Mila Getmansky

An Econometric Model of Serial Correlation and Illiquidity in Hedge Fund Returns

The returns to hedge funds and other alternative investments are often highly serially correlated in sharp contrast to the returns of more traditional investment vehicles such as long-only equity portfolios and mutual funds. In this paper, we explore several sources of such serial correlation and show that the most likely explanation is illiquidity exposure, i.e., investments in securities that are not actively traded and for which market prices are not always readily available. For portfolios of illiquid securities, reported returns will tend to be smoother than true economic returns, which will understate volatility and increase risk-adjusted performance measures such as the Sharpe ratio. We propose an econometric model of illiquidity exposure and develop estimators for the smoothing profile as well as a smoothing-adjusted Sharpe ratio. For a sample of 908 hedge funds drawn from the TASS database, we show that our estimated smoothing coefficients vary considerably across hedge-fund style categories and may be a useful proxy for quantifying illiquidity exposure.

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.

Dynamic risk exposures in hedge funds

A regime-switching beta model is proposed to measure dynamic risk exposures of hedge funds to various risk factors during different market volatility conditions. Hedge fund exposures strongly depend on whether the equity market (S&P 500) is in the up, down, or tranquil regime. In the down-state of the market, when market volatility is high and returns are very low, S&P 500, Small-Large, Credit Spread, and VIX are common risk factors for most of the hedge fund strategies. This suggests that hedge fund exposures to the market, liquidity, credit, and volatility risks change depending on market conditions, and these risks are potentially common factors for the hedge fund industry in the down-state of the market.

Crises and Hedge Fund Risk

We study the effect of financial crises on hedge fund risk. Using a regime-switching beta model, we separate systematic and idiosyncratic components of hedge fund exposure. The systematic exposure to various risk factors is conditional on market volatility conditions. We find that in the high-volatility regime (when the market is rolling-down and is likely to be in a crisis state) most strategies are negatively and significantly exposed to the Large-Small and Credit Spread risk factors. This suggests that liquidity risk and credit risk are potentially common factors for different hedge fund strategies in the down-state of the market, when volatility is high and returns are very low. We further explore the possibility that all hedge fund strategies exhibit a high volatility regime of the idiosyncratic risk, which could be attributed to contagion among hedge fund strategies. In our sample this event happened only during the Long-Term Capital Management (LTCM) crisis of 1998. Other crises including the recent subprime mortgage crisis affected hedge funds only through systematic risk factors, and did not cause contagion among hedge funds.

The Life Cycle of Hedge Funds: Fund Flows, Size, Competition, and Performance

This paper analyzes the life cycles of hedge funds. Using the Lipper TASS database it provides category and fund specific factors that affect the survival probability of hedge funds. The findings show that in general, investors chasing individual fund performance, thus increasing fund flows, decrease probabilities of hedge funds liquidating. However, if investors chase a category of hedge funds that has performed well (favorable positioned), then the probability of hedge funds liquidating in this category increases. We interpret this finding as a result of competition among hedge funds in a category. As competition increases, marginal funds are more likely to be liquidated than funds that deliver superior risk-adjusted returns. We also find that there is a concave relationship between performance and lagged assets under management. The implication of this study is that an optimal asset size can be obtained by balancing out the effects of past returns, fund flows, competition, market impact, and favorable category positioning that are modeled in the paper. Hedge funds in capacity constrained and illiquid categories are subject to high market impact, have limited investment opportuniteis, and are likely to exhibit an optimal size behavior.

On a New Approach for Analyzing and Managing Macrofinancial Risks

At the fifth annual CFA Institute European Investment Conference on 19 October 2012 in Prague, Robert C. Merton gave a presentation on analyzing and managing macrofinancial risk. This article is based on his talk and on research he carried out with his coauthors. A framework for measuring and analyzing macrofinancial risk, particularly financial system credit risk and sovereign credit risk, is described, along with how one might go about monitoring the connections. The data suggest that the degree of connectedness across different types of financial institutions and sovereigns changes considerably over time. Current financial system models used by economists and central banks to assess and manage economies are generally not capable of accurately analyzing and managing the macrofinancial risks because they do not incorporate the fundamental nonlinear structures of credit risks. As a result, they cannot measure the changing degree of connectedness among financial institutions and sovereigns. A new approach for analyzing and managing macrofinancial risks is needed, particularly one that integrates monetary, fiscal, and financial stability policies and accounts for interconnectedness and risk transmission. At the fifth annual CFA Institute European Investment Conference on 19 October 2012 in Prague, Robert C. Merton gave a presentation on analyzing and managing macrofinancial risk. This article is based on his talk and on research he carried out with his coauthors. Editor’s Note: Much of the research discussed herein is from the paper “Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Networks,” by M. Billio, M. Getmansky, D. Gray, A. Lo, R.C. Merton, and L. Pelizzon, MIT working paper (forthcoming 2013). Authors’ Note: This article was accepted before 3 February 2013.

Non-Parametric Analysis of Hedge Fund Returns: New Insights from High Frequency Data

The history of financial research often reflects the history of the changing nature of financial data. Simply put, one has a difficult time analyzing questions requiring empirical data that does not exist. In the area of hedge funds, currently-available databases provide only monthly returns. Moreover, various providers of hedge fund index data follow very different methodologies in structuring hedge fund indices. This article examines four daily hedge fund return indices: MSCI, FTSE, Dow Jones, and HFRX, all based on investable hedge funds, and three monthly hedge fund return indices: CSFB Tremont, CISDM, and HFR, which comprise both investable and non-investable hedge funds. This study, based on standard statistical analysis, non-parametric analysis of the return distribution, and non-parametric regressions with respect to the S&P500 index, shows that key biases like fund selection, asset liquidity,data frequency, sample period, and index construction methodologies are responsible for different statistical properties of hedge fund indices.One key variable that highly affects the statistical properties of hedge fund index returns is the “investability” of hedge fund indices.

Interconnectedness in the CDS Market

Concentrated risks in markets for credit default swaps (CDS) are widely considered to have significantly contributed to the recent financial crisis. In this paper we study the structure of the CDS market using explicit connections based on the total number of CDS transactions, notional value of CDS transactions, net outstanding positions, and network diagrams. The main objective is to provide statistics that characterize the CDS market, the degree of counterparty concentration, the size of different contracts as well as underlying contractual features, and a preliminary analysis of interconnectivity. Our new approach informs the discussion of the structure and resulting fragility or stability of the CDS market and studies potential contagion among its participants. Our findings are of practical importance because, even after central clearing becomes mandatory, counterparty risk will remain a relevant systemic consideration due to the long-term nature of CDS contracts.

Portfolio Similarity and Asset Liquidation in the Insurance Industry

Insurance companies have been designated as Systemically Important Financial Institutions (SIFI) based upon the presumption that large insurers have similar portfolios and this similarity has the potential to affect the asset liquidation channel of systemic risk transmission. Analyzing a comprehensive dataset of both public and private insurance companies from 2002 to 2014, we construct a portfolio similarity measure using cosine similarity. We show that greater portfolio similarity between two insurers is significantly related to the similarity in insurers’ asset liquidation decisions and this relationship is only marginally related to whether or not insurer pairs are capital constrained. Potential SIFIs (insurers with

An econometric model of serial correlation and illiquidity in hedge fund returns

50 billion or more in consolidated assets), but not other insurers, with greater portfolio similarity of illiquid and downgraded securities have greater sales similarity during the financial crisis. This relationship remains strong even during the post-crisis period, providing support for the basis of their designation as systemically important. Our portfolio similarity measure provides information on the potential selling behavior of insurers over and above size and total sales. This work provides an implementable mechanism to identify and monitor the interconnectedness of insurer portfolios, thus helping regulators to identify asset liquidation channel vulnerabilities.