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Portfolio Optimization on Multivariate Regime-Switching GARCH Model with Normal Tempered Stable Innovation

Author

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  • Cheng Peng

    (Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY 11794, USA)

  • Young Shin Kim

    (College of Business, Stony Brook University, Stony Brook, NY 11794, USA)

  • Stefan Mittnik

    (Chair of Financial Econometrics, Institute of Statistics, Ludwig Maximilian University Munich, Akademiestr. 1/I, 80799 Munich, Germany)

Abstract

This paper uses simulation-based portfolio optimization to mitigate the left tail risk of the portfolio. The contribution is twofold. (i) We propose the Markov regime-switching GARCH model with multivariate normal tempered stable innovation (MRS-MNTS-GARCH) to accommodate fat tails, volatility clustering and regime switch. The volatility of each asset independently follows the regime-switch GARCH model, while the correlation of joint innovation of the GARCH models follows the Hidden Markov Model. (ii) We use tail risk measures, namely conditional value-at-risk (CVaR) and conditional drawdown-at-risk (CDaR), in the portfolio optimization. The optimization is performed with the sample paths simulated by the MRS-MNTS-GARCH model. We conduct an empirical study on the performance of optimal portfolios. Out-of-sample tests show that the optimal portfolios with tail measures outperform the optimal portfolio with standard deviation measure and the equally weighted portfolio in various performance measures. The out-of-sample performance of the optimal portfolios is also more robust to suboptimality on the efficient frontier.

Suggested Citation

  • Cheng Peng & Young Shin Kim & Stefan Mittnik, 2022. "Portfolio Optimization on Multivariate Regime-Switching GARCH Model with Normal Tempered Stable Innovation," JRFM, MDPI, vol. 15(5), pages 1-23, May.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:5:p:230-:d:821738
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    References listed on IDEAS

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    Cited by:

    1. Li, Chenxing, 2022. "A multivariate GARCH model with an infinite hidden Markov mixture," MPRA Paper 112792, University Library of Munich, Germany.

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