高阶矩波动性建模及应用

为度量高阶矩风险的动态特征、考察时变高阶矩风险对金融投资决策的影响，本文提出了一个新的高阶矩波动模型：NAGARCHSK-M模型。讨论了该模型的包容性，给出了关于高阶矩波动性建模的一整套建模技术，基于正态密度的Gram-Charlier展开给出了模型的参数估计方法。利用该模型对我国股市的高阶矩风险进行了动态描述，并讨论了时变方差风险、时变偏度风险和时变峰度风险对资产收益的影响。     

贝叶斯视角下时变参数VAR建模——兼论“斜率之谜”

中国渐进式的改革实践要求中国宏观时间序列的建模能够允许参数平滑变化,而传统的VAR模型对此无能为力。本文详细阐述了在贝叶斯估计框架下,如何利用MCMC算法,建立时变参数VAR模型的过程,并利用该模型对徐高(2008)的数据重新进行了拟合,发现其文中提出的斜率之谜现象不复存在,因此时变参数VAR模型在拟合中国宏观时间序列方面更为精准。     

基于DCC模型的上市银行系统风险实证研究

本文应用DCC多元GARCH模型分析上市银行股票价格的动态相关性,并以此作为银行整体风险的度量监测指标,在模型的构建中考虑了系统风险的时变特征。结果表明,相关系数的大小和动态变化能够对银行系统性风险起到一定的监测和预警作用。本次金融危机过后,银行间动态相关水平一直处于高位,表明投资者对未来银行资产质量和其潜在风险仍然存在担忧。     

一个新的时变系数分位数回归模型及应用

结合普通分位数回归的模型结构和可行性最小二乘方法的时变系数特征,在普通分位数回归模型的损失函数中引入动态误差设定,提出了一个新的模型：时变系数分位数回归模型,并给出其模型表示、模型估计以及模型检验等建模方法。时变系数分位数回归模型更能够适应广泛数据类型的建模需求,体现回归系数的时变特征,揭示解释变量对响应变量完整条件分布特征的影响,具有广阔的应用前景。将其应用于组合投资决策分析,构造出VaR风险动态组合投资方案,并与VaR风险静态组合投资方案、方差风险静态组合投资方案、方差风险动态组合投资方案等进行实证比较。结果表明,基于时变系数分位数回归模型的VaR风险动态组合投资方案所得投资效果在收益、方差、Sharpe比率和VaR数值等方面都显著优于其他三种方案。     

基于关系风险的物流外包风险实证研究

通过结构模型假设和样本调查,研究了关系风险如何影响物流供应商受托的资产和胜任能力,在统计分析的基础上,找出了导致物流外包关系破裂的潜在风险因素以及其结构关系,并利用信度、效度检测方法和测量模型测试了研究结果。研究发现,物流外包中关系风险对资产风险、胜任能力风险以及资产风险对胜任能力风险有积极显著的影响,且能力风险的影响比资产风险稍微显著。研究还发现,物流服务供应商缺乏维持较好的运输经营和损失控制能力是批发商最直接相关的风险;资产风险与能力风险显著正相关,与内部治理成本相比,批发商更关心不可估量的专用资产。     

基于结构模型的物流外包内在风险关系研究

利用结构模型方法,选取浙江省农副产品经销商为对象,实证检验了物流外包内在风险的关系。研究表明,物流外包的合作风险、资产风险和胜任力风险之间存在密切关系,物流外包的合作风险能同时对资产风险和胜任力风险产生显著的影响,而且资产风险也能显著作用于胜任力风险。     

管理浮动机制下的人民币汇率行为分析——基于贝叶斯MCMC推断法与ESVDJ模型

本文提出一种汇率行为的理论模型——ESVDJ模型,并对该模型的估计设计出贝叶斯MCMC推断法。实证研究表明,在管理浮动汇率机制下,人民币汇率的日常波动持续地维持在较小范围。但如果市场供求双方发生显著的失衡,人民币汇率将产生跳跃行为,由此引发异常的汇率风险。此外,外汇市场并不显著地存在类似于权益市场的杠杆效应。本文同时对ESVDJ模型与通常的随机波动性模型SV及SVJ模型进行对比。MCMC似然比检验与密度函数非参数估计表明,后两者存在较大的设定错误。本文最后对汇率风险监管提出政策建议。     

有序选择因子结构模型的MCMC估计及应用

为了评估中国分布类政策效应,根据中国微观数据的变量可得性,本文在Heckman等构建的因子结构模型基础上,将Heckman基准模型中的连续型测度方程调整为离散型有序选择模型,建立了有序选择因子结构模型,并推导出MCMC估计方法。运用该方法,结合中国样本数据,本文对高等教育的分布类政策效应进行了实证估计。有序选择因子结构模型及其MCMC估计方法对于经济政策的分布类效应评估具有普遍的理论适应性和实际应用价值。     

资产一体化、市场均衡与资产定价--一种适用于企业资产定价的资本资产定价模型(CAPM)扩展形式

本文根据Williamson—Grossman—Hart的资产一体化研究思路，将资本资产定价模型(CAPM)扩展成为适用于异质资产定价(idiosyncratic asset pricing)的理论模型。按照资产一体化思路，定价资产的风险可分为绝对风险和相对风险贡献，定价资产的绝对风险反映了资产一体化诱取的风险积聚特征，相对风险贡献满足Shapley值的基本假说，因而可以得到在资产一体化条件下计量定价资产相对风险贡献的Shapley风险期望值。根据市场均衡条件下资产一体化总体均值一方差的消费表达形式，我们得到用Shapley风险期望值表达的企业资产风险及其预期报酬的均衡解。     

中国核心CPI随机游走识别和异常点分析

核心CPI能更真实地反映宏观经济运行趋势。然而,如何测算核心CPI一直备受关注。本文构建了一组带有异常因子的随机游走模型,利用MCMC法和Gibbs抽样,对时变参数进行估计,不仅从CPI中分离出核心CPI,而且对非核心CPI,通过捕捉到的异常点,发现与中国政策效应相吻合。本文仅利用了中国单一的CPI数据,不需要CPI子类权重测算及其再分配。进一步,通过与Wind核心CPI比较,以及Marques等（2003）核心CPI评价方法的检验,发现本文的核心CPI更具合理性和科学性。     

Computing marginal likelihoods from a single MCMC output

In this article, we propose new Monte Carlo methods for computing a single marginal likelihood or several marginal likelihoods for the purpose of Bayesian model comparisons. The methods are motivated by Bayesian variable selection, in which the marginal likelihoods for all subset variable models are required to compute. The proposed estimates use only a single Markov chain Monte Carlo (MCMC) output from the joint posterior distribution and it does not require the specific structure or the form of the MCMC sampling algorithm that is used to generate the MCMC sample to be known. The theoretical properties of the proposed method are examined in detail. The applicability and usefulness of the proposed method are demonstrated via ordinal data probit regression models. A real dataset involving ordinal outcomes is used to further illustrate the proposed methodology.     

A threshold stochastic volatility model with explanatory variables

In this paper, we introduce a threshold stochastic volatility model with explanatory variables. The Bayesian method is considered in estimating the parameters of the proposed model via the Markov chain Monte Carlo (MCMC) algorithm. Gibbs sampling and Metropolis–Hastings sampling methods are used for drawing the posterior samples of the parameters and the latent variables. In the simulation study, the accuracy of the MCMC algorithm, the sensitivity of the algorithm for model assumptions, and the robustness of the posterior distribution under different priors are considered. Simulation results indicate that our MCMC algorithm converges fast and that the posterior distribution is robust under different priors and model assumptions. A real data example was analyzed to explain the asymmetric behavior of stock markets.     

A comparison of multiple imputation with EM algorithm and MCMC method for quality of life missing data

This study investigated the performance of multiple imputations with Expectation-Maximization (EM) algorithm and Monte Carlo Markov chain (MCMC) method in missing data imputation. We compared the accuracy of imputation based on some real data and set up two extreme scenarios and conducted both empirical and simulation studies to examine the effects of missing data rates and number of items used for imputation. In the empirical study, the scenario represented item of highest missing rate from a domain with fewest items. In the simulation study, we selected a domain with most items and the item imputed has lowest missing rate. In the empirical study, the results showed there was no significant difference between EM algorithm and MCMC method for item imputation, and number of items used for imputation has little impact, either. Compared with the actual observed values, the middle responses of 3 and 4 were over-imputed, and the extreme responses of 1, 2 and 5 were under-represented. The similar patterns occurred for domain imputation, and no significant difference between EM algorithm and MCMC method and number of items used for imputation has little impact. In the simulation study, we chose environmental domain to examine the effect of the following variables: EM algorithm and MCMC method, missing data rates, and number of items used for imputation. Again, there was no significant difference between EM algorithm and MCMC method. The accuracy rates did not significantly reduce with increase in the proportions of missing data. Number of items used for imputation has some contribution to accuracy of imputation, but not as much as expected.     

A Markov Chain Monte Carlo procedure to generate revealed preference consistent datasets

This paper presents Markov-Chain-Monte-Carlo (MCMC) procedures to sample uniformly from the collection of datasets that satisfy some revealed preference test. The MCMC for the GARP test combines a Gibbs-sampler with a simple hit and run step. It is shown that the MCMC has the uniform distribution as its unique invariant distribution and that it converges to this distribution at an exponential rate.     

A Guide to Exact Simulation

Markov Chain Monte Carlo (MCMC) methods are used to sample from complicated multivariate distributions with normalizing constants that may not be computable in practice and from which direct sampling is not feasible. A fundamental problem is to determine convergence of the chains. Propp & Wilson (1996) devised a Markov chain algorithm called Coupling From The Past (CFTP) that solves this problem, as it produces exact samples from the target distribution and determines automatically how long it needs to run. Exact sampling by CFTP and other methods is currently a thriving research topic. This paper gives a review of some of these ideas, with emphasis on the CFTP algorithm. The concepts of coupling and monotone CFTP are introduced, and results on the running time of the algorithm presented. The interruptible method of Fill (1998) and the method of Murdoch & Green (1998) for exact sampling for continuous distributions are presented. Novel simulation experiments are reported for exact sampling from the Ising model in the setting of Bayesian image restoration, and the results are compared to standard MCMC. The results show that CFTP works at least as well as standard MCMC, with convergence monitored by the method of Raftery & Lewis (1992, 1996).     

Stochastic model specification search for Gaussian and partial non-Gaussian state space models

Model specification for state space models is a difficult task as one has to decide which components to include in the model and to specify whether these components are fixed or time-varying. To this aim a new model space MCMC method is developed in this paper. It is based on extending the Bayesian variable selection approach which is usually applied to variable selection in regression models to state space models. For non-Gaussian state space models stochastic model search MCMC makes use of auxiliary mixture sampling. We focus on structural time series models including seasonal components, trend or intervention. The method is applied to various well-known time series.     

带未知异方差广义空间模型的有效估计

空间单元大小以及其它的经济特征上的差异,常常会导致空间异方差问题。本文给出了广义空间模型异方差问题的三种不同估计方法。第一种方法是将异方差形式参数化,来克服自由度的不足,使用ML估计进行实现。而针对异方差形式未知时,分别采用了基于2SLS的迭代GMM估计和更加直接的MCMC抽样方法加以解决,特别是MCMC方法表现得更加优美。蒙特卡罗模拟表明,给定异方差形式条件下, ML估计通过异方差参数化的方法依然可以获得较好的估计效果。而异方差形式未知的情况下,另外两种方法随着样本数的增大时也可以与ML的估计结果趋于一致。     

Stick-breaking autoregressive processes

This paper considers the problem of defining a time-dependent nonparametric prior for use in Bayesian nonparametric modelling of time series. A recursive construction allows the definition of priors whose marginals have a general stick-breaking form. The processes with Poisson-Dirichlet and Dirichlet process marginals are investigated in some detail. We develop a general conditional Markov Chain Monte Carlo (MCMC) method for inference in the wide subclass of these models where the parameters of the marginal stick-breaking process are nondecreasing sequences. We derive a generalised Pólya urn scheme type representation of the Dirichlet process construction, which allows us to develop a marginal MCMC method for this case. We apply the proposed methods to financial data to develop a semi-parametric stochastic volatility model with a time-varying nonparametric returns distribution. Finally, we present two examples concerning the analysis of regional GDP and its growth.     

A Tutorial on Reversible Jump MCMC with a View toward Applications in QTL-mapping

A tutorial derivation of the reversible jump Markov chain Monte Carlo (MCMC) algorithm is given. Various examples illustrate how reversible jump MCMC is a general framework for Metropolis-Hastings algorithms where the proposal and the target distribution may have densities on spaces of varying dimension. It is finally discussed how reversible jump MCMC can be applied in genetics to compute the posterior distribution of the number, locations, effects, and genotypes of putative quantitative trait loci.     

What are the advantages of MCMC based inference in latent variable models?

Recent developments in Markov chain Monte Carlo [MCMC] methods have increased the popularity of Bayesian inference in many fields of research in economics, such as marketing research and financial econometrics. Gibbs sampling in combination with data augmentation allows inference in statistical/econometric models with many unobserved variables. The likelihood functions of these models may contain many integrals, which often makes a standard classical analysis difficult or even unfeasible. The advantage of the Bayesian approach using MCMC is that one only has to consider the likelihood function conditional on the unobserved variables. In many cases this implies that Bayesian parameter estimation is faster than classical maximum likelihood estimation. In this paper we illustrate the computational advantages of Bayesian estimation using MCMC in several popular latent variable models.