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Forecasting time‐varying covariance with a robust Bayesian threshold model

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  • Chih‐Chiang Wu
  • Jack C. Lee
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    Abstract

    This paper proposes a robust multivariate threshold vector autoregressive model with generalized autoregressive conditional heteroskedasticities and dynamic conditional correlations to describe conditional mean, volatility and correlation asymmetries in financial markets. In addition, the threshold variable for regime switching is formulated as a weighted average of endogenous variables to eliminate excessively subjective belief in the threshold variable decision and to serve as the proxy in deciding which market should be the price leader. The estimation is performed using Markov chain Monte Carlo methods. Furthermore, several meaningful criteria are introduced to assess the forecasting performance in the conditional covariance matrix. The proposed methodology is illustrated using daily S&P500 futures and spot prices. Copyright (C) 2010 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/for.1183
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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 30 (2011)
    Issue (Month): 5 (August)
    Pages: 451-468

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    Handle: RePEc:jof:jforec:v:30:y:2011:i:5:p:451-468

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

    Related research

    Keywords: dynamic conditional correlation ; generalized autoregressive conditional heteroskedasticity ; hedge performance ; Markov chain Monte Carlo ; value at risk ;

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