Multivariate stochastic volatility
Multivariate stochastic volatility
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DOI：
10.1007/978-3-540-71297-8_16
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
S. Chib;Yasuhiro Omori;Manabu Asai
中科院分区：
文献类型：
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作者：
S. Chib;Yasuhiro Omori;Manabu Asai
我们提供了一个详细的总结大型和充满活力的新兴文献，涉及金融时间序列的条件波动率的随机波动率的框架内的多变量建模。这一领域的发展和成就是金融计量经济学最成功的案例之一。多元随机波动率模型有三大类：一类是单变量随机波动率模型的直接扩展，另一类与多元分析的因子模型有关，第三类是基于通过矩阵指数变换、Wishart过程和其他方法对时变相关矩阵进行直接建模。我们讨论每一个不同的模型配方，提供连接和差异，并显示如何估计模型。鉴于在这一领域的兴趣，进一步的重大发展可以预期，也许促进概述和本文中描述的细节，特别是在高维模型的拟合。
We provide a detailed summary of the large and vibrant emerging literature that deals with the multivariate modeling of conditional volatility of financial time series within the framework of stochastic volatility. The developments and achievements in this area represent one of the great success stories of financial econometrics. Three broad classes of multivariate stochastic volatility models have emerged: one that is a direct extension of the univariate class of stochastic volatility model, another that is related to the factor models of multivariate analysis and a third that is based on the direct modeling of time-varying correlation matrices via matrix exponential transformations, Wishart processes and other means. We discuss each of the various model formulations, provide connections and differences and show how the models are estimated. Given the interest in this area, further significant developments can be expected, perhaps fostered by the overview and details delineated in this paper, especially in the fitting of high-dimensional models.