基于GAS t-Copula模型的金融市场非对称风险溢出效应测度研究

准确测度金融风险溢出效应对于金融风险管理和构建投资组合具有重要意义,而金融市场之间的非线性及动态相关结构一直是风险溢出效应研究中的难点问题之一。本文通过引入GAS t-copula模型与CoVaR方法,结合能够刻画重要典型事实特征的边缘分布模型,构建了金融市场间的风险溢出效应测度模型,以中国内地等五个股市为研究对象,测度美国股市对中国内地等四个重要股市的风险溢出效应,以检验模型的可靠性与准确性。实证结果表明:中国内地等四个股市与美国股市之间呈现出显著为正且时变相关结构,随着金融危机的爆发,相关系数逐渐增加达到最大值;中国内地等四个股市受到美国股市的风险溢出效应呈现出非对称特征,即下跌风险溢出效应强度显著大于上涨风险溢出效应;中国内地股市受到的金融风险溢出效应显著小于香港、日本以及英国股市。     

基于t-Copula的一篮子信用违约互换定价模型

为了刻画分布函数的厚尾特征和违约的传染性,构建了单因子t-Copula模型,以此研究一篮子信用违约互换(BDS)的定价问题。依据风险中性定价原理和顺序统计量方法,分别得到了第k次违约和n个参照实体中m个受保护的BDS价格的解析式.为了说明定价模型的有效性,用随机模拟方法分析了相应的数值算例.     

二元响应模型的选择

本文在中、小样本试验数据下,研究响应模型的选择问题.文中给出了一个四参数分布族,变化其中两个参数的取值可以得到四种常见的响应分布模型.从而,将四种常见响应分布的选择问题,转化为该分布族的参数检验问题.     

贝叶斯分类模型的改进及其应用

利用加权贝叶斯分类模型对北京科技大学本科生英语四级考试通过率进行预测.通过对误判数据的分析,调整贝叶斯分类器的判别条件,改进了加权贝叶斯分类模型.实验表明,改进后的模型大大提高了预测结果的准确性.此外,还引入了学生"潜力因子"的概念,为教学与学习提供了个性化的提示和有针对性的建议.     

一个捕捉与再捕捉模型的贝叶斯分析

本文在无信息先验和Jeffreys先验下 ,就捕捉与再捕捉试验和多次重复的捕捉与再捕捉试验两种情况 ,推导了封闭总体中个体总数N的贝叶斯点估计与区间估计 ,并计算了一个实例     

A Bayesian Model for Root Computation

A univariate polynomial over the real or the complex numbers is given approximately. We present a Bayesian method for the computation of the posterior probabilities of different multiplicity patterns. The method is based on interpreting the root computation problem as an inverse problem which is then treated in the Bayesian framework. The method can be used to select the most probable multiplicity pattern when the coefficients of a univariate polynomial are not known exactly. The method is illustrated by several numerical examples.     

一个贝叶斯离散可靠性增长模型(英)

本文提出了一个贝叶斯离散可靠性增长模型．假设一个产品的开发过程由m个阶段组成．在每一个阶段中，都进行一个成败型寿命试验．在试验结束后，再分析其结果，然后对产品进行修改或重新设计，以期提高产品的可靠性．如果产品的失效可分为不可修复的以及可修复的两种．假定产品的不可修复失效概率在各个阶段中保持相同，而可修复失效概率随着试验阶段的增加而减少．     

Semiparametric Bayesian Regression via Potts Model

We consider Bayesian nonparametric regression through random partition models. Our approach involves the construction of a covariate-dependent prior distribution on partitions of individuals. Our goal is to use covariate information to improve predictive inference. To do so, we propose a prior on partitions based on the Potts clustering model associated with the observed covariates. This drives by covariate proximity both the formation of clusters, and the prior predictive distribution. The resulting prior model is flexible enough to support many different types of likelihood models. We focus the discussion on nonparametric regression. Implementation details are discussed for the specific case of multivariate multiple linear regression. The proposed model performs well in terms of model fitting and prediction when compared to other alternative nonparametric regression approaches. We illustrate the methodology with an application to the health status of nations at the turn of the 21st century. Supplementary materials are available online.     

线性回归模型的Bayes统计推断

Bayes方法虽融合了样本信息和先验信息，但利用的先验信息都是有历史经验和专家估计所得，因此可靠度不高。该文研究了正态线性回归模型：Y=Xβ＋e，e—N（0，σ^2。L），其中σ^2已知，β为未知参数向量，对传统的Bayes方法进行了改进，即把Bayes方法中的后验信息作为改进Bayes的无验信息并融合样本信息进行统计推断，在二次损失函数下得到了β的改进的Bayes估计。由于改进的Bayes方法的先验信息中有样本信息，因此其准确度比传统的Bayes方法准确度更高。     

A Bayesian Model of Panic in Belief

One common principle in the study of belief is what has been called the “consensual validation of reality”: the idea that persons in highly inbred social networks alter their beliefs regarding the external world by repeated interaction with each other rather than by direct observation. This notion accounts for phenomena such as panics, in which a substantial number of actors in a given population suddenly converge to (typically unsubstantiated) beliefs. In this paper, a Bayesian conditional probability model will be used to explore the conditions necessary for such outcomes, and alternative results will be likewise documented. Finally, suggestions for operationalization of the Bayesian model in experimental research will be given, along with some implications of the theory for common phenomena such as the propagation of ideas by media sources, organizational rumors, and polarization of group opinion.     

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A Bayesian semiparametric procedure for confirmatory factor analysis model is proposed to address the heterogeneity of the multivariate responses. The approach relies on the use of a prior over the space of mixing distributions with finite components. Blocked Gibbs sampler is implemented to cope with the posterior analysis. For model comparison, themeasure and Bayes factor are developed. A generalized weighted Chinese restaurant algorithm is suggested to compute the likelihood of data. Empirical results are presented to illustrate the effectiveness of the methodologies.     

Bayesian Inference in the Noncentral Student-t Model

This article takes up Bayesian inference in linear models with disturbances from a noncentral Student-t distribution. The distribution is useful when both long tails and asymmetry are features of the data. The distribution can be expressed as a location-scale mixture of normals with inverse weights distributed according to a chi-square distribution. The computations are performed using Gibbs sampling with data augmentation. An empirical application to Standard and Poor's stock returns indicates that posterior odds strongly favor a noncentral Student-t specification over its symmetric counterpart.     

Bayesian Ising Graphical Model for Variable Selection

In this article, we propose a new Bayesian variable selection (BVS) approach via the graphical model and the Ising model, which we refer to as the “Bayesian Ising graphical model” (BIGM). The BIGM is developed by showing that the BVS problem based on the linear regression model can be considered as a complete graph and described by an Ising model with random interactions. There are several advantages of our BIGM: it is easy to (i) employ the single-site updating and cluster updating algorithm, both of which are suitable for problems with small sample sizes and a larger number of variables, (ii) extend this approach to nonparametric regression models, and (iii) incorporate graphical prior information. In our BIGM, the interactions are determined by the linear model coefficients, so we systematically study the performance of different scale normal mixture priors for the model coefficients by adopting the global-local shrinkage strategy. Our results indicate that the best prior for the model coefficients in terms of variable selection should place substantial weight on small, nonzero shrinkage. The methods are illustrated with simulated and real data. Supplementary materials for this article are available online.     

Regression Estimation from Grouped Observations

Journal of the Operational Research Society -     

Bayesian Inference for the One-Factor Copula Model

We develop efficient Bayesian inference for the one-factor copula model with two significant contributions over existing methodologies. First, our approach leads to straightforward inference on dependence parameters and the latent factor; only inference on the former is available under frequentist alternatives. Second, we develop a reversible jump Markov chain Monte Carlo algorithm that averages over models constructed from different bivariate copula building blocks. Our approach accommodates any combination of discrete and continuous margins. Through extensive simulations, we compare the computational and Monte Carlo efficiency of alternative proposed sampling schemes. The preferred algorithm provides reliable inference on parameters, the latent factor, and model space. The potential of the methodology is highlighted in an empirical study of 10 binary measures of socio-economic deprivation collected for 11,463 East Timorese households. The importance of conducting inference on the latent factor is motivated by constructing a poverty index using estimates of the factor. Compared to a linear Gaussian factor model, our model average improves out-of-sample fit. The relationships between the poverty index and observed variables uncovered by our approach are diverse and allow for a richer and more precise understanding of the dependence between overall deprivation and individual measures of well-being.     

On the Bayesian Mixture Model and Identifiability

This article is concerned with Bayesian mixture models and identifiability issues. There are two sources of unidentifiability: the well-known likelihood invariance under label switching and the perhaps less well-known parameter identifiability problem. When using latent allocation variables determined by the mixture model, these sources of unidentifiability create arbitrary labeling that renders estimation of the model very difficult. We endeavor to tackle these problems by proposing a prior distribution on the allocations, which provides an explicit interpretation for the labeling by removing gaps with high probability. We propose a Markov chain Monte Carlo (MCMC) estimation method and present supporting illustrations.     

基于MCMC模拟的贝叶斯厚尾金融随机波动模型分析

针对现有金融时间序列模型建模方法难以刻画模型参数的渐变性问题,利用贝叶斯分析方法构建贝叶斯厚尾SV模型。首先对反映波动性特征的厚尾金融随机波动模型(SV-T)进行贝叶斯分析,构造了基于Gibbs抽样的MCMC数值计算过程进行仿真分析,并利用DIC准则对SV-N模型和SV-T模型进行优劣比较。研究结果表明:在模拟我国股市的波动性方面,SV-T模型比SV-N模型更优,更能反应我国股市的尖峰厚尾的特性,并且证明了我国股市具有很强的波动持续性。     

出行选择行为的演化博弈分析

为缓解城市交通拥堵,通过分析出行者出行选择方式的博弈心理,建立了交管部门与出行者策略选择的决策博弈模型,应用演化博弈的思想分析了交管部门对私家车管理与不管理、出行者选择私家车与选择公交车出行各自两种策略选择的演化过程.根据雅克比矩阵的局部稳定性,对演化稳定状态进行了分析,并对一些主要参数对选择行为的影响进行了研究,结果表明,交管部门对公交车的激励效应以及对私家车管理效应对出行者出行方式选择行为的演化起着至关重要的作用.在讨论过程中,对各种情况做了详细地分析和解释,可以为政府决策部门提供相应的参考.