Stochastic volatility with leverage: Fast and efficient likelihood inference
Stochastic volatility with leverage: Fast and efficient likelihood inference
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DOI：
10.1016/j.jeconom.2006.07.008
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发表时间：
2007-10-01
影响因子：
6.3
通讯作者：
Nakajima, Jouchi
中科院分区：
文献类型：
--
作者：
Omori, Yasuhiro;Chib, Siddhartha;Nakajima, Jouchi
本文研究杠杆作用下随机波动率(SV)模型的贝叶斯分析。具体地说，本文展示了经常使用的Kim等人是如何。[1998年。随机波动率：似然推断及与ARCH模型的比较。经济研究回顾65,361-393]为无杠杆的SV模型开发的方法可以推广到有杠杆的模型。该方法依赖于一种新的想法，即用适当构造的二元正态分布的十分量混合来逼近结果和波动率新息的联合分布。所得到的后验分布由MCMC方法总结，并通过重新加权过程来修正混合近似中的小的逼近误差。整个过程快速高效。我们对东京股票价格指数的日收益进行了说明。最后，将该方法推广到叠加模型(其中对数波动率由异质和独立自回归的线性组合组成)和重尾误差分布(学生分布和对数正态分布)。(C)2006爱思唯尔B.V.保留所有权利。
This paper is concerned with the Bayesian analysis of stochastic volatility (SV) models with leverage. Specifically, the paper shows how the often used Kim et al. [1998. Stochastic volatility: likelihood inference and comparison with ARCH models. Review of Economic Studies 65, 361-393] method that was developed for SV models without leverage can be extended to models with leverage. The approach relies on the novel idea of approximating the joint distribution of the outcome and volatility innovations by a suitably constructed ten-component mixture of bivariate normal distributions. The resulting posterior distribution is summarized by MCMC methods and the small approximation error in working with the mixture approximation is corrected by a reweighting procedure. The overall procedure is fast and highly efficient. We illustrate the ideas on daily returns of the Tokyo Stock Price Index. Finally, extensions of the method are described for superposition models (where the log-volatility is made up of a linear combination of heterogenous and independent auto regressions) and heavy-tailed error distributions (student and log-normal). (c) 2006 Elsevier B.V. All rights reserved.