多维条件方差偏度峰度建模  

Modeling multivariate conditional variance skewness kurtosis

作　　者：王春峰[1] 庄泓刚[1] 房振明[1] 卢涛[1] 

机构地区：[1]天津大学管理学院金融工程研究中心,天津300072

出　　处：《系统工程理论与实践》2010年第2期324-331,共8页Systems Engineering-Theory & Practice

基　　金：国家自然科学基金(70771076)

摘　　要：为了考察多个市场或多个金融资产之间的高阶矩风险度量问题,有效地捕获收益率时间序列高阶矩动态特征,在考虑当前预期和波动性条件下,推导了高阶中心矩和协矩之间的关系,提出了能够有效解决维数灾祸问题的多维条件高阶矩模型.在多维S_U分布基础上,采用动态条件相关性(DCC)和自回归条件密度技术,通过智能优化算法对条件高阶矩模型的时变参数进行估计.实证研究结果表明,多维条件高阶矩模型较好的拟合了收益率时间序列高阶矩动态特征,与之前的高阶矩模型相比,能够有效解决高阶矩模型的维数灾祸问题,表明该模型能够捕捉到我国多个市场之间高阶矩风险特征,提高多维条件高阶矩模型测度能力.Considering factors of anticipation and volatility, to measure the dynamic character of higher moments risk and investigate impacts of the risk on multi-financial markets or assets, a model of multivariate conditional higher order moments, which can solve the problem of ＇dimension disaster＇, was proposed with the determination of the formulas between moments and co-moments. Time-varying parameters of higher order moments were estimated using Dynamic Conditional Correlation, Autoregressive conditional density and intelligence optimization algorithm on distribution. The analysis on models shows that model. of multivariate conditional higher order moments is better fitted with the feature of higher order moments of return series. Comparing our model with others, the model perform well in solving the problem of ＇dimension disaster＇, which has implication that our model can catch the risk character of Chinese multi-markets and improve estimation in multivariate conditional higher order moments.

关 键 词：高阶矩 SU分布 动态条件相关性 自回归条件密度 

分 类 号：F830.91[经济管理—金融学]