基于滚窗VAR的沪港通波动溢出效应测度

股市间的互联互通是把“双刃剑”,在加强联动性的同时也扩大了风险敞口。作为中国股市对外开放的重大举措,沪港通开通六年来的效果如何是业界关注的议题。本文运用滚窗VAR方法计算沪港通前后各1000个交易日期间上海、香港和纽约股市的时变波动溢出指数,进而分析两两市场之间波动溢出效应的大小、传导方向变化。研究发现,沪港通的实施,增大了沪市与香港股市的波动溢出效应,与美国的波动溢出效应相对减小,沪港通在加强沪、港两市联动性的同时弱化了与纽约股市的联动性;改变了沪港两市之间波动溢出效应的传导方向,由单一的港→沪溢出变为以沪市为主的双向沪←→港溢出,同时也增大了对纽约股市的溢出效应,增强了沪市的竞争力。这一结果符合经济基础假说和协同市场假说。本文的研究改进了股市间互联互通波动溢出效应程度和传导方向的测度方法,并为中国沪港通政策效果评价及进一步的国际板开设提供决策参考依据。     

存在时变方差的股指期货最佳套期保值比率计算

在Cecchetti等人研究的基础上，给出存在时变方差的情形下，利用最大效用法计算股指期货套期保值比率的方法，推导出用矩阵表达的形式，并对相关参数进行估计。     

Semiparametric analysis of case series data

Summary.  The case series model for estimating the association between an age-dependent exposure and an outcome event requires information only on cases and implicitly adjusts for all age-independent multiplicative confounders, while allowing for an age-dependent base-line incidence. In the paper the model is presented in greater generality than hitherto, including more general discussion of its derivation, underlying assumptions, applicability, limitations and efficiency. A semiparametric version of the model is developed, in which the age-specific relative incidence is left unspecified. Modelling covariate effects and testing assumptions are discussed. The small sample performance of this model is studied in simulations. The methods are illustrated with several examples from epidemiology.     

石油期货套期保值时变套期比研究

通过对石油价格的实证分析认为，石油价格波动具有时变性，并且现货价格和期货价格具有协整关系．考虑这两种特征，用带有GARCH误差项的向量误差修正模型求解时变套期保值比率．实证结果表明，该方法套期保值效果好于通常最优固定套期比的方法。     

基于C－D函数的变系数动态投入产出模型

将Ｃｏｂｂ－Ｄｏｕｇｌａｓ函数应用于投入产出模型中消耗系数ａ＿（ｉｊ）（ｔ）和资本系数ｂ＿（ｉｊ）（ｔ）的确定，提出了一种新的时变结构系数动态投入产出模型的建模方法。     

Weighting methods for ties between event times and covariate change times

Recent work has shown that the presence of ties between an outcome event and the time that a binary covariate changes or jumps can lead to biased estimates of regression coefficients in the Cox proportional hazards model. One proposed solution is the Equally Weighted method. The coefficient estimate of the Equally Weighted method is defined to be the average of the coefficient estimates of the Jump Before Event method and the Jump After Event method, where these two methods assume that the jump always occurs before or after the event time, respectively. In previous work, the bootstrap method was used to estimate the standard error of the Equally Weighted coefficient estimate. However, the bootstrap approach was computationally intensive and resulted in overestimation. In this article, two new methods for the estimation of the Equally Weighted standard error are proposed. Three alternative methods for estimating both the regression coefficient and the corresponding standard error are also proposed. All the proposed methods are easy to implement. The five methods are investigated using a simulation study and are illustrated using two real datasets.     

Investigation of age–treatment interaction in the SPACE trial using different statistical approaches

Selection of treatments to fit the specific needs for a certain patient is one major challenge in modern medicine. Personalized treatments rely on established patient–treatment interactions. In recent years, various statistical methods for the identification and estimation of interactions between relevant covariates and treatment were proposed. In this article, different available methods for detection and estimation of a covariate–treatment interaction for a time-to-event outcome, namely the standard Cox regression model assuming a linear interaction, the fractional polynomials approach for interaction, the modified outcome approach, the local partial-likelihood approach, and STEPP (Subpopulation Treatment Effect Pattern Plots) were applied to data from the SPACE trial, a randomized clinical trial comparing stent-protected angioplasty (CAS) to carotid endarterectomy (CEA) in patients with symptomatic stenosis, with the aim to analyse the interaction between age and treatment. Time from primary intervention to the first relevant event (any stroke or death) was considered as outcome parameter. The analyses suggest a qualitative interaction between patient age and treatment indicating a lower risk after treatment with CAS compared to CEA for younger patients, while for elderly patients a lower risk after CEA was observed. Differences in the statistical methods regarding the observed results, applicability, and interpretation are discussed.     

Identification of Confounding versus Dispersing Covariates by Confounding Influence

As is well known, omission of non confounding covariates identified by the treatment assignment may lead to considerable bias for estimated treatment effect even in a simple randomized trial. In this article we identify confounding vs. dispersing covariates by the confounding influence characterizing variance change and bias risk of estimated treatment effect due to constraint on effects of these covariates. Consequently, consistent constraint on effects of identified confounding covariates reduces variance of estimated treatment effect whereas inconsistent constraint on effects of identified dispersing covariates—such as omission of identified dispersing covariates—leads to little bias for estimated treatment effect.     

Strong approximations for time-varying infinite-server queues with non-renewal arrival and service processes

In real stochastic systems, the arrival and service processes may not be renewal processes. For example, in many telecommunication systems such as internet traffic where data traffic is bursty, the sequence of inter-arrival times and service times are often correlated and dependent. One way to model this non-renewal behavior is to use Markovian Arrival Processes (MAPs) and Markovian Service Processes (MSPs). MAPs and MSPs allow for inter-arrival and service times to be dependent, while providing the analytical tractability of simple Markov processes. To this end, we prove fluid and diffusion limits for MAP_t/MSP_t/∞ queues by constructing a new Poisson process representation for the queueing dynamics and leveraging strong approximations for Poisson processes. As a result, the fluid and diffusion limit theorems illuminate how the dependence structure of the arrival or service processes can affect the sample path behavior of the queueing process. Finally, our Poisson representation for MAPs and MSPs is useful for simulation purposes and may be of independent interest.     

Proportional Rate Model with Incomplete Covariate and Complete Auxiliary Information

This paper deals with the analysis of proportional rate model for recurrent event data when covariates are subject to missing. The true covariate is measured only on a randomly chosen validation set, whereas auxiliary information is available for all cohort subjects. To further utilize the auxiliary information to improve study efficiency, we propose an estimated estimating equation for the regression parameters. The resulting estimators are shown to be consistent and asymptotically normal. Both graphical and numerical techniques for checking the adequacy of the model are presented. Simulations are conducted to evaluate the finite sample performance of the proposed estimators. Illustration with a real medical study is provided.