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Bayesian Analysis of Realized Matrix-Exponential GARCH Models

Author

Listed:
  • Manabu Asai

    (Soka University)

  • Michael McAleer

    (Asia University
    University of Sydney Business School
    Erasmus University Rotterdam
    Complutense University of Madrid)

Abstract

This study develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and realized measure of co-volatility matrix simultaneously. An alternative multivariate asymmetric function to develop news impact curves is also considered. We consider Bayesian Markov chain Monte Carlo estimation to allow non-normal posterior distributions and illustrate the usefulness of the algorithm with numerical simulations for two assets. We compare the realized MEGARCH models with existing multivariate GARCH class models for three US financial assets . The empirical results indicate that the realized MEGARCH models outperform the other models regarding out-of-sample performance. The news impact curves based on the posterior densities provide reasonable results.

Suggested Citation

  • Manabu Asai & Michael McAleer, 2022. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 103-123, January.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:1:d:10.1007_s10614-020-10074-6
    DOI: 10.1007/s10614-020-10074-6
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    More about this item

    Keywords

    Multivariate GARCH; Realized measure; Matrix-exponential; Bayesian Markov chain Monte Carlo method; Asymmetry;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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