高频数据瞬时波动率核估计的窗宽选择及算法研究

在瞬时波动率的各种估计量中,非参数估计量因其能准确地度量瞬时波动率,一直是学者们的研究热点。然而,这类估计量在实际应用中都面临着最优窗宽的确定问题。由于最优窗宽中往往携带一些难以估计的未知参数,使得在实际应用过程中确定最优窗宽的具体数值存在困难。本文以瞬时波动率的核估计量为例,借鉴非参数回归分析中窗宽选择的思想,构建了一种能从数据中准确计算出最优窗宽具体值的算法。理论的分析和数值上的验证表明：文中所构建的算法具有良好的稳定性、适应性和收敛速度。算法的提出为瞬时波动率的后续应用研究铺平道路。     

属性集容量确定的夹挤式测度模式及其推算模型

迄今,为解决多属性偏好关联决策属性集容量判断指数复杂性难题所提出的λ模糊测度模式与k-可加模糊测度模式,以及建构在它们之上的属性集容量确定的推算模型,尚存在着适用性差的技术不足。为此,以平衡容量判断的可操作性和容量推算的准确性为视角,提出了一种新容量测度模式,即关于容量判断与推算的夹挤式测度模式,并在此基础上通过引入决策者较易判断给出的容量序信息构建了相应的容量推算模型。基于数值模拟的对比分析表明：新模式不仅在应用可行性上高于k-可加模糊测度模式,而且从容量推算的准确性上看也明显优于λ模糊测度模式和k-可加模糊测度模式,因而对实际决策具有更强的适用性。     

异质品双渠道供应链下制造商的渠道决策研究

研究由两个制造商、两个零售商以及消费者组成的双渠道供应链,制造商的最优产品分销渠道策略问题,通过构建Stackelberg主从动态博弈模型,分析制造商在不同渠道选择下的产品质量水平及定价决策,消费者三种不同渠道偏好下渠道竞争和品牌竞争激烈程度对制造商渠道选择均衡结果及帕累托最优选择的影响,以及对产品质量水平、产品价格、制造商及零售商利润的影响.研究表明：品牌和渠道双重竞争下,两种竞争之间存在相互作用的关系,并共同影响制造商最终渠道选择,制造商通过权衡价格决策的后动优势与消费者渠道偏好对其利润的贡献选择是否开辟双渠道,产品价格与产品质量水平正相关,与产品的替代率负相关,产品质量水平对渠道价格的影响程度等于消费者对该渠道的偏好程度.当消费者偏好某一渠道时,制造商可在该渠道上的产品采取优质高价策略,但渠道上产品性价比降低,当品牌竞争越激烈,产品价格越低,对制造商和零售商的利润挤压越严重.     

基于次梯度投影的泛投资组合选择策略

在线投资组合选择(online portfolio selection)问题是当前量化投资领域一个重要的研究问题.近些年来,可投资标的的爆炸式增长急需能够有效计算的投资组合选择策略,而现有高绩效算法大多具有指数级或多项式级的时间复杂度,不利于在实际中应用.由此,本文提出了一种基于次梯度投影的泛投资组合选择策略SGP.将次梯度投影的思想应用到资产组合构建的过程中,得到策略的再平衡规则.理论上,本文分析了次梯度投影算法的竞争性能,证明了该策略是一个泛投资组合选择策略;并发现该算法具有线性时间复杂度.实证上,验证了SGP策略在美国与中国市场的表现.结果表明,SGP策略能够实现和最新的泛投资组合选择策略相当的收益率,而算法运行时间短于现有策略.参数敏感性分析表明SGP策略对参数选择不敏感;交易成本敏感性分析表明SGP策略能够承受合理的交易成本.     

Educational Assortative Mating and Income Dynamics in Couples: A Longitudinal Dyadic Perspective

The question of how educational assortative mating may transform couples' lives and within‐family gender inequality has gained increasing attention. Using 25 waves (1979–2012) of data from the National Longitudinal Survey of Youth 1979 and longitudinal multilevel dyad models, this study investigated how educational assortative mating shapes income dynamics in couples during the marital life course. Couples were grouped into three categories—educational hypergamy (wives less educated than their husbands), homogamy, and hypogamy (wives more educated than their husbands). Results show that change in husbands' income with marital duration is similar across couples, whereas change in wives' income varies by educational assortative mating, with wives in educational hypogamy exhibiting more positive change in income during the marital life course. The finding that husbands' long‐term economic advancement is less affected than that of wives by educational assortative mating underscores the gender‐asymmetric nature of spousal influence in heterosexual marriages.     

Family Characteristics and Mate Selection: Evidence From Computer‐Assisted Dating in Japan

Research on mate selection rarely considers singles' preferences for their future partners' family configurations and experiences. Using online dating records from a matchmaking agency in Japan, a society with a strong emphasis on family and kinship, we examine how singles' responses to date requests correspond to potential mates' family circumstances. Results showed that singles' preferences for potential partners' family characteristics stem from a concern about future obligations toward the partner's family and stereotypes associated with certain family traits. Singles are less likely to accept requests from those from large families, which are seen as traditional. Being from a large family, however, hampers individuals' dating chances more if they are firstborn and have no brothers, two conditions that make them the designated child to care for elderly parents. We also find that Japanese singles seek partners with more of the universally valued family traits rather than traits similar to their own.     

道德的经济学——亲缘选择与广义效用理论

早期功利主义者认为效用不仅包括人们获得的物质利益,还包括精神上的追求和道德上的满足感。然而经济学数学化和公理化的趋势使经济学家更愿意把效用解释为物质利益上的满足,这极大地限制了经济学的解释力度。广义效用理论是在现代经济学的框架下对效用含义的扩展。通过对当前广义效用理论的回顾,结合生物学中的亲缘选择理论,提出了一个广义效用模型,并对模型的结果进行了分析。     

Covariance-regularized regression and classification for high dimensional problems

Summary.  We propose covariance-regularized regression, a family of methods for prediction in high dimensional settings that uses a shrunken estimate of the inverse covariance matrix of the features to achieve superior prediction. An estimate of the inverse covariance matrix is obtained by maximizing the log-likelihood of the data, under a multivariate normal model, subject to a penalty; it is then used to estimate coefficients for the regression of the response onto the features. We show that ridge regression, the lasso and the elastic net are special cases of covariance-regularized regression, and we demonstrate that certain previously unexplored forms of covariance-regularized regression can outperform existing methods in a range of situations. The covariance-regularized regression framework is extended to generalized linear models and linear discriminant analysis, and is used to analyse gene expression data sets with multiple class and survival outcomes.     

The additive hazards model with high-dimensional regressors

This paper considers estimation and prediction in the Aalen additive hazards model in the case where the covariate vector is high-dimensional such as gene expression measurements. Some form of dimension reduction of the covariate space is needed to obtain useful statistical analyses. We study the partial least squares regression method. It turns out that it is naturally adapted to this setting via the so-called Krylov sequence. The resulting PLS estimator is shown to be consistent provided that the number of terms included is taken to be equal to the number of relevant components in the regression model. A standard PLS algorithm can also be constructed, but it turns out that the resulting predictor can only be related to the original covariates via time-dependent coefficients. The methods are applied to a breast cancer data set with gene expression recordings and to the well known primary biliary cirrhosis clinical data.