Determining the Number of Factors in Approximate Factor Models

In this paper we develop some econometric theory for factor models of large dimensions. The focus is the determination of the number of factors (r), which is an unresolved issue in the rapidly growing literature on multifactor models. We first establish the convergence rate for the factor estimates that will allow for consistent estimation of r. We then propose some panel criteria and show that the number of factors can be consistently estimated using the criteria. The theory is developed under the framework of large cross‐sections (N) and large time dimensions (T). No restriction is imposed on the relation between N and T. Simulations show that the proposed criteria have good finite sample properties in many configurations of the panel data encountered in practice.     

Ambiguity,Risk, and Asset Returns in Continuous Time

Models of utility in stochastic continuous–time settings typically assume that beliefs are represented by a probability measure, hence ruling out a priori any concern with ambiguity. This paper formulates a continuous–time intertemporal version of multiple–priors utility, where aversion to ambiguity is admissible. In a representative agent asset market setting, the model delivers restrictions on excess returns that admit interpretations reflecting a premium for risk and a separate premium for ambiguity.     

资产证券化在金融资产管理公司的运用研究

通过对资产证券化的含义 ,及其在金融资产管理公司不良资产资产处置业务中运用的可行性、操作模式 ,存在的问题等研究 ,对作为金融资产管理公司业务范围之一的资产证券化业务 ,从理论和实践两方面进一步进行剖析 ,为金融资产管理公司的实际工作提供建议和启发。     

基于流通基本方程的人民币研究

本文在一些假设的基础上推导出了我国货币流通的基本方程,并结合我国的当前实际情况,描述我国的人民币市场。     

Remove unwanted variation retrieves unknown experimental designs

Remove unwanted variation (RUV) is an estimation and normalization system in which the underlying correlation structure of a multivariate dataset is estimated from negative control measurements, typically gene expression values, which are assumed to stay constant across experimental conditions. In this paper we derive the weight matrix which is estimated and incorporated into the generalized least squares estimates of RUV-inverse, and show that this weight matrix estimates the average covariance matrix across negative control measurements. RUV-inverse can thus be viewed as an estimation method adjusting for an unknown experimental design. We show that for a balanced incomplete block design (BIBD), RUV-inverse recovers intra- and interblock estimates of the relevant parameters and combines them as a weighted sum just like the best linear unbiased estimator (BLUE), except that the weights are globally estimated from the negative control measurements instead of being individually optimized to each measurement as in the classical, single measurement BIBD BLUE.     

GMM with Weak Identification

This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weak‐identification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo studies. Confidence sets immune to weak identification are proposed. We use these results to inform an empirical investigation of various CCAPM specifications; the substantive conclusions reached differ from those obtained using conventional methods.     

Challenges of social workers' involvement in the recovery of the 5.12 Wenchuan Earthquake in China

This article explores the challenges of social workers' involvement in recovery work of the 5.12 Wenchuan Earthquake (12 May 2008). Six social workers working in three social work stations in Sichuan, China, were asked to report the challenges they faced in working with disaster survivors. Findings reveal that the social workers faced many challenges. These include lack of government support, low professional status of social work, rapid changes in the social environment in disaster‐affected areas, lack of supervision, lack of cooperation and coordination among social service agencies, and lack of experience and knowledge in working with disaster survivors. The practical, educational and policy implications of the findings are addressed.     

An Investigation of Parameter Recovery in MCMC Estimation for the Additive IRT Model

The article aims at evaluating the parameter recovery for the multidimensional additive IRT model (Sheng, 2005 Sheng , Y. ( 2005 ). Bayesian Analysis of Hierarchical IRT Models: Comparing and Combining the Unidimensional & Multi-Unidimensional IRT Models. PhD thesis , Faculty of the Graduate SchoolUniversity of Missouri-Columbia . [Google Scholar]; Sheng and Wikle, 2009 Sheng , Y. , Wikle , C. K. ( 2009 ). Bayesian IRT models incorporating general and specific abilities . Behaviormetrika 36 : 27 – 48 .[Crossref] , [Google Scholar]). By estimating the model parameters via Gibbs sampler, a simulation study is conducted under different testing conditions, e.g., dimensionality, test and subtest lengths, correlation matrices, and different values of discrimination parameters. The results show that, especially when the test length is short and the abilities are highly correlated, the accuracy of the parameter estimates is reduced and more iterations are required to convergence. An application in educational testing is also described to show the effectiveness of the model in use.     

基于可变强度跳跃-GARCH模型的资产价格跳跃行为分析——以中国上市公司股票市场数据为例

近年来,美国金融危机、欧债危机、地震等突发事件不断冲击着我国金融市场,各类资产价格频繁出现大幅跳动,收益风险短期内急剧扩大。鉴于此,本文构建了门限效应下状态变量依赖自回归强度跳跃-GARCH模型（简称TSD-ARJI-GARCH模型）来探讨股票资产价格随时间平滑波动和大幅度跳跃的双重特征。该模型扩展了现有可变强度跳跃-GARCH模型,克服了片面强调内生或外生因素的局限性,既允许跳跃强度受单个资产异质因素的内生驱动,以刻画跳跃变化的时变性及集聚性,也考虑了外部状态变量影响的门限效应。通过对不同类型中国上市公司股票市场数据的实证分析,验证了该模型对各类上市公司股票资产价格跳跃特征都具有较好的辨别和预测能力,可为动态监管金融资产的跳跃风险提供理论依据。     

基于动态损失厌恶投资组合模型的最优资产配置与实证研究

从行为金融学的角度考虑投资者损失厌恶的心理特征,建立预期效用最大化的动态损失厌恶投资组合优化模型。以我国股票市场为依托,将市场分为上升、下降和盘整三种状态,研究动态损失厌恶投资组合模型的最优资产配置和绩效表现,并与静态损失厌恶投资组合模型、M-V投资组合模型和CVaR投资组合模型进行比较。最后,在具有交易成本的条件下对动态模型进行稳健性检验。得出结论：不同情况下,动态损失厌恶投资者具有不同的最优资产配置比例,且动态损失厌恶投资组合模型明显优于静态模型、M-V投资组合模型和CVaR投资组合模型。