非对称结构下金融市场动态风险测度研究

针对金融市场呈现出的非对称结构,以新兴市场的中国大陆沪市上证综指(SSEC)和成熟市场的标准普尔指数(S&P500)作为代表性的研究对象,运用有偏学生分布(SKST)来刻画金融收益的有偏非对称分布形态;运用APARCH等模型来刻画金融收益条件波动率的非对称波动性,并以此来开展风险测度研究;最后运用返回测试中LRT和DQR来检验风险测度的准确性。实证结果表明:没有哪种金融收益的条件非对称波动模型具有绝对优越的风险测度能力;在标准收益服从的分布上,新兴市场SSEC与成熟市场S&P500市场却又表现出明显的不同,Normal分布并不适合SSEC,但能适合S&P500;ST能够适应SSEC,却不能适应S&P500,而SKST能够适应两种市场;对于S&P500,在99%这样高的置信水平下是Normal优越,而在95%的置信水平下却是SKST优秀,对于SSEC,SKST分布的准确性在两个水平下都是最高。

Study on Risk Measure of Financial Market Risk with Asymmetric Structure

In the paper,which draws upon Chinese Shanghai Stock Exchange Composite Index(SSEC) typical of emerging market and S&P500 index typical of developed financial market,we use skew student distribution(SKST) to model financial conditional return,use APARCH to model asymmetry conditional volatility,then use both to construct a risk measure model,and finally use back testing technology to test the accuracy of different risk models.Our results indicate that no one asymmetric conditional volatility risk model is superior to another.Normal distribution does not fit SSEC,but fit S&P500;ST fits S&P500,not SSEC;SKST is superior to ST and ST is superior to Normal distribution for SSEC,but for S&P500,ST does not fit and SKST is superior for both.