On the Asymptotic Stability of the Intrinsic and Fractional Bayes Factors for Testing Some Diffusion Models

Random processes, from which a single sample path data are available on a fine time scale, abound in many areas including finance and genetics. An effective way to model such data is to consider a suitable continuous-time-scale analog, X _t say, for the underlying process. We consider three diffusion models for the process X _t and address model selection under improper priors. Specifically, fractional and intrinsic Bayes factors (FBF and IBF) for model selection are considered. Here, we focus on the asymptotic stability of the IBF's and FBF's for comparing these models. Specifically, we propose to employ certain novel transformations of the data in order to ensure the asymptotic stability of the IBF's. While we use different transformations for pairwise comparisons of the models, we also show that a single common transformation can be used when simultaneously comparing all three models. We then demonstrate that, when FBF's are used to compare these models, we may have to employ different, model-specific training fractions in order to achieve asymptotic stability of the FBF's.     

Estimation of intrinsic growth factors in a class of stochastic population model

This article discusses the problem of parameter estimation with nonlinear mean-reversion type stochastic differential equations (SDEs) driven by Brownian motion for population growth model. The estimator in the population model is the climate effects, population policy and environmental circumstances which affect the intrinsic rate of growth r. The consistency and asymptotic distribution of the estimator θ is studied in our general setting. In the calculation method, unlike previous study, since the nonlinear feature of the model, it is difficult to obtain an explicit formula for the estimator. To solve this, some criteria are used to derive an asymptotically consistent estimator. Furthermore Girsanov transformation is used to simplify the equations, which then gives rise to the corresponding convergence of the estimator being with respect to a family of probability measures indexed by the dispersion parameter, while in the literature the existing results have dealt with convergence with respect to a given probability measure.     

统计套利模型研究——基于上证50指数成份股的检验

本文借鉴协整的思想,并采用比协整回归更一般化的方法来研究股票之间的统计套利模型。采用逐步回归法来确定合适的定价子空间与证券组合,并将统计套利模型应用于上证50指数的50个成份股,并使用方差比分析来检验可预测性,其结果表明随机去势后的股票价格序列明显偏离随机游走,存在着可预测成分。联立方程模型的估计结果表明错误定价趋于在短期内形成趋势,而在更长时间内回复。样本外绩效对交易费用水平的变动非常灵敏,机构投资者的年夏普比为1.3。     

长期投资者的战略资产配置选择

在股票期望收益率服从一个均值回复过程的假设下,推导出具有幂效用函数的投资者的资产配置函数,着重分析了投资期限对投资者资产配置结果的影响,发现长期投资者比短期投资者在股票上配置更大的资产比例.虽然不同投资期限的投资者具有相同的短视配置,但是战略配置随着投资期限的增大而增大.     

中国证券市场价格及波动率长记忆性实证研究

证券市场股价运动是否存在长记性,近年来的实证研究结论不一.其中重要原因是数据选择的局限和检验方法的不统一.通过自相关函数、修正的R/S和GPH方法对中国证券市场的日和五分钟数据进行了递进检验,得出了一些与众不同的结论.     

Utility based pricing and exercising of real options under geometric mean reversion and risk aversion toward idiosyncratic risk

We study the classical real option problem in which an agent faces the decision if and when to invest optimally into a project. The investment is assumed to be irreversible. This problem has been studied by Myers and Majd (Adv Futures Options Res 4:1–21, 1990) for the case of a complete market, in which the risk can be perfectly hedged with an appropriate spanning asset, by McDonald and Siegel (Q J Econ, 101:707–727, 1986), who include the incomplete case but assume that the agent is risk neutral toward idiosyncratic risk, and later by Henderson (Valuing the option to invest in an incomplete market, , 2006) who studies the incomplete case with risk aversion toward idiosyncratic risk under the assumption that the project value follows a geometric Brownian motion. We take up Henderson’s utility based approach but assume as suggested by Dixit and Pindyck (Investment under uncertainty, Princeton University Press, Princeton, 1994) as well as others, that the project value follows a geometric mean reverting process. The mean reverting structure of the project value process makes our model richer and economically more meaningful. By using techniques from optimal control theory we derive analytic expressions for the value and the optimal exercise time of the option to invest.      

基于ARMA-GARCH模型的恒指隐含波动率指数预测及其在期权交易中的应用

基于ARMA-GARCH模型,并结合均值回归效应,溢出效应和周内效应,本文研究了恒指隐含波动率指数(VHSI)能否被预测及预测是否有助于期权投资实践的问题.研究结果验证了香港股市具有均值回归的特性,标准普尔500指数对恒指隐含波动率指数有明显的溢出效应.此外,恒指隐含波动率指数呈现出周一上涨,周五下跌的特征,具有明显的周内效应.最后,本文运用ARMA-GARCH模型对恒指隐含波动率指数进行预测,并结合实际的市场数据做了期权交易模拟.结果显示,ARMA-GARCH模型比ARMA模型更适合对恒指隐含波动率进行建模;考虑了均值回归效应,溢出效应和周内效应之后,ARMAGARCH模型对恒指隐含波动率指数的预测能力显著提高,并且预测结果有助于期权交易获得较好的收益.