We provide a simple method to estimate the parameters of multivariate stochastic volatility models with latent factor structures. These models are very useful as they alleviate the standard curse of dimensionality, allowing the number of parameters to increase only linearly with the number of the return series. Although theoretically very appealing, these models have only found limited practical application due to huge computational burdens. Our estimation method is simple in implementation as it consists of two steps: first, we estimate the loadings and the unconditional variances by maximum likelihood, and then we use the efficient method of moments to estimate the parameters of the stochastic volatility structure with GARCH as an auxiliary model. In a comprehensive Monte Carlo study we show the good performance of our method to estimate the parameters of interest accurately. The simulation study and an application to real vectors of daily returns of dimensions up to 148 show the method's computation advantage over the existing estimation procedures.
Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models
通过因子随机波动模型讨论了多元时间序列中动态协方差阵的有效贝叶斯估计。特别地，我们提出了两种交织策略（Yu和Meng，Journal of Computational and Graphical Statistics，20（3），531-570，2011）以显著加速标准MCMC方法的收敛和混合。类似于边际数据增强技术，建议的加速程序利用非可识别性问题，经常出现在因素模型。我们的新交织策略很容易实现，几乎没有额外的计算成本，然而，它们可以提高估计效率的几个数量级，如在广泛的模拟研究中所示。最后，我们的算法应用到26维汇率数据集说明了优越的性能，新的方法对现实世界的数据。
