strategies conditional on market circumstances. In this paper we use only the reported monthly returns on a set of funds to infer the type of switching strategies they follow, if any, as well as their switching times. A set of regime-switching models for each equity hedge funds’ returns against various benchmarks are estimated; subsequently we answer the following general questions: What proportion of equity funds seem to have switching strategies in place? Which are the most popular instruments for switching strategies? And what is the relationship between the switching times of different funds? The general methodology applied in this paper may be useful to investors that wish to detect, from only from their reported returns, whether and when a particular fund has been timing the market.

Further Evidence on Hedge Funds Performance.

In this analysis we identify dynamic hedge fund strategies quantitatively pursuing a Principal Component Analysis following Fung and Hsieh (1997). We extract five dominant hedge fund strategies each representing similar investment styles and analyse the performance of each strategy by employing a multi-factor model comprising both market indices and passive option strategies along the lines of Agerwal and Naik (2000). We find that the majority of the five homogenous strategies show superior performance. However, correcting for survivorship bias this superior performance disappears.Hedge funds; Investment in securities; Performance; Dynamic strategies; Hedge funds performance;