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

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