Hedge Fund Portfolio Research Articles

The Nature of Alpha

We suggest an empirical model of investment strategy returns which elucidates the importance of non-Gaussian features, such as time-varying volatility, asymmetry and fat tails, in explaining the level of expected returns. Estimating the model on the (former) Lehman Brothers Hedge Fund Index data, we demonstrate that the volatility compensation is a significant component of the expected returns for most strategy styles, suggesting that many of these strategies should be thought of as being 'short vol'. We present some fundamental and technical reasons why this should indeed be the case, and suggest explanation for exception cases exhibiting 'long vol' characteristics. We conclude by drawing some lessons for hedge fund portfolio construction.

Dynamic Hedge Fund Portfolio Construction: A Semi-Parametric Approach

In this paper, we evaluate alternative optimization frameworks for constructing portfolios of hedge funds. Using monthly hedge fund index returns for the period 1990 to 2011, we compare the standard mean-variance optimization model with models based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investment strategies. In order to implement the CVaR, CDaR and Omega optimization models, we propose a semi-parametric methodology, in which we first model the marginal density of each hedge fund index using extreme value theory and construct the joint density of hedge fund index returns using a copula-based approach. We then simulate hedge fund returns from this joint density in order to compute CVaR, CDaR and Omega, which are used in the optimization process. We compare the semi-parametric approach with the standard, non-parametric approach, in which the quantiles of the marginal density of portfolio returns are estimated empirically and used to compute CVaR, CDaR and Omega. We report two main findings. The first is that the CVaR, CDaR and Omega models offer a significant improvement in terms of risk-adjusted portfolio performance over the mean-variance model. The second is that semi-parametric estimation of the CVaR, CDaR and Omega models offers a very substantial improvement over non-parametric estimation. Our results are robust to the choice of target return, risk limit and estimation sample size.

A Comparison of Hedge Fund Gross and Net Performance

In this paper, we explore the difference between the gross-of-fee and net-of-fee hedge fund performance, by investigating the difference in distribution, factor exposures and alphas between gross returns and net returns. We find that gross returns are distributed significantly different from net returns. The gross-of-fee alphas are higher than the net-of-fee alphas by about 4% per year on average. We also find positive relation between hedge fund performance and fund size, fund flows, and managerial incentives, which holds for both gross-of-fee performance and net-of-fee performance. Our findings suggest that it is necessary to examine the gross-of-fee performance of hedge funds separately from the net-of-fee performance, which may give us a clearer picture of the risk structure and performance of hedge fund portfolios.

Portfolio Optimization with Asian Hedge Funds: A Comparison between the Mean - Variance and the Polynomial Goal Progamming Methods

This study attempts to construct optimal portfolios of Asian hedge funds (AHFs) by employing both the traditional mean - variance (MV) method and the polynomial goal programming (PGP) method. In addition, further issues such as the existence of optimal number of funds and the contribution of a specific fund characteristic to the performance of a portfolio of AHFs are also investigated. From the Eurekahedge database, a sample consisting of 95 AHFs was selected for this study, covering the period of 1st January 2000 - 31st May 2009. Results confirmed that (1) diversification benefits exist in AHFs, (2) optimal number of funds exist for a portfolio of AHFs, (3) many portfolios of AHFs with specific fund characteristics achieved high probabilities of being either the first or the second best-performing portfolios at various levels of standard deviations, and (4) the PGP method was shown to be more effective than the MV method as it enables different efficient frontiers to be constructed according to individual investors’ risk preferences.

Diversification in Funds of Hedge Funds: Is It Possible to Overdiversify?

Many institutions are attracted to diversified portfolios of hedge funds, referred to as Funds of Hedge Funds (FoHFs). In this article, we examine a new database that separates out for the first time the effects of diversification (the number of underlying hedge funds) from scale (the magnitude of assets under management). We find with others that the variance-reducing effects of diversification diminish once FoHFs hold more than 20 underlying hedge funds. This excess diversification actually increases their left-tail risk exposure once we account for return smoothing. Furthermore, the average FoHF in our sample is more exposed to left-tail risk than are naive $1/N$ randomly chosen portfolios. This increase in tail risk is accompanied by lower returns, which we attribute to the cost of necessary due diligence that increases with the number of hedge funds.

The Failure of Traditional Portfolio Management For Long-Only and Hedge Fund Portfolios (Mean-Variance; Normal Distributions, Black Litterman)

Modern Portfolio Theory, introduced by Markowitz in 1952, is still used extensively as a tool for portfolio management of traditional, as well as alternative investments. Rather than selecting an asset based on its expected risk‐return profile, it considers how assets in a portfolio move in tandem. The fundamental concept is that of diversification. Given two assets, as long as there is less than perfectly positive correlation, the portfolio’s overall risk can be reduced. Although many of the model’s failings have been widely discussed, insufficient attention has been paid to the implications of constantly changing correlations. This paper is divided into two sections. The first section summarises the model’s key assumptions and limitations for managing traditional or alternative investment portfolios. Using data from 1999‐2010, we demonstrate that currency, commodity and equity returns are increasingly failing to follow a normal distribution. The second section (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1964388) tackles a less widely published topic: that of constantly changing correlations, as well as the impact of the time period under observation.

The Impact of Dynamic Correlations on Diversification within Long-Only and Hedge Fund Portfolios: Time To Rethink Portfolio Management

Correlation and Diversification: Strong (positive or negative) correlations make diversification that much harder to achieve, especially as correlations tend to increase with volatility. As greater occurrences of increasing volatility and more frequent ‘surprises’ have become the norm, it seems sensible to examine correlations in more depth, as these impact how asset‐classes and funds perform within the overall portfolio. To condense and assess vast quantities of data, a rudimentary scoring system was introduced. Counts were applied to rolling correlations across various time periods and the frequency of each score was recorded. We demonstrate that intra-day correlations vary from -1 to 1 across and intra-asset classes. This throws into question the relevance of Traditional Portfolio Theory and its attempt at diversification, as it assumes constant correlations and volatility. This paper demonstrates how asset correlations change significantly intra-day according to the different input/output time periods selected. From an investor's standpoint it should be clear that relying on monthly performance reports and monthly correlations mask an awful lot of information and in effect makes portfolio management ineffective.

Hedging Equity Market Risk in Hedge FundInvesting: A New Approach

Hartmann and Kaiser argue that investors in funds of hedge funds underestimate the traditional equity market beta of these perceived absolute return investment vehicles. Hence, during times of financial crisis when the correlations of most asset categories with each other increase dramatically, the traditional equity market beta can have an adverse effect on the performance of funds of funds. Because investors in such environments are rarely able to redeem their fund-of-funds holdings due to low liquidity (e.g.,notice and redemption periods or lock-ups) or extraordinary circumstances (e.g., maximum redemptions, gates, and side pockets), the authors posit that the only way to decrease unwanted exposure is through overlay strategies. The academic literature so far has focused on the multifactor approach to estimate equity exposure in hedge fund portfolios. The authors compare different hedging approaches: a pure value-at-risk (VaR)-based approach, a technical market risk signal approach, and a combination of both over the May 2004–September 2009 period using weekly data from an investable hedge fund index. Their results suggest that using a risk overlay system for funds of hedge funds significantly decreases the downside risk of hedge fund investing during economic crises. <b>TOPICS:</b>Equity portfolio management, VAR and use of alternative risk measures of trading risk, private equity

Diversification in Funds of Hedge Funds: Is it Possible to Overdiversify?

Samuelson (1967) argues that as a general matter it is easy to show that investors should be maximally diversified. For this reason many institutions are attracted to diversified portfolios of hedge funds, referred to as Funds of Hedge Funds (FOFs). In this paper we examine a new database that separates out for the first time the effects of diversification (the number of underlying hedge funds) from scale (the magnitude of assets under management). We find with others that the variance reducing effects of diversification peter out once FOFs hold more than 20 underlying hedge funds. Yet the majority of FOFs are more diversified than this. We find a new and surprising result that this excess diversification actually increases the left tail risk exposure of FOFs particularly once we account for the extent to which hedge fund returns are smoothed. Furthermore the average FOF in our sample is more exposed to left tail risk than are naive 1/N randomly chosen portfolios. This increase in tail risk is accompanied by lower returns which we attribute to the cost of necessary due diligence which increases with the number of underlying hedge funds in the FOFs .

Boost your traditional portfolio: Activate traditional asset classes with alternative investments

We propose a new methodology to include hedge funds in a traditional portfolio. The concept of ‘substitution’ consists of activating a passive traditional investment strategy in a given asset class by adding active hedge fund strategies focusing on the same asset class. Our analysis, based on long-term data, shows that activated asset classes outperform the passive investments in terms of total performance, risk-adjusted performance and downside protection. Aggregating activated asset classes into an overall portfolio improves the efficiency of the initial portfolio without deteriorating its fat tail risk. Finally, we show that the ‘substitution’ approach leads to similar results as an optimisation approach in which the hedge fund portfolio is optimised as a stand-alone investment class and then added to the traditional portfolio. These findings validate an alternative solution for institutions that want to invest in hedge fund strategies, a solution that may be closer to their approach of portfolio management.

Illiquidity Premia in Asset Returns: An Empirical Analysis of Hedge Funds, Mutual Funds, and US Equity Portfolios

We establish a link between illiquidity and positive autocorrelation in asset returns among a sample of hedge funds, mutual funds, and various equity portfolios. For hedge funds, this link can be confirmed by comparing the return autocorrelations of funds with shorter vs. longer redemption-notice periods. We also document significant positive return-autocorrelation in portfolios of securities that are generally considered less liquid, e.g., small-cap stocks, corporate bonds, mortgage-backed securities, and emerging-market investments. Using a sample of 2,927 hedge funds, 15,654 mutual funds, and 100 size- and book-to-market-sorted portfolios of US common stocks, we construct autocorrelation-sorted long/short portfolios and conclude that illiquidity premia are generally positive and significant, ranging from 2.74% to 9.91% per year among the various hedge funds and fixed-income mutual funds. We do not find evidence for this premium among equity and asset-allocation mutual funds, or among the 100 US equity portfolios. The time variation in our aggregated illiquidity premium shows that while 1998 was a difficult year for most funds with large illiquidity exposure, the following four years yielded significantly higher illiquidity premia that led to greater competition in credit markets, contributing to much lower illiquidity premia in the years leading up to the Financial Crisis of 2007–2008.

Caught by the Tail: Tail Risk Neutrality and Hedge Fund Returns

We propose a simple and yet robust measure of tail neutrality. By this measure, hedge funds are more sensitive to market risk when the market experiences a substantial decline. This is also true when we consider a number of distinct hedge fund styles. This source of risk is not diversifiable, and for this reason funds-of-funds as portfolios of hedge funds concentrate tail risk exposure rather than mitigate this effect. In today's uncertain market environment, the idea of investing in a fund immune from the day-to-day fluctuations in the market has a certain attraction. Indeed most hedge funds strategies generate returns that are reasonably uncorrelated with standard benchmarks. This leads many investors to believe that they are actually less risky than their active trading strategies would suggest. For many hedge funds this low correlation is illusory. Many active trading strategies that anticipate market movements will enter and leave the market with some frequency generating returns that are uncorrelated with benchmarks over the extended holding period of the average hedge fund investor. But this does not imply that they are low risk strategies, as in the kind of liquidity crisis that could prove fatal to the success of these trading schemes would cause the funds to fail just as the market is collapsing. This is of concern not only to investors but also to regulators as well. The bailout of LTCM in 1998 was occasioned by fears among regulators that liquidity problems at this fund could have a contagion effect spreading across the

A Robust Approach to Misspecifications and Non Linearities for Hedge Fund Replication and Alternative Beta

This paper explores a linear hedge fund replication and alternative beta methodology that is robust to the presence of non linearities and the possibility of model mis-specification. In a fashion similar to Roncalli and Weisang (2009a), the problem is cast as a tracking problem in order to allow for a dynamic treatment of the replication problem. In a quasi-experiment, using simulations, I explore the robustness of the methodology, and develop a statistic that helps in detecting the introduction of unmodeled components in hedge fund portfolios. Finally, the methodology is applied to a set of hedge fund indices.

Hedge Fund Biases after the Financial Crisis

In this paper, we explore how hedge fund database biases developed during the 2007-2009 financial crisis. Our sample consists of 8,935 hedge funds from the Lipper TASS Hedge Fund Database for the January 2002-September 2010 time period. The theoretical foundation of this paper draws from Fung and Hsieh (2000), who argue that time series of funds of hedge funds should be less prone to some of the documented database biases. We use a sampling technique to create hedge fund portfolios, and we then compare them using fund of fund data. We find empirical evidence that fund of hedge fund data is less biased than single hedge fund data, and that the impact of the survivorship and backfilling biases has increased since the financial crisis. We also find that the attrition rate for hedge funds has nearly doubled since the financial crisis, and that an elevated attrition rate has a negative impact on the quality and representativeness of hedge fund data due to the liquidation bias. The liquidation bias increased strongly in the aftermath of the financial crisis. It also fluctuates over time, and it can account for an overestimate of performance of over 10% p.a. Given this increase and the volatile nature of hedge fund biases, we believe investors (for benchmarking) and academics (for empirical studies) should consider refraining from using single hedge fund index data.

Portfolio Allocation of Hedge Funds

Research in hedge fund investing proposes different solutions to build optimal hedge fund portfolios. However, these solutions are direct extensions of the usual meanvariance framework, and still suffer from model risks. More complex approaches start to be used but are related to numerous estimation risks. We compare in this paper the out-sample properties of different allocation models through a dynamic investment exercise using hedge fund indices. We show that the best out-of-sample properties are obtained by allocation models that take into account the specific statistical properties of hedge fund returns.

The Value of Liquidity from the Hedge Fund Portfolio Manager’s Perspective

Most recent articles on the value of liquidity try to answer the following question: “What additional return should investors earn to compensate for the liquidity risk they take?” In this article, the authors propose a model that prices liquidity from the portfolio manager’s perspective. The authors focus on the case of hedge fund managers, because the liquidity of hedge funds has been an important issue during the recent financial crisis, as managers have struggled with an important flow of redemption orders. The authors find that under normal market conditions, a liquidity risk premium of at least 50 basis points per year can be justified. More volatile and illiquid conditions justify a much higher premium. The authors’ results are consistent with the idea that liquidity is a sort of put option on the illiquid asset and are also consistent with recent empirical findings.

Implementing a Hedge Fund Portfolio

Selection of a hedge fund (HF) strategy, or strategies, is highly relevant in determining how a HF portfolio will fare under various financial market environments. Equally important, one can easily argue, is the task of implementing the selected strategy mix by choosing among HF managers in each of the selected strategies and skillfully combining them into robust portfolios. This installment sketches a framework for due diligence for selecting from among competing HFs and touches on aspects of portfolio construction. While financial professionals have specific regulatory diligence requirements, the issues discussed in this paper will also be useful to anyone considering an investment in HFs.

The Value of Liquidity from the Hedge Fund Portfolio Manager’s Perspective

The Value of Liquidity from the Hedge Fund Portfolio Manager’s Perspective

Optimal hedge fund portfolios under liquidation risk

We use an expected utility framework to integrate the liquidation risk of hedge funds into portfolio allocation problems. The introduction of realistic investment constraints complicates the determination of the optimal solution, which is solved using a genetic algorithm that mimics the mechanism of natural evolution. We analyse the impact of the liquidation risk, of the investment constraints and of the agent's degree of risk aversion on the optimal allocation and on the optimal certainty equivalent of hedge fund portfolios. We observe, in particular, that the portfolio weights and their performance are significantly affected by liquidation risk. Finally, tight portfolio constraints can only provide limited protection against liquidation risk. This approach is of special interest to fund of hedge fund managers who wish to include the hedge fund liquidation risk in their portfolio optimization scheme.

Limited Liability Leverage (L3): A New Measure of Leverage

Average leverage is often used as a measure of risk. However, average leverage in a limited liability context should not be computed as a simple arithmetic average of the underlying constituents. In fact, using a simple arithmetic average can give misleading results. For example, the simple arithmetic average leverage may give results which run counter to the actual risk exposure for certain portfolios. This article introduces a new measure, Limited Liability Leverage (<i>L</i>³), which corrects the simple average for the limited liability framework. This new measure is discussed in the context of a portfolio of hedge funds and compared to a multi-strategy hedge fund structure, where trading units do not have limited liability. This perspective can also be extended to reflect the lower risk of a financial system with many small institutions relative to a system with large, “too-big-to-fail” institutions. <b>TOPICS:</b>Real assets/alternative investments/private equity, legal/regulatory/public policy, risk management, portfolio construction