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雷达卡
摘要翻译：
基于协整向量自回归(CVAR)模型，我们考虑了一个对交易资产的统计模型。我们扩展了标准CVAR模型，在价格序列水平移动存在的情况下，将模型参数的估计纳入其中，这些价格序列水平移动在标准高斯误差修正模型(ECM)框架中没有准确地建模。这包括在ECM框架中开发一个新的矩阵变量贝叶斯CVAR混合模型，该模型包含日内高斯误差和日内alpha稳定误差。为了实现这一点，我们导出了一个新的共轭后验模型，用于alpha稳定的日间新息的标度法线混合(SMiN，CVAR)表示。这些结果被推广到日间边界新息噪声的非对称模型，允许倾斜的alpha稳定模型。我们提出的模型和抽样方法是通用的，结合了当前关于高斯模型的文献作为一个特殊的子类，还允许价格序列水平在随机估计的时间点或先验已知的时间点上移动。我们的重点分析是经常观察到的非高斯水平移动，这些水平移动可能对统计模型中的估计性能有显著影响，但未能考虑这种水平移动，如在市场收盘和开盘时。当单个序列（如日间边界）中存在非高斯价格序列水平移动时，我们比较了我们的模型和估计方法与标准频率和贝叶斯CVAR模型的估计精度。我们将一个双变量alpha稳定模型拟合到日间跳跃，并使用基于似然的Johansen过程和贝叶斯估计来模拟这种跳跃对矩阵变量CVAR模型参数估计的影响。我们说明了我们的模型和相应的估计程序，我们开发的综合和实际数据。
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英文标题：
《Bayesian Cointegrated Vector Autoregression models incorporating
Alpha-stable noise for inter-day price movements via Approximate Bayesian
Computation》
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作者：
Gareth W. Peters, Balakrishnan B. Kannan, Ben Lasscock, Chris Mellen
and Simon Godsill
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最新提交年份：
2010
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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：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：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：Computation 计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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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：
We consider a statistical model for pairs of traded assets, based on a Cointegrated Vector Auto Regression (CVAR) Model. We extend standard CVAR models to incorporate estimation of model parameters in the presence of price series level shifts which are not accurately modeled in the standard Gaussian error correction model (ECM) framework. This involves developing a novel matrix variate Bayesian CVAR mixture model comprised of Gaussian errors intra-day and Alpha-stable errors inter-day in the ECM framework. To achieve this we derive a novel conjugate posterior model for the Scaled Mixtures of Normals (SMiN CVAR) representation of Alpha-stable inter-day innovations. These results are generalized to asymmetric models for the innovation noise at inter-day boundaries allowing for skewed Alpha-stable models. Our proposed model and sampling methodology is general, incorporating the current literature on Gaussian models as a special subclass and also allowing for price series level shifts either at random estimated time points or known a priori time points. We focus analysis on regularly observed non-Gaussian level shifts that can have significant effect on estimation performance in statistical models failing to account for such level shifts, such as at the close and open of markets. We compare the estimation accuracy of our model and estimation approach to standard frequentist and Bayesian procedures for CVAR models when non-Gaussian price series level shifts are present in the individual series, such as inter-day boundaries. We fit a bi-variate Alpha-stable model to the inter-day jumps and model the effect of such jumps on estimation of matrix-variate CVAR model parameters using the likelihood based Johansen procedure and a Bayesian estimation. We illustrate our model and the corresponding estimation procedures we develop on both synthetic and actual data.
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PDF链接：
https://arxiv.org/pdf/1008.0149
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