Bayesian Nonparametric Measurement of Factor Betas and Clustering with Application to Hedge Fund Returns

Urbi Garay, Enrique Ter Horst, German Molina and Abel Rodriguez
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Enrique Ter Horst: IESA, Caracas 1010, Venezuela
German Molina: Idalion Capital Group, London W1J 8NR, United Kingdom
Abel Rodriguez: Baskin School, University of California at Santa Cruz, Santa Cruz 95064, USA

Econometrics, 2016, vol. 4, issue 1, 1-23

Abstract: We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return) and betas (to a choice set of explanatory factors) in a multivariate setting. This approach, as well as the outputs, has a dynamic, nonstationary and nonparametric form, which circumvents the problem of model risk and parametric assumptions that the Kalman filter and other widely used approaches rely on. The by-product of clusters, used for shrinkage and information borrowing, can be of use to determine relationships around specific events. This approach exhibits a smaller Root Mean Squared Error than traditionally used benchmarks in financial settings, which we illustrate through simulation. As an illustration, we use hedge fund index data, and find that our estimated alphas are, on average, 0.13% per month higher (1.6% per year) than alphas estimated through Ordinary Least Squares. The approach exhibits fast adaptation to abrupt changes in the parameters, as seen in our estimated alphas and betas, which exhibit high volatility, especially in periods which can be identified as times of stressful market events, a reflection of the dynamic positioning of hedge fund portfolio managers.

Keywords: nonparametric clustering; Bayesian; cluster; nonparametric alpha and beta; hedge fund performance (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2016
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