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本文针对金融市场的典型事实特征,运用自回归分数移动平均(Fractional Integrated Autoregressive Moving Average,ARFIMA)模型与双曲线记忆广义自回归条件异方差模型(Hyperbolic Memory Generalized Autoregressive Conditional Heteroscedasticity,HYGARCH)模型、分数协整非对称自回归条件异方差(Fractional Integrated Asymmetric Power Autoregressive Conditional Heteroscedasticity,FIAPARCH)模型和分数协整指数广义自回归条件异方差(Fractional Integrated Exponential Generalized Autoregressive Conditional Heteroscedasticity,FIEGARCH)模型结合,并运用有偏学生t分布(Skew Student t Distribution,SKST)来捕获金融收益分布形态,以此开展动态风险测度研究,进而运用返回测试(Back-Testing)中的似然比率测试(Likelihood Ratio Test,LRT)和动态分位数回归(Dynamic Quantile Regression,DQR)方法对风险模型的准确性与精度进行联合检验。通过实证研究,得到了一些非常有价值的实证结论:ARFIMA(1,d,1)-FIAPARCH(1,d,1)-SKST模型与ARFIMA(1,d,1)-HYGARCH(1,d,1)-SKST模型均表现出卓越的风险测度能力,但没有绝对优劣之分;ARFIMA(1,d,1)-FIEGARCH(1,d,1)-SKST模型在成熟市场的表现能力差强人意;本文引入的所有风险模型在中国大陆沪、深股市表现优越且没有实质性差异。
In this paper, according to the typical factual characteristics of financial markets, we use the Fractional Integrated Autoregressive Moving Average (ARFIMA) model and Hyperbolic Memory Generalized Autoregressive Conditional Heteroscedasticity (HYGARCH) model, Fractional Integrated Asymmetric Power Autoregressive Conditional Heteroscedasticity (FIAPARCH) model and Fractional Integrated Exponential Generalized Autoregressive Conditional Heteroscedasticity (FIEGARCH) model, t distribution (Skew Student t Distribution, SKST) to capture the distribution of financial returns in order to carry out dynamic risk measure research, and then use the Likelihood Ratio Test (LRT) and dynamic score in Back-Testing Dynamic Quantile Regression (DQR) method is used to test the accuracy and accuracy of the risk model. Through empirical research, some very valuable empirical conclusions are obtained: ARFIMA (1, d, 1) -FIAPARCH (1, d, 1) -SKST model and ARFIMA (1, d, 1) 1) -SKST models all show excellent risk measurement ability, but there is no absolute advantage or disadvantage; ARFIMA (1, d, 1) -FIEGARCH (1, d, 1) -SKST model is unsatisfactory in mature market; All of the risk models introduced in this paper are superior and not materially different in Shanghai and Shenzhen stock markets.