随机波动率估计的Riemann流形Langevin方法
Riemann Manifold Langevin Methods on Stochastic Volatility Estimation
摘要：
本文利用Metropolis调整Langevin（MALA）和Riemman流形Langevin（MMALA）方法对具有重尾分布的随机波动率模型进行贝叶斯估计。我们提供了这些方法的应用分析表达式，评估这些方法在模拟数据中的性能，并说明它们在两个金融时间序列数据集上的使用。
英文摘要：
In this paper we perform Bayesian estimation of stochastic volatility models with heavy tail distributions using Metropolis adjusted Langevin (MALA) and Riemman manifold Langevin (MMALA) methods. We provide analytical expressions for the application of these methods, assess the performance of these methodologies in simulated data and illustrate their use on two financial time series data sets.
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