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Econometrics 2016, 4(1), 13; doi:10.3390/econometrics4010013

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

1
IESA, Caracas 1010, Venezuela
2
CESA, Bogota , Colombia
3
Idalion Capital Group, London W1J 8NR, United Kingdom
4
Baskin School, University of California at Santa Cruz, Santa Cruz 95064, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Roberto Casarin, Francesco Ravazzolo, Herman K. van Dijk and Nalan Basturk
Received: 8 June 2015 / Revised: 4 December 2015 / Accepted: 28 January 2016 / Published: 8 March 2016
(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)
View Full-Text   |   Download PDF [1265 KB, uploaded 8 March 2016]   |  

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. View Full-Text
Keywords: nonparametric clustering; Bayesian; cluster; nonparametric alpha and beta; hedge fund performance nonparametric clustering; Bayesian; cluster; nonparametric alpha and beta; hedge fund performance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Garay, U.; ter Horst, E.; Molina, G.; Rodriguez, A. Bayesian Nonparametric Measurement of Factor Betas and Clustering with Application to Hedge Fund Returns. Econometrics 2016, 4, 13.

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