论文标题
因子随机波动率模型的变异近似
Variational Approximation of Factor Stochastic Volatility Models
论文作者
论文摘要
高维多元因子随机波动率模型中的估计和预测是一个重要而活跃的研究领域，因为这样的模型允许多元随机波动率的简约表示。贝叶斯对因子随机波动率模型的推断通常是由马尔可夫链蒙特卡洛法（Markov Carlo Carlo）进行的，通常是由Markov Chain Monte Carlo进行的，由于涉及大量参数和潜在状态，因此对于高维或长时间序列而言，通常对高维或长时间序列进行缓慢。我们的文章做出了两个贡献。首先是提出快速准确的变分贝叶斯方法，以近似于因子随机波动率模型中状态和参数的后验分布。第二个贡献是扩展这种批处理方法，以开发随着新观察的到来，预测快速顺序变化更新。与最新的粒子马尔可夫链蒙特卡洛方法相比，该方法应用于模拟和真实数据集，并显示出可产生良好的近似推断和预测，但要快得多。
Estimation and prediction in high dimensional multivariate factor stochastic volatility models is an important and active research area because such models allow a parsimonious representation of multivariate stochastic volatility. Bayesian inference for factor stochastic volatility models is usually done by Markov chain Monte Carlo methods, often by particle Markov chain Monte Carlo, which are usually slow for high dimensional or long time series because of the large number of parameters and latent states involved. Our article makes two contributions. The first is to propose fast and accurate variational Bayes methods to approximate the posterior distribution of the states and parameters in factor stochastic volatility models. The second contribution is to extend this batch methodology to develop fast sequential variational updates for prediction as new observations arrive. The methods are applied to simulated and real datasets and shown to produce good approximate inference and prediction compared to the latest particle Markov chain Monte Carlo approaches, but are much faster.
