Model Averaging in Risk Management with an Application to Futures Markets
M. Hashem Pesaran
University of Southern California; Cambridge University - Faculty of Economics; CESifo (Center for Economic Studies and Ifo Institute for Economic Research); Institute for the Study of Labor (IZA)
Bank of England
Imperial College Business School
CESifo Working Paper Series No. 2231
IEPR Working Paper No. 08.3
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 on six currencies, four equity indices, four ten year government bonds and four commodities over the period 1991-2007. The empirical evidence supports the use of 'thick' model averaging strategies over single models or Bayesian type model averaging procedures.
Number of Pages in PDF File: 52
Keywords: model averaging, Value-at-Risk, decision based evaluations
JEL Classification: C32, C52, C53, G11working papers series
Date posted: February 26, 2008
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