Stochastic Covariance Models
Stochastic Covariance Models
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DOI：
10.2139/ssrn.1673764
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发表时间：
2010-08
期刊：
影响因子：
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通讯作者：
Manabu Asai;Mike K. P. So
中科院分区：
文献类型：
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作者：
Manabu Asai;Mike K. P. So
提出了一类新的基于Wishart分布的随机协方差模型。根据时变协方差矩阵的形式以及它是否是一个潜在变量，本文介绍了三种动态相关模型。一个随机协方差滤波器也被开发用于过滤和预测协方差。基本模型的扩展使得能够研究动态相关性、阈值相关性效应和投资组合分析的长记忆特性。随机协方差模型和随机协方差滤波器中的适当参数化有助于高效计算高维问题中的似然函数，无论协方差矩阵是可观察的还是潜在的。蒙特卡洛实验研究有限样本性质的最大似然估计进行。两个实证的例子。一个处理高频汇率数据的已实现协方差，而另一个检查每日股票收益率。
A new class of stochastic covariance models based on Wishart distribution is proposed. Three categories of dynamic correlation models are introduced depending on how the time-varying covariance matrix is formulated and whether or not it is a latent variable. A stochastic covariance filter is also developed for filtering and predicting covariances. Extensions of the basic models enable the study of the long memory properties of dynamic correlations, threshold correlation effects and portfolio analysis. Suitable parameterization in the stochastic covariance models and the stochastic covariance filter facilitate efficient calculation of the likelihood function in high-dimensional problems, no matter whether the covariance matrix is observable or latent. Monte Carlo experiments investigating finite sample properties of the maximum likelihood estimator are conducted. Two empirical examples are presented. One deals with the realized covariance of high frequency exchange rate data, while the other examines daily stock returns.