英文标题：
《Multivariate stochastic volatility modelling using Wishart
autoregressive processes》
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作者：
K. Triantafyllopoulos
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最新提交年份：
2013
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英文摘要：
A new multivariate stochastic volatility estimation procedure for financial time series is proposed. A Wishart autoregressive process is considered for the volatility precision covariance matrix, for the estimation of which a two step procedure is adopted. The first step is the conditional inference on the autoregressive parameters and the second step is the unconditional inference, based on a Newton-Raphson iterative algorithm. The proposed methodology, which is mostly Bayesian, is suitable for medium dimensional data and it bridges the gap between closed-form estimation and simulation-based estimation algorithms. An example, consisting of foreign exchange rates data, illustrates the proposed methodology.
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中文摘要：
提出了一种新的金融时间序列多元随机波动率估计方法。对于波动率精度协方差矩阵，考虑了Wishart自回归过程，其估计采用了两步程序。第一步是对自回归参数的条件推理，第二步是基于牛顿-拉斐逊迭代算法的无条件推理。所提出的方法主要是贝叶斯方法，适用于中维数据，它弥补了封闭形式估计和基于仿真的估计算法之间的差距。一个由汇率数据组成的例子说明了所提出的方法。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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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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