Dow Jones Club Conference

Scientific conference (2009, October 19)

Dynamic Hedge Fund Style Analysis with Errors-in-Variables

Conference (2009, May)

This paper revisits the traditional return-based style analysis (RBSA) in presence of time-varying exposures and errors-in-variables (EIV). We first apply a selection algorithm using the Kalman filter to ... [more ▼]

This paper revisits the traditional return-based style analysis (RBSA) in presence of time-varying exposures and errors-in-variables (EIV). We first apply a selection algorithm using the Kalman filter to identify the more appropriate benchmarks for the analyzed fund return. Then, we compute their corresponding higher moment estimated errors-in-variables, i.e. the measurement error series introducing the (cross) moments of order three and four. We adjust the selected benchmarks by subtracting their higher moments estimated EIV from the initial return series, to obtain an estimate of the true uncontaminated benchmarks. We finally run the Kalman filter on these adjusted regressors. Analyzing EDHEC alternative indexes styles, we show that this technique improves the factor loadings and permits to identify more precisely the return sources of the considered hedge fund strategy. [less ▲]

Une radiographie des risques des fonds monétaires

in Magazine du Trésorier, Association des trésoriers d'entreprise à Luxembourg (2009)

Belgian Parliamentary commission dealing with the study of the financial and banking crisis

Report (2009)

Quel avenir pour l’industrie des hedge funds ?

in La Lettre de Financière MJ - Family Office (2009)

Mean-Variance versus Mean-VaR and Mean-Utility Spanning

in Gregoriou, Greg (Ed.) Stock Market Volatility (2009)

How Stable are the Major Performance Measures?

in Journal of Performance Measurement (2008), (Fall), 21-30

In this paper, the authors compare three usual performance measures of actively managed portfolios: Jensen’s Alpha, the Information Ratio (IR), and the newly proposed Generalized Treynor Ratio (GTR ... [more ▼]

In this paper, the authors compare three usual performance measures of actively managed portfolios: Jensen’s Alpha, the Information Ratio (IR), and the newly proposed Generalized Treynor Ratio (GTR) introduced by Hübner (2005). They focus on model specification, sensitivity, and persistence for a large sample of mutual funds from January 1996 to December 2006. Their results reveal that fund classification made with the GTR displays a higher stability while the IR exhibits a greater capacity to reveal persistence in performance. The value of alpha is clearly contingent on model specifications and thus needs to be considered with greater caution to perform ranking of portfolio managers. [less ▲]

Dynamic Hedge Fund Style Analysis with Errors-in-Variables

Conference (2008, July)

This paper revisits the traditional return-based style analysis (RBSA) in presence of time-varying exposures and errors-in-variables (EIV). We first apply a selection algorithm using the Kalman filter to ... [more ▼]

This paper revisits the traditional return-based style analysis (RBSA) in presence of time-varying exposures and errors-in-variables (EIV). We first apply a selection algorithm using the Kalman filter to identify the more appropriate benchmarks for the analyzed fund return. Then, we compute their corresponding higher moment estimated errors-in-variables, i.e. the measurement error series introducing the (cross) moments of order three and four. We adjust the selected benchmarks by subtracting their higher moments estimated EIV from the initial return series, to obtain an estimate of the true uncontaminated benchmarks. We finally run the Kalman filter on these adjusted regressors. Analyzing EDHEC alternative indexes styles, we show that this technique improves the factor loadings and permits to identify more precisely the return sources of the considered hedge fund strategy. [less ▲]

Dynamic Hedge Fund Style Analysis with Errors-in-Variables

Conference (2008, June)

This paper revisits the traditional return-based style analysis (RBSA) in presence of time-varying exposures and errors-in-variables (EIV). We first apply a selection algorithm using the Kalman filter to ... [more ▼]

This paper revisits the traditional return-based style analysis (RBSA) in presence of time-varying exposures and errors-in-variables (EIV). We first apply a selection algorithm using the Kalman filter to identify the more appropriate benchmarks for the analyzed fund return. Then, we compute their corresponding higher moment estimated errors-in-variables, i.e. the measurement error series introducing the (cross) moments of order three and four. We adjust the selected benchmarks by subtracting their higher moments estimated EIV from the initial return series, to obtain an estimate of the true uncontaminated benchmarks. We finally run the Kalman filter on these adjusted regressors. Analyzing EDHEC alternative indexes styles, we show that this technique improves the factor loadings and permits to identify more precisely the return sources of the considered hedge fund strategy. [less ▲]

Mean-Variance versus Mean-VaR and Mean-Utility Spanning

in Gregoriou, Greg N. (Ed.) Stock Market Volatility Book (2008)

In this chapter, we contrast the optimal spanning properties of portfolios built under the traditional mean-variance (VAR) or mean-modified value-at-risk (MVaR) approaches with those created with the ... [more ▼]

In this chapter, we contrast the optimal spanning properties of portfolios built under the traditional mean-variance (VAR) or mean-modified value-at-risk (MVaR) approaches with those created with the linear-exponential (linex) utility function. Unlike asset allocation procedures that build on volatility or MVaR as a measure of risk and a single risk aversion parameter that characterizes investors, the use of linex utility introduces risk differentiation amongst investors and the risk-return relation of the optimal portfolio trades off between mean, variance, skewness and kurtosis. We identify efficient portfolios under the three competing frameworks and analyze their optimal allocations. [less ▲]