Monte Carlo Likelihood Estimation for Three Multivariate Stochastic Volatility Models
展示了如何用重要性抽样方法对三种多元随机波动率模型进行蒙特卡洛最大似然估计，并通过标准数据集和模拟实验验证了方法的有效性。
Estimating parameters in a stochastic volatility (SV) model is a challenging task. Among other estimation methods and approaches, efficient simulation methods based on importance sampling have been developed for the Monte Carlo maximum likelihood estimation of univariate SV models. This paper shows that importance sampling methods can be used in a general multivariate SV setting. The sampling methods are computationally efficient. To illustrate the versatility of this approach, three different multivariate stochastic volatility models are estimated for a standard data set. The empirical results are compared to those from earlier studies in the literature. Monte Carlo simulation experiments, based on parameter estimates from the standard data set, are used to show the effectiveness of the importance sampling methods.