Markov Switching Garch Models: Higher Order Moments, Kurtosis Measures, and Volatility Evaluation in Recessions and Pandemic
推导了单变量马尔可夫转换GARCH模型高阶矩和峰度的闭式矩阵公式，提供了样本估计量的渐近理论，并通过蒙特卡洛模拟验证，最后用理论结果识别金融危机和疫情等股市高波动时期。
In this article, we derive neat matrix formulas in closed form for computing higher order moments and kurtosis of univariate Markov switching GARCH models. Then we provide asymptotic theory for sample estimators of higher order moments and kurtosis which can be used for testing normality. We also check our theory statements numerically via Monte Carlo simulations. Finally, we take advantage of our theoretical results to recognize different periods of high volatility stressing the stock markets, such as financial crisis and pandemic.