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

Journal of Risk and Financial Management

Stefan Mittnik¹  Young Shin Kim²  Cheng Peng³ 

[1] Chair of Financial Econometrics, Institute of Statistics, Ludwig Maximilian University Munich, Akademiestr. 1/I, 80799 Munich, Germany;College of Business, Stony Brook University, Stony Brook, NY 11794, USA;Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY 11794, USA;

关键词: Markov regime-switching model;    GARCH model;    normal tempered stable distribution;    portfolio optimization;    conditional drawdown-at-risk;    conditional value-at-risk;   

DOI  :  10.3390/jrfm15050230

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown