To improve the dynamic assessment of risks of speculative assets, we apply a Markov switching MGARCH approach to portfolio risk forecasting. More specifically, we take advantage of the flexible Markov switching copula multivariate GARCH (MS‐C‐MGARCH) model of Fülle and Herwartz (2022). As an empirical illustration, we take the perspective of a risk‐averse agent and employ the suggested model for assessments of future risks of portfolios composed of a high‐yield equity index (S&P 500) and two safe‐haven investment instruments (i.e., Gold and US Treasury Bond Futures). We follow recent suggestions to employ the expected shortfall as a prime assessment of tail risks. To accurately evaluate the merits of the new model, we back‐test the risk forecasting for daily returns over 10 years for heterogeneous market environments including, for example, the COVID‐19 pandemic. We find that the MS‐C‐MGARCH model outperforms benchmark volatility models (MGARCH, C‐MGARCH) in predicting both value‐at‐risk and expected shortfall. The superiority of the MS‐C‐MGARCH model becomes stronger, when the share of comparably risky assets in the portfolio is relatively large.

中文翻译：

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通过具有不同 copula 机制的马尔可夫切换 MGARCH 模型来预测尾部风险

为了改进投机资产风险的动态评估，我们将马尔可夫切换 MGARCH 方法应用于投资组合风险预测。更具体地说，我们利用 Fülle 和 Herwartz (2022) 的灵活马尔可夫切换联结多元 GARCH (MS-C-MGARCH) 模型。作为实证说明，我们从风险规避主体的角度出发，采用建议的模型来评估由高收益股票指数（S&P 500）和两种避险投资工具（即黄金）组成的投资组合的未来风险。和美国国债期货）。我们遵循最近的建议，将预期缺口作为尾部风险的主要评估。为了准确评估新模型的优点，我们对 10 年来日回报率的风险预测进行了回测，针对不同的市场环境（例如 COVID-19 大流行）。我们发现 MS-C-MGARCH 模型在预测风险价值和预期缺口方面优于基准波动率模型（MGARCH、C-MGARCH）。当投资组合中相对风险资产的份额相对较大时，MS-C-MGARCH模型的优越性变得更强。