Asymmetric responses of international stock markets to trading volume

The major goal of this paper is to examine the hypothesis that stock returns and return volatility are asymmetric, threshold nonlinear, functions of change in trading volume. A minor goal is to examine whether return spillover effects also display such asymmetry. Employing a double-threshold GARCH model with trading volume as a threshold variable, we find strong evidence supporting this hypothesis in five international market return series. Asymmetric causality tests lend further support to our trading volume threshold model and conclusions. Specifically, an increase in volume is positively associated, while decreasing volume is negatively associated, with the major price index in four of the five markets. The volatility of each series also displays an asymmetric reaction, four of the markets display higher volatility following increases in trading volume. Using posterior odds ratio, the proposed threshold model is strongly favored in three of the five markets, compared to a US news double threshold GARCH model and a symmetric GARCH model. We also find significant nonlinear asymmetric return spillover effects from the US market.     

分位点门限自回归时间序列模型的贝叶斯方法

本文运用贝叶斯方法研究了门限分位点自回归时间序列模型的估计和预测. 将分位点回归的最优化问题转化为极大似然估计的问题,从而可以利用Metropolis-Hastings算法对模型中的参数进行Bayesian估计. 同时我们将模型应用于上证综合指数的增长率的数据, 得到了这一增长率的分位点估计. 这一方法的优越之处在于它不需要对数据的分布作预先的假定.     

陆地地震勘探环境噪声的混沌性建模研究

为提取地震资料中的有效信息, 了解环境噪声的性质和产生机制, 提出了一种陆地地震勘探环境噪声的混沌建模方法。该类环境噪声具有混沌性出发, 采用经典混沌杜芬(Duffing)系统对中国林带测区的环境噪声进行建模。根据Duffing 系统的非线性特征, 采用MCMC(Markov Chain Monte Carlo)算法对系统参数进行求解,并对模拟噪声记录与实际环境噪声从时域波形, 相态图, 频谱图以及李雅谱诺夫(Lyapunov)指数等方面进行对比分析。结果表明, 模拟噪声记录可保持实际环境噪声的混沌性, 且在时域波形, 相态图和频谱图上拟合较好。利用此模型获得的替代数据更加接近实际噪声, 从而为压制环境噪声提供新对策, 为复杂环境下的地震勘探提供理论支持。     

广义变系数模型的Bayesian B样条估计

提出了广义变系数模型函数系数的一种新的估计方法．我们用B样条函数逼近函数系数,不具体选择节点的个数,而是节点个数取均匀的无信息先验,样条函数系数取正态先验,用Bayesian模型平均的方法估计各个函数系数．这种估计方法一个主要特点是允许各个函数系数所需节点个数的后验分布不同,因此允许不同函数系数使用不同的光滑参数．另外,本文还给出了Bayesian B样条估计的计算方法,并通过模拟例子,说明广义变系数模型的函数系数可以由Bayesian B样条估计方法得到很好的估计．     

Automated rejection sampling from product of distributions

Summary  Sampling from probability density functions (pdfs) has become more and more important in many areas of applied science, and has therefore been the subject of great attention. Many sampling procedures proposed allow for approximate or asymptotic sampling. On the other hand, very few methods allow for exact sampling. Direct sampling of standard pdfs is feasible, but sampling of much more complicated pdfs is often required. Rejection sampling allows to exactly sample from univariate pdfs, but has the huge drawback of needing a case-by-case calculation of a comparison function that often reveals as a tremendous chore, whose results dramatically affect the efficiency of the sampling procedure. In this paper, we restrict ourselves to a pdf that is proportional to a product of standard distributions. From there, we show that an automated selection of both the comparison function and the upper bound is possible. Moreover, this choice is performed in order to optimize the sampling efficiency among a range of potential solutions. Finally, the method is illustrated on a few examples.     

Pairwise clustering using a Monte Carlo Markov Chain

In this work an application of MCMC is proposed for unsupervised data classification, in conjunction with a novel pairwise objective function, which is shown to work well in situations where clusters to be identified have a strong overlap, and the centroid oriented methods (such as K-means) fail by construction. In particular, an exceptionally simple but difficult situation is addressed when cluster centroids coincide, and one can differentiate between the clusters only on the basis of their variance. Performance of the proposed approach is tested on synthetic and real datasets.     

Geometric Convergence Rates for Time-Sampled Markov Chains

We consider time-sampled Markov chain kernels, of the form P = _n n P ⁿ . We prove bounds on the total variation distance to stationarity of such chains. We are motivated by the analysis of near-periodic MCMC algorithms.     

基于随机波动模型的沪深股市波动分析——以06,07年度沪深股指为例

基于马尔科夫链蒙特卡罗(MCMC)模拟的贝叶斯(Bayes)分析方法,应用随机波动(SV)模型实证分析06、07年度中国股票市场指数的波动性,并对比沪市与深市的股指,对不同形式的SV模型的参数进行估计,对结论作出合理的解释.