A conditional approach to hedge fund risks.

This article applies a two-step conditional Bayesian approach to hedge fund risk. First, a mixture or-two normal distributions is estimated for a core asset; one distribution being identified as linked to a "quiet" regime and the other to a "hectic" regime. The conditional probabilities of each regime are then inferred and a mixture of distributions is deduced for peripheral assets. The core asset is alternatively chosen as the S&P index or the Baa/Treasuries yield spread whereas the peripheral assets are chosen to be the major hedge funds strategies over the period 1990-2004. This methodology has several advantages given specific features of hedge funds returns, notably non-linear exposure to standard assets returns and short sample history. Significant changes in the distribution (mean and standard deviation) of hedge fund returns are identified across regimes. Results are less clear-cut for the correlation with standard assets, as modifications can be imputed to a certain extent to a form of selection bias. An application of this methodology to stress tests on hedge funds portfolios is presented.Risk; Standard deviations; Hedging (finance); Mutual funds; Hedge funds; Finance;

Reconsidering asset allocation involving illiquid assets

International audienc

Finance Internationale

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Active risk-based investing

Risk-based investment solutions are seen as incorporating no views. In this article, we propose an analytical framework that allows the introduction of explicit active views on expected asset returns in risk-based solutions. Starting from a Black-Litterman approach, we derive closed-form formulas for the weights of the active risk-based portfolio, and identify their main determinants. We discuss implementation aspects and show how our framework is related to other popular active investing methodologies. We illustrate the methodology with a multi-asset portfolio allocation problem using views based on macroeconomic regimes over the period 1974-2013

Active risk-based investing

Risk-based investing is experiencing growing success among investors, although some critics contend that the implicit “no-views” characteristic of these solutions might trigger other forms of risk, such as valuation risk. In this article, the authors introduce an analytical framework that allows investors to add active views on top of a risk-based solution, bridging the gap between risk-based investing and mean-variance portfolio optimization. Starting from a Black-Litterman approach, the authors derive closed-form expressions for the active risk-based portfolio weights and discuss practical implementation aspects. The framework is illustrated with a multi-asset allocation exercise over the period 1974–2016. Using views generated from macroeconomic regime signals, the active risk-based strategy is shown to outperform empirically both passive risk-based strategies and popular methodologies such as Equal-weight or Maximum Sharpe ratio

An Alternative Approach to Alternative Beta

Hedge fund replication based on factor models is encountering growing interest. In this paper, we investigate the implications of substituting standard rolling windows regressions, which appear ad-hoc, with more efficient methodologies like the Kalman filter. We show that the copycats constructed this way offer risk-return profiles which share several characteristics with the ones posted by hedge funds indices: Sharpe ratios above buy-and-hold strategies on standard assets, moderate correlation with tandard assets, and limited drawdowns during equity downward trends. An interesting result is that the shortfall risk seems less important than with hedge fund indices and regressions-based trackers. We finally propose new breakdowns of hedge fund erformance into alpha, traditional beta, and alternative beta.hedge fund replication; alternative beta; Kalman filter

Foreign-Exchange Intervention Strategies and Market Expectations: Insights from Japan

This study extends the traditional set of central bank's interventions to include official announcements in order to provide empirical evidence on two pivotal questions: (i) are FX authorities able to influence market expectations with different instruments? (ii) how should interventions be designed to have the greatest impact? Using Japanese data over 1992–2004 and an event-study approach, we estimate the effect of different strategies on the USD/JPY exchange-rate risk-neutral density. Overall, transparent policies (public and oral interventions) appear to be the most effective. Moreover, the effect is greater when policies involve a financial cost (risk) suggesting that simple announcements can only be deemed as an imperfect substitute for actual interventions.ou