基于独立成分分解的多元波动率模型  

Multivariate volatilities modeling based on independent components

作　　者：王明进[1] 陈奇志[1] 

机构地区：[1]北京大学光华管理学院,北京100871

出　　处：《管理科学学报》2006年第5期56-64,共9页Journal of Management Sciences in China

基　　金：国家自然科学基金资助项目(70201007)

摘　　要：利用独立成分分解(ICA)提出了一类新的多元波动率模型———IC-GARCH模型.通过研究上海A、B股以及亚洲5个股票市场等两个真实数据的例子,进一步分析比较了该模型与Morgan在RiskmetricsTM中使用的EWMA模型和基于主成分分解的O-GARCH模型的拟合效果.Multivariate volatility model play an important role in portfolio construction, asset pricing and risk management. In practice, since a large number of assets are considered simultaneously, the two most common models are the exponential weighted moving average （EWMA） model suggested in J.P. Morgan＇ s RiskmetricsTM and orthogonal GARCH （O-GARCH） model based on principal component analysis （PCA） of the return series. However, the assumptions used in both models are too restrictive. For instance, principal components （PCs） are unconditionally uncorrelated but not necessarily conditionally correlated, so their conditional covariance matrix may not be diagonal and O-GARCH model is not reliable in this sense. This paper puts forward a new multivariate volatility model, i.e., IC-GARCH model, based on the so-called independent component analysis （ICA）. It is expected that the conditional covariance matrix of ICs may look more like a diagonal one than that of PCs, which hopefully can remedy the defect of O-GARCH model. Two real data sets are used to illustrate the power of IC-GARCH model. The results from two mis-specification tests both demonstrate the advantage of IC-GARCH model over EWMA and O-GARCH models.

关 键 词：独立成分分解 IC-GARCH模型 O-GARCH模型 条件相关系数 

分 类 号：F830[经济管理—金融学]