Factor Selection in Dynamic Hedge Fund Replication Models: A Bayesian Approach

Guillaume Weisang

A chapter in Bayesian Model Comparison, 2014, vol. 34, pp 181-222 from Emerald Group Publishing Limited

Abstract: In this paper, I propose an algorithm combining adaptive sampling and Reversible Jump MCMC to deal with the problem of variable selection in time-varying linear model. These types of model arise naturally in financial application as illustrated by a motivational example. The methodology proposed here, dubbed adaptive reversible jump variable selection, differs from typical approaches by avoiding estimation of the factors and the difficulties stemming from the presence of the documented single factor bias. Illustrated by several simulated examples, the algorithm is shown to select the appropriate variables among a large set of candidates.

Keywords: Tracking problem; hedge fund replication; alternative beta; Kalman filter; factor selection; model selection; C1; C11; C32; C52; G11 (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320140000034009

DOI: 10.1108/S0731-905320140000034009

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