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

Cheng Peng, Young Shin Kim and Stefan Mittnik
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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

JRFM, 2022, vol. 15, issue 5, 1-23

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.

Keywords: Markov regime-switching model; GARCH model; normal tempered stable distribution; portfolio optimization; conditional drawdown-at-risk; conditional value-at-risk (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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Working Paper: Portfolio Optimization on Multivariate Regime Switching GARCH Model with Normal Tempered Stable Innovation (2023)
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