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

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Pesaran, M Hashem
Zaffaroni, Paolo

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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).

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 5279.

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Date of creation: Oct 2005
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Handle: RePEc:cpr:ceprdp:5279

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Related research
Keywords: decision-based evaluations; model averaging; value-at-risk;

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Pesaran, B. & Pesaran, M.H., 2007. "Modelling Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution," Cambridge Working Papers in Economics 0734, Faculty of Economics, University of Cambridge. [Downloadable!]
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  2. Bahram Pesaran & M. Hashem Pesaran, 2007. "Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution," CESifo Working Paper Series CESifo Working Paper No. , CESifo Group Munich. [Downloadable!]
  3. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," CFS Working Paper Series 2005/02, Center for Financial Studies. [Downloadable!]
    Other versions:
  4. Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics. [Downloadable!]
    Other versions:
  5. Anthony Garratt & Kevin Lee & Emi Mise & Kalvinder Shields, 2006. "Real Time Representation of the UK Output Gap in the Presence of Trend Uncertainty," Birkbeck Working Papers in Economics and Finance 0618, Birkbeck, Department of Economics, Mathematics & Statistics. [Downloadable!]
  6. Antonio Ciccone & Marek Jarocinski, 2008. "Determinants of economic growth - will data tell?," Working Paper Series 852, European Central Bank. [Downloadable!]
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  7. Alessandra Amendola & Giuseppe Storti, 2009. "Combination of multivariate volatility forecasts," SFB 649 Discussion Papers SFB649DP2009-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
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