论文标题
具有正常钢化创新的多元政权切换GARCH模型的投资组合优化
Portfolio Optimization on Multivariate Regime Switching GARCH Model with Normal Tempered Stable Innovation
论文作者
论文摘要
本文使用基于模拟的投资组合优化来减轻投资组合的左尾风险。贡献是双重的。 （i）我们提出了Markov政权开关GARCH模型，该模型具有多元正常钢化稳定创新（MRS-MNTS-GARCH），以容纳脂肪尾巴，波动性聚类和政权开关。每个资产的波动性独立遵循了制度开关GARCH模型，而Garch模型的关节创新的相关性遵循隐藏的Markov模型。 （ii）在投资组合优化中，我们使用尾部风险度量，即有条件的危险价值（CVAR）和条件下降风险（CDAR）。使用MRS-MNTS-GARCH模型模拟的样品路径进行优化。我们对最佳投资组合表现的实证研究进行了实证研究。样本外测试表明，带有尾巴测量的最佳投资组合的表现优于具有标准偏差度量的最佳投资组合和各种性能指标中同样加权的投资组合。最佳投资组合的样本外部性能对于有效边界上的次级临时性也更强。
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.
