A Maximum Likelihood Approach for Non-Gaussian Stochastic Volatility Models
提出一种基于递归数值积分的最大似然方法，用于估计随机波动率模型，该方法简单灵活且性能与现有最佳工具相当。新模型在股票指数数据中表现最优，其波动率创新分布高度非高斯，优于包含自回归条件异方差或条件均值中随机波动率效应的模型。
Abstract A maximum likelihood approach for the analysis of stochastic volatility models is developed. The method uses a recursive numerical integration procedure that directly calculates the marginal likelihood. Only conventional integration techniques are used, making this approach both flexible and simple. Experimentation shows that the method matches the performance of the best estimation tools currently in use. New stochastic volatility models are introduced and estimated. The model that best fits recent stock-index data is characterized by a highly non-Gaussian stochastic volatility innovation distribution. This model dominates a model that includes an autoregressive conditional heteroscedastic effect in the stochastic volatility process and a model that includes a stochastic volatility effect in the conditional mean. KEY WORDS: Filtering and smoothingHeteroscedasticityNon-Gaussian filteringNumerical integrationStochastic variance