Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management: How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
50 Pages Posted: 16 Dec 2014 Last revised: 17 Apr 2015
Date Written: December 4, 2014
In aftermath of the Financial Crisis, some risk management practitioners advocate wider adoption of Bayesian inference to replace Value-at-Risk (VaR) models for minimizing risk failures (Borison & Hamm, 2010). They claim reliance of Bayesian inference on subjective judgment, the key limitation of Bayesian methodology as underscored by statisticians (Kass & Raftery, 1995; Kruschke, 2011; Lynch, 2007), as the most significant advantage compared with VaR (Christoffersen, 2012). Despite its well-known limitations, just like all other quantitative models (Derman, 1996; Morini, 2011), VaR – [mostly] non-Bayesian and [increasingly] Bayesian – continues to be a key methodological foundation of risk management and regulation related risk modeling practices in global Finance (Danielsson et al., 2014; Zangari, 1996). Bayesian inference modeling and VaR modeling frameworks are outlined to facilitate model risk management (Derman, 1996; Morini, 2011; US Fed & OCC, 2011) for minimizing risk of any model – Bayesian, VaR, or Bayesian VaR. VaR frameworks are empirically applied for hedge fund risk modeling (Darbyshire & Hampton, 2012, 2014) of a multi-asset fund of funds portfolio of a large Wall Street investment bank. Multiple risk models and measures with transparent assumptions to cross-validate convergent findings across multiple levels of risk analysis are examined for empirical model risk management.
Keywords: Model Risk Management, Risk Modeling, Bayesian Inference, VaR, Portfolio Construction, Portfolio Optimization, Fund of Funds, Hedge Funds.
JEL Classification: B23, C1, C10, C11, C12, C13, C14, C15, C19, C22, C32, C4, C5, C51, C52, D81, G11
Suggested Citation: Suggested Citation