Asymmetric Information, Heterogeneous Prior Beliefs and Market Regulation

This paper studies the trading behavior of an irrational insider and its influence on the market equilibrium in the presence of market regulation.We find that the market with only one insider with private information is almost close to a strong efficient market,under the condition of market regulation.In the equilibrium,the probability of the insider being caught trading with private information is zero,which shows that the reasonable behavior of the regulator is to essentially give up regulation.But the market efficiency and the irrational trader’s trading intensity all greatly improve because of the existence of the market regulation.

Construction of column-orthogonal designs for computer experiments

Latin hypercube design and uniform design are two kinds of most popular space-filling designs for computer experiments. The fact that the run size equals the number of factor levels in a Latin hypercube design makes it difficult to be orthogonal. While for a uniform design, it usually has good space-filling properties, but does not necessarily have small or zero correlations between factors. In this paper, we construct a class of column-orthogonal and nearly column-orthogonal designs for computer experiments by rotating groups of factors of orthogonal arrays, which supplement the designs for computer experiments in terms of various run sizes and numbers of factor levels and are flexible in accommodating various combinations of factors with different numbers of levels. The resulting column-orthogonal designs not only have uniformly spaced levels for each factor but also have uncorrelated estimates of the linear effects in first order models. Further, they are 3-orthogonal if the corresponding orthogonal arrays have strength equal to or greater than three. Along with a large factor-to-run ratio, these newly constructed designs are economical and suitable for screening factors for physical experiments.

Testing the independence of sets of large-dimensional variables

This paper proposes the corrected likelihood ratio test(LRT) and large-dimensional trace criterion to test the independence of two large sets of multivariate variables of dimensions p1 and p2 when the dimensions p=p1+p2 and the sample size n tend to infinity simultaneously and proportionally.Both theoretical and simulation results demonstrate that the traditional χ2 approximation of the LRT performs poorly when the dimension p is large relative to the sample size n,while the corrected LRT and large-dimensional trace criterion behave well when the dimension is either small or large relative to the sample size.Moreover,the trace criterion can be used in the case of p> n,while the corrected LRT is unfeasible due to the loss of definition.

A note on statistical analysis of factor models of high dimension

Linear factor models are familiar tools used in many fields.Several pioneering literatures established foundational theoretical results of the quasi-maximum likelihood estimator for high-dimensional linear factor models.Their results are based on a critical assumption:The error variance estimators are uniformly bounded in probability.Instead of making such an assumption,we provide a rigorous proof of this result under some mild conditions.

An Information-Theoretic Approach to Effective Inference for Z-functionals

In this paper, through an information-theoretic approach, we construct estimations and confidence intervals of Z-functionals involving finite population and with the presence of auxiliary information. In particular, we give a method of estimating the variance of finite population with known mean. The modified estimates and confidence intervals for Z-functionals can adequately use the auxiliary information, at least not worse than what the standard ones do. A simulation study is presented to assess the performance of the modified estimates for the finite sample case.