Crisis 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.Hedge Fund, Risk Management, High frequency data

Dynamic Risk Exposure in Hedge Funds

We measure dynamic risk exposure of hedge funds to various risk factors during different market volatility conditions using the regime-switching beta model. We find that in the high-volatility regime (when the market is rolling-down) most of the 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 idiosyncratic risk in a high volatility regime and find that the joint probability jumps from approximately 0% to almost 100% only during the Long-Term Capital Management (LTCM) crisis. Out-of-sample forecasting tests confirm the economic importance of accounting for the presence of market volatility regimes in determining hedge funds risk exposure.Hedge Funds; Risk Management; Regime-Switching Models, Liquidity

Phase-Locking and Switching Volatility in Hedge Funds

This article aims to investigate the phase-locking and switching volatility in the idiosyncratic risk factor of hedge funds using switching regime beta models. This approach allows the analysis of hedge fund tail event behavior and in particular the changes in hedge fund exposure to various risk factors potentially related to liquidity risk, conditional on different states of the market. We and that in a normal state of the market, the exposure to risk factors could be very low but as soon as the market risk factor captured by the S&P500 moves to a down-market state characterized by negative returns and high volatility, the exposure of hedge fund indexes to the S&P500 and especially to other risk factors changes signi?cantly presenting evidence of phase-locking. We further extend the regime switching model to allow for non-linearity in residuals and show that switching regime models are able to capture and forecast the evolution of the idiosyncratic risk factor in terms of changes from a low volatility regime to a distressed state that are not directly related to market risk factors.Hedge Funds; Risk Management; Regime-Switching Models, Liquidity

Terrorism and Voting: The Effect of Rocket Threat on Voting in Israeli Elections

How does the threat of becoming a victim of terrorism affect voting behavior? Localities in southern Israel have been exposed to rocket attacks from the Gaza Strip since 2001. Relying on variation across time and space in the range of rockets, we identify the effect of this threat on voting in Israeli elections. We first show that the evolution of the rockets? range leads to exogenous variation in the threat of terrorism. We then compare voting in national elections within and outside the rockets? range. Our results suggest that the right-wing vote share is 2 to 6 percentage points higher in localities that are within the range?a substantively significant effect. Unlike previous studies that explore the role of actual exposure to terrorism on political preferences and behavior, we show that the mere threat of an attack affects voting

Tourism and Cross-Border Conflict: An Empirical Analysis of the Israeli-Palestinian Case

How does tourism affect conflict, and how is it affected by conflict and violence? Tourism is often proposed as a way to manage and resolve conflicts, especially those between close neighbors. Drawing on theories of economic cooperation and conflict, and using data from the Israeli-Palestinian case, this paper finds no strong evidence that tourism has a pacifying effect on conflict: regions that host more tourists, and that have a stronger tourism potential, are not more peaceful than other regions. Furthermore, hosting more tourists from the other side of the border does not affect violence. Finally, although tourism is sensitive to violence, this sensitivity is short-term and often conditional on other factors. These findings suggest that we should moderate our expectations about the potential effect of tourism on conflict resolution

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.

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.

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

This paper examines four different daily datasets of hedge fund return indexes: MSCI, FTSE, Dow Jones and HFRX, all based on investable hedge funds, and three different monthly datasets of hedge fund return indexes: CSFB, CISDM and HFR which comprise both investable and non-investable hedge funds. Our study, based on standard statistical analysis, non-parametric analysis of the distribution and non-parametric regressions with respect to the S&P500 index shows that key data biases and disparate index construction methodologies lead to different statistical properties of hedge fund databases. One key variable that highly affects the statistical properties of hedge fund index returns is the “investability” of hedge fundsHedge Fund, Risk Management, High frequency data

Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors

We propose several econometric measures of connectedness based on principal-components analysis and Granger-causality networks, and apply them to the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. We find that all four sectors have become highly interrelated over the past decade, likely increasing the level of systemic risk in the finance and insurance industries through a complex and time-varying network of relationships. These measures can also identify and quantify financial crisis periods, and seem to contain predictive power in out-of-sample tests. Our results show an asymmetry in the degree of connectedness among the four sectors, with banks playing a much more important role in transmitting shocks than other financial institutions.Systemic Risk; Financial Institutions; Liquidity; Financial Crises