Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
利用马尔可夫链蒙特卡洛方法为随机波动率模型提供统一的似然推断框架，开发了高效的一次性抽样所有未观测波动率的方法，并与GARCH模型进行拟合比较。
In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulation-based methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using non-nested likelihood ratios and Bayes factors is also investigated. These methods are used to compare the fit of stochastic volatility and GARCH models. All the procedures are illustrated in detail.