Much research into financial contagion and systematic risks has been motivated by the finding that cross-market correlations (resp. coexceedances) between asset returns increase significantly during crisis periods. Is this increase due to an exogenous shock common to all markets (interdependence) or due to certain types of transmission of shocks between markets (contagion)? Darolles and Gourieroux explain that an attempt to convey contagion and causality in a static framework can be flawed due to identification problems; they provide a more precise definition of the notion of shock to strengthen the solution within a dynamic framework. This book covers the standard practice for defining shocks in SVAR models, impulse response functions, identitification issues, static and dynamic models, leading to the challenges of measurement of systematic risk and contagion, with interpretations of hedge fund survival and market liquidity risks - Features the standard practice of defining shocks to models to help you to define impulse response and dynamic consequences - Shows that identification of shocks can be solved in a dynamic framework, even within a linear perspective - Helps you to apply the models to portfolio management, risk monitoring, and the analysis of financial stability

The focus of the paper is the nonparametric estimation of an instrumental regression function f defined by conditional moment restrictions stemming from a structural econometric model: E [Y - f (Z) | W] = 0, and involving endogenous variables Y and Z and instruments W. The function f is the solution of an ill-posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyses identification and overidentification of this model and presents asymptotic properties of the estimated nonparametric instrumental regression function.

This paper introduces a new fund performance measure, called the L-performance. It is proposed as an alternative to the Sharpe performance measure that is commonly used for fund performance valuation despite its inability to account for skewness and thick tails of fund return distributions. The L-performance improves upon the Sharpe measure in this respect. Technically, the L-performance is based on sample statistics, called L-moments, which are conceptually close to the conventional power moments, but provide more detailed information about the extremes. For this reason, the L-moments are used for prediction and assessment of extreme events, such as floods and earthquakes. In this paper, the new L-performance measure is calculated for a variety of hedge funds and is used to derive a fund ranking.

This paper develops a dynamic approach for assessing hedge fund risk exposures. First, we focus on an approximate factor model framework to deal with the factor selection issue. Instead of keeping the number of factors unchanged, we apply Bai and Ng (2002, 2006) to select the appropriate factors at each date. Second, we take into account the instability of asset risk profile by using rolling period analysis in order to estimate hedge fund risk exposures. Individual hedge fund returns instead of index returns are employed in the empirical application to go further in the comprehension of the covariation structure of the data. In particular, the common behavior of hedge fund returns is filtered not only from the past historical data (time-series dimension), but also from the cross-section of returns. Finally, we apply our approach to equity hedge funds, and replicate the returns of the aggregated index.

Despite having registered significant investor appetite in recent years, empirical research on UCITS compliant hedge funds (“Newcits” or “Alternative UCITS”) is a rare commodity. The major contribution of this paper is therefore to evaluate the performance of publicly regulated Alternative UCITS vehicles in comparison to traditional hedge funds. For the first time, a representative set of data has been analyzed on a period from June 2004 to May 2011. The results shed light on what really matters for investors: regulation, managerial skills and risk. We show that the UCITS regulatory constraints come at a cost to performance and that the impact of regulation differs from one strategy to another. We also find that the performance of Alternative UCITS is positively affected by the skill set of the manager. In particular, hedge fund experience is relevant when managing Alternative UCITS funds.

We develop a new methodology to analyse the dynamics of liquidation risk dependence in the hedge funds industry. This dependence results either from a common exogenous factor, or from conta- gion phenomena caused by an endogenous behaviour of fund managers. Our empirical analysis shows that the common factor, the sensitivities to this factor and the contagion scheme can be interpreted in terms of liquidity risks. The factor is related nonlinearly to rollover and margin funding liquidity risks. The sensitivities to the factor are funding liquidity risk exposures, which depend on the redemption and leverage policies of fund managers. The causal scheme captures the reinforcing spiral between funding and market liquidity.

This paper analyses the purchase and redemption behaviour of mutual fund investors and its implications on fund liquidity risk. We collect a novel set of proprietary data which contains a large number of French investors holding funds with various degrees of asset liquidity. We build a Self-Exciting Poisson model capturing fund flows' clustering effects and over-dispersion. The model improves the forecast accuracy of future flows and provides a reliable risk indicator (Flow Value at Risk.) Accordingly, we introduce the notion of liability risk where investor's behaviour increases mutual fund liquidity risk. We further decompose fund flows into investor categories. We find that investors exhibit high heterogeneous behaviour, and a lead-lag relation exists between them. Finally, we control flow dynamics for various economic conditions. We show that although flows evolve with economic conditions, investor's behaviour stays the main significant determinant of flows' randomness. Our findings encourage fund manager to adopt an ALM approach.