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

Cheng Peng, Young Shin Kim and Stefan Mittnik

Papers from arXiv.org

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.

Date: 2020-09, Revised 2023-02
New Economics Papers: this item is included in nep-fmk and nep-rmg
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Citations: View citations in EconPapers (1)

Published in J. Risk Financial Manag. 2022, 15(5), 230

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Journal Article: Portfolio Optimization on Multivariate Regime-Switching GARCH Model with Normal Tempered Stable Innovation (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2009.11367

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