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

TL;DR

This paper introduces a novel multivariate regime-switching GARCH model with tempered stable innovations for portfolio optimization, focusing on tail risk measures like CVaR and CDaR to improve out-of-sample performance.

Contribution

We develop a new regime-switching GARCH model with stable innovations and incorporate tail risk measures into simulation-based portfolio optimization.

Findings

Optimal portfolios with tail risk measures outperform standard deviation-based portfolios.

Out-of-sample tests show improved robustness and performance.

The model captures fat tails, volatility clustering, and regime shifts effectively.

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.