Evaluating hedge fund managers: A Bayesian investigation of skill and persistence
AbstractThe last couple of decades has witnessed a growing interest in hedge funds. Academics and practitioners are intrigued by the distinct characteristics of these investment vehicles: hedge funds are flexible with respect to the types of securities they hold and the type of positions they take; they are not subject to public disclosure of their activities; and they are not evaluated against a passive benchmark. Operating in this environment encourages hedge fund managers to construct highly dynamic trading strategies and as a result expose their portfolios to a plethora of economic risk factors. From the investor point of view, very little is known about the investment process, the economic risks associated with a particular hedge fund or a hedge fund strategy, and the skills of the hedge fund manager. While the details of the investment process could be of little practical importance to the investor, the latter â€“ risks and managerial skill - are not. The objective of separating â€˜alphaâ€™ and â€˜betaâ€™ - the non-systematic and systematic components of returns respectively â€“ is thus one of the main themes of current interest on hedge fund investing. This work introduces a Bayesian approach to estimate the â€˜alphaâ€™ and â€˜betaâ€™ of hedge fund investments. The Bayesian analysis consists of parameter estimation and model selection, and is implemented via Markov Chain Monte Carlo methodologies. The proposed stochastic search algorithm is appealing in identifying the relevant systematic risk factors in situations where the set of possible factors is very large, i.e. in hedge funds (â€˜black boxâ€™ investments). The entire analysis is carried out in a dynamic setup where hedge fund return volatilities change over time. We address two empirical questions. First we examine if the proposed methodology has an impact on the relative ranking of hedge fund managers compared with current approaches. Second, we study the nature of persistence in hedge fund return performance, i.e. short- or long-term. These questions are extremely interesting for the practice of hedge fund investing. A reliable â€˜alphaâ€™ estimation leads to more efficient manager selection schemes, i.e. fund of hedge funds or multi-strategy funds. In addition identifying the nature of persistence in hedge fund investment performance helps in determining successful portfolio rebalancing strategies (timing).
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 487.
Date of creation: 04 Jul 2006
Date of revision:
Bayesian methods; Hedge Funds; Markov Chain Monte Carlo; Manager skill;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum).
If references are entirely missing, you can add them using this form.