Multivariate Stochastic Volatility Models with Correlated Errors
Multivariate Stochastic Volatility Models with Correlated Errors
复制标题
具有相关误差的多元随机波动率模型
DOI：
10.1080/07474930600713309
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发表时间：
2005
影响因子：
1.2
通讯作者：
Chris Kirby
中科院分区：
文献类型：
--
作者：
David Chan;R. Kohn;Chris Kirby
本文提出了一种贝叶斯方法，用于简约地估计多变量随机波动模型中误差的相关结构。由于在联合相关矩阵的回报率和波动性误差的参数的数量可能是非常大的，我们施加一个先验，允许非对角元素的逆相关矩阵是相同的零。该模型估计使用马尔可夫链模拟方法，从后验分布的波动率和参数的样本。我们用模拟和真实的例子来说明这种方法。在真实的例子中，该方法被应用于三个层次的股票：同一行业内公司的回报，不同行业的回报，以及在指数水平上的回报。我们发现，只有在最高水平的聚集显着的相关性效应。
We develop a Bayesian approach for parsimoniously estimating the correlation structure of the errors in a multivariate stochastic volatility model. Since the number of parameters in the joint correlation matrix of the return and volatility errors is potentially very large, we impose a prior that allows the off-diagonal elements of the inverse of the correlation matrix to be identically zero. The model is estimated using a Markov chain simulation method that samples from the posterior distribution of the volatilities and parameters. We illustrate the approach using both simulated and real examples. In the real examples, the method is applied to equities at three levels of aggregation: returns for firms within the same industry, returns for different industries, and returns aggregated at the index level. We find pronounced correlation effects only at the highest level of aggregation.