单指标非参数期权定价——改进的非参数定价方法

本文建立一种改进的非参数期权定价模型,称为单指标非参数期权定价模型。相比现有非参数回归期权定价模型是期权价格关于各个因素的多元回归函数,本模型通过变量变换把期权价格多个因素指标转换为一个综合变量——单指标,得到期权价格关于单指标的一元非参数回归方程。改进的模型实现了多元非参数期权定价模型的降维和简化了模型计算;还通过多个期限期权的单指标组合解决了非参数估计的样本数量问题;以及通过期限平滑解决了现有非参数定价模型中的日历效应问题。选取上证50ETF期权数据实证分析表明,无论是样本内的估计结果还是样本外的预测结果都比传统的Black-Scholes模型、半参数Black-Scholes模型和多元非参数回归期权定价模型估计效果有提高。     

转型背景下地方高校新同质化问题思考?

地方高校在转型过程中陷入了新同质化的困境,出现了侧重向工科专业投入建设的一边倒倾向,过度强调就业技能等问题,偏离了政府对地方高校转型的目的。地方高校在转型中应找准定位,将传统优势与地方资源需求相结合,相关专业互融支撑,创办特色专业,从而实现错位化、差异化的发展。还要利用优质智力资源,及时更新、动态调整专业设置,发展社会急需的新专业,从而实现地方高校真正意义上的转型,为地方高校的生存争取更大的空间。     

陕西地方院校高层次人才引进策略研究?

陕西地方院校长期存在着学校知名度不高,教师收入水平低,高层次人才储备不足、引进困难和增长缓慢的不良现状。为改善这一现状,有效加强高层次人才引进工作力度,陕西地方院校应以重点学科为突破点,以实际需求为导向,牢固树立成本意识,理性制定高层次人才引进策略;应增强引才主动意识,积极学习西方发达国家引才理念,善于利用政府牵引模式下的高层次人才引进合力,全面活化高层次人才引进手段;应注重经济契约与心理契约有机结合,刚性管理与柔性管理有机结合,将团队建设纳入考核内容之中,切实加强对高层次人才的管理。     

真实性偏好如何影响全球品牌中国元素产品的购买可能性?

在中国市场上,全球品牌本土化的现象越来越频繁.但在理论上,还少有研究分析中国消费者如何评价融入中国元素的全球品牌.为此,文章以认知—情感人格系统理论为框架,构建了包含真实性偏好、品牌本土象征价值、尊重品牌遗产和购买可能性的概念模型,试图探索其中的因果关系.实证研究表明,品牌本土象征价值认知—情感单元和尊重品牌遗产认知—情感单元均能提高购买可能性.真实性偏好高的消费者更容易通达品牌本土象征价值认知—情感单元和尊重品牌遗产认知—情感单元.尊重品牌遗产认知—情感单元有助于增强品牌本土象征价值认知—情感单元.文章不仅在理论上丰富了全球品牌相关研究,同时对实践也有战略指引.     

中国农村社会学研究现状与前景展望——“第七届农村社会学论坛”观点综述

“第七届农村社会学论坛”于2016年5月21－22日在西北农林科技大学成功举办。这次论坛主要围绕现代乡村文化建设与公共空间重建、乡村的现代命运及其未来发展趋势、国家治理下的基层社会治理困境与逻辑、农村公共事务治理及社区发展转型、农村社会发展和社会治理等议题展开讨论交流。     

PPP模式在政府债务危机中的应用研究

近年来,政府债务危机频频爆发,国务院办公厅下文要赋予地方政府适度举债融资权,规范地方政府的举债融资机制.政府与社会资本合作模式(P P P模式)被列入规范政府举债融资机制之中,该举措对于化解地方政府债务压力,缓解公用基础设施建设资金短缺发挥重要作用.因此需要对PPP模式适用于政府债务领域的可行性和必要性进行分析,在缓解政府债务的PPP具体模式中加以选择,并从法律层面保障PPP模式的实施.     

一类含有随机参数的混沌系统的Hopf分岔研究

文章选取随机变量为系统的随机变量研究含有随机参数混沌系统的Hopf分岔,利用Chebyshev正交多项式逼近理论将含有随机变量的系统转化为等价的确定性系统,通过Hopf分岔定理和Lyapunov系数讨论了随机参数系统的Hopf分岔及稳定性,发现随机系统的渐进稳定性参数区间大小不仅和确定性参数有关,还与随机参数有非常密切的关系.     

The k-factor GARMA Process with Infinite Variance Innovations

In this article, we develop the theory of k-factor Gegenbauer Autoregressive Moving Average (GARMA) process with infinite variance innovations which is a generalization of the stable seasonal fractional Autoregressive Integrated Moving Average (ARIMA) model introduced by Diongue et al. (2008 Diongue, A.K., Guégan, D. (2008). Estimation of k-Factor GIGARCH Process: A Monte Carlo Study. Communications in Statistics-Simulation and Computation 37:2037–2049.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). Stationarity and invertibility conditions of this new model are derived. Conditional Sum of Squares (CSS) and Markov Chains Monte Carlo (MCMC) Whittle methods are investigated for parameter estimation. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques. Finally, the usefulness of the model is corroborated with the application to streamflow data for Senegal River at Bakel.     

Understanding Estimators of Treatment Effects in Regression Discontinuity Designs

In this paper, we propose two new estimators of treatment effects in regression discontinuity designs. These estimators can aid understanding of the existing estimators such as the local polynomial estimator and the partially linear estimator. The first estimator is the partially polynomial estimator which extends the partially linear estimator by further incorporating derivative differences of the conditional mean of the outcome on the two sides of the discontinuity point. This estimator is related to the local polynomial estimator by a relocalization effect. Unlike the partially linear estimator, this estimator can achieve the optimal rate of convergence even under broader regularity conditions. The second estimator is an instrumental variable estimator in the fuzzy design. This estimator will reduce to the local polynomial estimator if higher order endogeneities are neglected. We study the asymptotic properties of these two estimators and conduct simulation studies to confirm the theoretical analysis.     

A Seemingly Unrelated Nonparametric Additive Model with Autoregressive Errors

This article considers a nonparametric additive seemingly unrelated regression model with autoregressive errors, and develops estimation and inference procedures for this model. Our proposed method first estimates the unknown functions by combining polynomial spline series approximations with least squares, and then uses the fitted residuals together with the smoothly clipped absolute deviation (SCAD) penalty to identify the error structure and estimate the unknown autoregressive coefficients. Based on the polynomial spline series estimator and the fitted error structure, a two-stage local polynomial improved estimator for the unknown functions of the mean is further developed. Our procedure applies a prewhitening transformation of the dependent variable, and also takes into account the contemporaneous correlations across equations. We show that the resulting estimator possesses an oracle property, and is asymptotically more efficient than estimators that neglect the autocorrelation and/or contemporaneous correlations of errors. We investigate the small sample properties of the proposed procedure in a simulation study.