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.     

Change in Volatility in the Won/U.S. Dollar Daily Exchange Rate:Stochastic Volatility Model

This study estimates the changes in volatility of the won/U.S. dollar dailyexchange rates before and after the Korean currency crisis, using the stochastic volatility model with the ARMAregression error term. We find that the persistence of volatility increased after the Koreancurrency crisis.     

随机利率影响下信用风险的Monte—Carlo模拟

在前人利用马尔科夫链表示公司信用等级的基础上,将信用等级和随机利率引入离散时间的信用风险模型中,从而提出随机利率影响下的新的信用风险模型。就上述模型,对不同初始信用等级、初始盈余以及不同时刻的破产概率进行Monte—Carlo模拟,并讨论了相同条件下初始盈余与破产概率、初始信用等级与破产概率以及时间长短与破产概率之间的相互关系。     

Scaledt分布、杠杆效应和上证综指的VaR风险

与正态分布相比,上证指数收益率的经验分布具有尖峰厚尾特征,但用Scaled t-分布比正态分布可以更好地拟合上证指数收益率的经验分布。本文以Scaled t-分布假设下的GJR模型为基础,测量了上证指数收益率波动性的杠杆效应,即信息对波动性的不对称影响：并根据GJR模型应用Monte Carlo模拟方法,测定上证指数日收益率和持有期收益率的风险价值（VaR）。根据GJR模型提供的结果,上证指数30天、60天和90天持有期收益率的风险值分别为12．1％、17．8％、22．0％。用GJR模型比均值-方差模型和历史模拟方法计算的5％显著性水平VaR值更接近实际收益率。     

互联网企业价值评估模型比较研究

基于互联网企业轻资产、高估值、迭代快以及风险大等特点,比较传统价值评估模型与Schwar-tz-Moon等实物期权价值评估模型,分别运用于评估案例企业泛微网络价值.结果发现,相较于传统现金流贴现模型,实物期权价值评估模型评估结果更接近于公司实际价值.三种实物期权模型敏感性分析表明:Schwartz-Moon模型评估误差最小,且模型稳健性最强,适用于不确定性高的互联网企业估值.     

CEV模型的单位根检验研究

CEV模型（Constant Elasticity of Variance Model）作为常用的利率模型,在实证分析中得到了广泛运用,但是其单位根检验一直被忽略或者被默认可以使用迪基一富勒检验。本文首次运用Box—Cox变换的技巧,针对CEV模型的单位根检验问题,找到了合适的统计量并且证明其渐进分布存在,然后通过蒙特卡罗方法求出了该统计量的分布表。得到了在大样本的情形下可以沿用迪基一富勒检验,但在小样本的情形下与迪基一富勒检验有所偏差的结论。     

随机折现因子方法与CAPM关于风险溢价的实证比较

本文根据随机折现因子方法的基本理论,结合广义矩阵法和蒙特卡罗模拟,对随机折现因子方法和传统的CAPM对风险溢价的计算进行实证比较研究。实证结果表明,对于中小样本,随机折现因子方法比传统的CAPM方法优越。估计量较为精确,误差小;对于大容量样本,这两种方法性能接近。另外,随机折现因子方法得到Jensen＇sα均值比CAPM方法得到Jensen＇sα均值小,而且标准偏差明显较小,也从另一角度说明了随机折现因子方法的优越性。     

基于蒙特卡罗模拟的商业银行信用风险度量方法

通过与Matlab程序相结合的方式介绍了基于蒙特卡罗模拟的商业银行信用风险度量方法。该方法使在给定的置信水平下科学地估算国内商业银行的信用风险成为可能。     

A Bayesian Nonparametric Investigation of the Predictive Effect of Exchange Rates on Commodity Prices

This study proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates in relation to commodity prices for three commodity-exporting countries: Canada, Australia, and New Zealand. We propose a new time-dependent infinite mixture of a normal linear regression model of the conditional distribution of the commodity price index. The mixing weights follow a set of Probit stick-breaking priors that are time-varying. We find that exchange rates have a positive predictive effect in general, but accounting for time variation does not improve forecasting performance. By contrast, the intercept in the regression and the lagged dependent variable show signs of parameter change over time in most cases, which is important in forecasting both the mean and the density of commodity prices one period ahead. The results also suggest that the variance is a large source of the time variation in the conditional distribution of commodity prices.     

Endogeneity in multiple output production: Evidence from the US hotel industry

The treatment of endogenous inputs and outputs in the estimation of multiple–output distance functions is an important issue that has been largely ignored in the hotel and tourism literature. It is well known that directional distance functions with endogeneity are difficult to estimate using maximum likelihood-based methods since the specification of additional identifiable equations for each endogenous variable is highly challenging. In this paper, we propose a limited-information approach to models of this type using a flexible reduced form for the endogenous variables. This approach allows us to easily estimate technical efficiency and does not rely on information about input or output prices, which are typically unavailable. We employ Bayesian methods and propose and use novel posterior measures of weakness and relevance of instruments in an application involving data from major US hotels. We show that controlling for endogeneity has a substantial impact for relative and absolute hotel performance.