Beyond ‘Bayesian vs. Var’ Dilemma to Empirical Model Risk Management: Managing Risk for Hedge Funds

Abstract

In the aftermath of the Global Financial Crisis, some risk management practitioners have advocated wider adoption of Bayesian inference to replace Value- at-Risk (VaR) models in order to minimize risk failures. Despite its limitations, the Bayesian methodology has significant advantages. Just like all other quantitative models, 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. Many studies have noted that Bayesian inference modeling and VaR modeling frameworks facilitate model risk management for minimizing risks. VaR frameworks are empirically applied for hedge fund risk modeling of multi-asset fund of funds portfolios of a large investment bank. In this study, 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.


Journal granted non-exclusive copyright to publish the article: Author retains all copyrights:
Malhotra, Yogesh. Beyond 'Bayesian vs. VaR' Dilemma to Empirical Model Risk Management: Managing Risk for Hedge Funds, IUP Journal of Financial Risk Management. Jun 2022, Vol. 19 Issue 2, p5-51. 47p.

© Copyright Dr. Yogesh Malhotra, www.YogeshMalhotra.com. All Rights Reserved, 2022.

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