Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management

By M. Pesaran, Paolo Zaffaroni. Social Science Research Network, 2004

78 citations recorded by the source corpus.

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. In particular, it is shown that under certain conditions portfolio returns based on an average model will be more fat-tailed than if based on an individual underlying model with the same average volatility. Evaluation of volatility models is also considered and a simple Value-at-Risk (VaR) diagnostic test is proposed for individual as well as 'average' models and its exact and asymptotic properties are established. The model averaging idea and the VaR diagnostic tests are illustrated by an application to portfolios of daily returns based on twenty two of Standard & Poor's 500 industry group indices over the period January 2, 1995 to October 13, 2003, inclusive.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/8484ff0ad09bff428fb5058d6f70d2dcdf9e6928

Citations in the corpus

• Modelling Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution (2007, unknown): “See Pesaran and Pesaran (2007).”
• Evaluating, Comparing and Combining Density Forecasts Using the KLIC with an Application to the Bank of England and NIESR 'Fan' Charts of Inflation (2005, unknown): “5 Garratt et al. (2003) and Pesaran & Zaffaroni (2004) have also considered the combination of probability forecasts using BMA.”