摘要翻译：
本文提出了一种估计和预测多元时间序列波动率的贝叶斯方法。本文工作的基础是矩阵变量动态线性模型，对其波动率采用乘性随机演化，使用Wishart分布和奇异多元贝塔分布。采用折现因子的对角矩阵来逐元素折现方差，从而允许一种灵活实用的方差建模方法。详细讨论了诊断测试和顺序模型监测。将所提出的估计理论应用于包括伦敦金属交易所铝、铜、铅和锌现货价格的四维时间序列。实证结果表明，所提出的贝叶斯方法可以有效地应用于金融数据，克服了现有波动率模型的许多缺点。
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英文标题：
《Multivariate stochastic volatility with Bayesian dynamic linear models》
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作者：
K. Triantafyllopoulos
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最新提交年份：
2008
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrix-variate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multivariate beta distributions. A diagonal matrix of discount factors is employed in order to discount the variances element by element and therefore allowing a flexible and pragmatic variance modelling approach. Diagnostic tests and sequential model monitoring are discussed in some detail. The proposed estimation theory is applied to a four-dimensional time series, comprising spot prices of aluminium, copper, lead and zinc of the London metal exchange. The empirical findings suggest that the proposed Bayesian procedure can be effectively applied to financial data, overcoming many of the disadvantages of existing volatility models.
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PDF链接：
https://arxiv.org/pdf/0802.0214