Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility models.     

Global and Extreme Dependence Between Investor Sentiment and Stock Returns in European Markets

The authors investigate the global and extreme dependence structure between investor sentiment and stock returns in 7 European stock markets (Belgium, France, Germany, Greece, the Netherlands, Portugal, and the UK), over the period 1985–2015. Global dependence refers to the correlation of changes in sentiment and stock returns over the whole range of these 2 variables, and extreme dependence refers to the local correlation of high (i.e. asymptotic) changes in sentiment and high stock returns. Using copula models and a bootstrap procedure, 6 statistical tests are performed for this purpose. Among the results of the tests, the authors highlight those that provide evidence of contemporaneous lower extreme dependence and contemporaneous upper extreme independence between sentiment and returns. As policy implications, these results suggest that financial stability can be promoted if regulators consider the impact of their decisions on investor sentiment. Also, the results seem to support the arguments in favor of short selling ban during turmoil periods. Finally, overall, the results are relevant for both investors and regulators and reinforce the importance of considering investor sentiment to better understand the behavior of financial markets.     

基于Copula的股票型开放式基金的组合风险度量

本文在＂推出股指期货和融资融券＂的新政策下,结合t-EGARCH模型和Copula方法对股票型开放式基金进行分析.该模型能更好地捕捉资产间的非线性相关性,更符合现实市场.并在此基础上,利用蒙特卡洛模拟计算了景顺增长基金前十大重仓股票及其投资组合的VaR值,从而验证了模型的有效性。     

大掺量粉煤灰混凝土在工程中应用研究

通过试验和工程实践结舍,对高寒地区超大参量粉煤灰混凝土性能进行研究。结暴表明,掺一定量的优质粉煤友的混凝土吴有良好的施工性能、力学性能和耐久性能。为指导施工,建立了大掺量粉煤灰混凝土回弹法测强曲线经验公式,这为大掺量粉煤灰混凝土在高寒地区的应用提供了理论参考。     

宏观经济与股票价格波动之间的关系研究

利用Copula模型,研究宏观经济变量与上证股指收益率之间的相关关系,在选择合适的边缘分布函数的基础上,分别建立了常相关的二元正态Copula函数、t—Copula函数、FrankCopula函数、ClaytonCopula函数以及Gum—belCopula函数模型,并且利用欧氏距离方法选择出最佳拟合Copula模型。选取2001年1月至2011年12月的月度数据作为处理对象,并利用最佳拟合模型分析宏观经济变量与上证股指收益率间相关关系及相关结构,从而揭示了中国宏观经济与股票市场之间的相关性。     

Basket trading under co-integration with the logistic mixture autoregressive model

In this paper, we propose a co-integration model with a logistic mixture auto-regressive equilibrium error (co-integrated LMAR), in which the equilibrium relationship among cumulative returns of different financial assets is modelled by a logistic mixture autoregressive time series model. The traditional autoregression (AR) based unit root test (ADF test), used in testing co-integration, cannot give a sound explanation when a time series passes the ADF test. However, its largest root in the AR polynomial is extremely close to, but less than, one, which is most likely the result of a mixture of random-walk and mean-reverting processes in the time series data. With this background, we put an LMAR model into the co-integration framework to identify baskets that have a large spread but are still well co-integrated. A sufficient condition for the stationarity of the LMAR model is given and proved using a Markovian approach. A two-step estimating procedure, combining least-squares estimation and the Expectation-Maximization (EM) algorithm, is given. The Bayesian information criterion (BIC) is used in model selection. The co-integrated LMAR model is applied to basket trading, which is a widely used tool for arbitrage. We use simulation to assess the model in basket trading strategies with the statistical arbitrage feature in equity markets. Data from several sectors of the Hong Kong Hang Seng Index are used in a simulation study on basket trading. Empirical results show that a portfolio using the co-integrated LMAR model has a higher return than portfolios selected by traditional methods. Although the volatility in the return increases, the Sharpe ratio also increases in most cases. This risk–return profile can be explained by the shorter converging period in the co-integrated LMAR model and the larger volatility in the ‘mean-reverting’ regime.     

Mean and dispersion modelling for policy claims costs

A model for the statistical analysis of the total amount of insurance paid out on a policy is developed and applied. The model simultaneously deals with the number of claims (zero or more) and the amount of each claim. The number of claims is from a Poisson-based discrete distribution. Individual claim sizes are from a continuous right skewed distribution. The resulting distribution of total claim size is a mixed discrete-continuous model, with positive probability of a zero claim. The means and dispersions of the claim frequency and claim size distribution are modeled in terms of risk factors. The model is applied to a car insurance data set.     

基于关联交易的企业集团信用风险研究

基于合作博弈理论构建了企业集团关联交易基本模型,刻画了企业集团的公允关联交易以及两种广泛存在的、典型的非公允关联交易行为。利用一类Survival Copula函数刻画了不同类型关联交易下的企业集团信用风险,从理论上阐明了关联交易影响企业集团信用风险的基本机制。研究结果表明:通过关联交易"掏空"母公司的行为会使企业集团的信用风险显著增加;存在最优内部资源配置方式可使企业集团的信用风险水平最低。     

沥青混合料高温稳定性的灰色关联分析

灰色关联分析是一种系统分析方法。运用灰色关联分析可以分辨出各个影响因素与系统的发展态势之间的紧密关系,从中找出对系统起到推动性作用的主要因素,哪些会是对系统的发展起到很小影响力的次要因素。本文运用灰色关联熵分析法对沥青混合料的高温性能进行分析。     

基于Copula函数和蒙特卡洛模拟方法的权证定价

在分析计算单一权证价格的基础上,提出了利用Copula函数和蒙特卡洛模拟的方法来计算多种权证的定价模型。具体实例分析表明,该方法可为投资者提供有益的投资决策参考。