Working Paper: CEPR ID: DP5279
Authors: M. Hashem Pesaran; Paolo Zaffaroni
Abstract: This paper considers the problem of model uncertainty in the case of multi-asset volatility models and discusses the use of model averaging techniques as a way of dealing with the risk of inadvertently using false models in portfolio management. Evaluation of volatility models is then considered and a simple Value-at-Risk (VaR) diagnostic test is proposed for individual as well as ?average? models. The asymptotic as well as the exact finite-sample distribution of the test statistic, dealing with the possibility of parameter uncertainty, are established. The model averaging idea and the VaR diagnostic tests are illustrated by an application to portfolios of daily returns based on 22 of Standard & Poor?s 500 industry group indices over the period 1995-2003. We find strong evidence in support of ?thick? modelling proposed in the forecasting literature by Granger and Jeon (2004).
Keywords: decision-based evaluations; model averaging; value-at-risk
JEL Codes: C32; C52; C53; G11
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| model averaging (C52) | reduce the risk of using incorrect models in portfolio management (C52) |
| model averaging (C52) | integrate the predictive densities of multiple models (C59) |
| VaR diagnostic test (C29) | reliable method for evaluating the performance of models (C52) |
| superior VaR performance (G17) | more effective in risk management (H12) |
| test statistic follows a binomial distribution (C46) | enables formal evaluation of model validity (C52) |
| thick modeling approach (C59) | importance of considering multiple models (C52) |
| choice of underlying distribution (C46) | impacts outcomes of VaR tests (C22) |