Conditionally Reducible Natural Exponential Families and Enriched Conjugate Priors

Consider a standard conjugate family of prior distributions for a vector-parameter indexing an exponential family. Two distinct model parameterizations may well lead to standard conjugate families which are not consistent, i.e. one family cannot be derived from the other by the usual change-of-variable technique. This raises the problem of finding suitable parameterizations that may lead to enriched conjugate families which are more flexible than the traditional ones. The previous remark motivates the definition of a new property for an exponential family, named conditional reducibility. Features of conditionally-reducible natural exponential families are investigated thoroughly. In particular, we relate this new property to the notion of cut, and show that conditionally-reducible families admit a reparameterization in terms of a vector having likelihood-independent components. A general methodology to obtain enriched conjugate distributions for conditionally-reducible families is described in detail, generalizing previous works and more recent contributions in the area. The theory is illustrated with reference to natural exponential families having simple quadratic variance function.     

股市预警系统与构成指标简述

中国股市是一个高风险的股市 ,因此有必要运用现代金融计算技术和计量金融理论建立一套股市预警系统。本文在讨论了建立股市预警系统的必要性、可行性的基础上 ,着重讨论了股市预警系统的指标构成原则和框架 ,将近年来在现代金融理论中发展很快的协整理论(CointegrationTheory)、条件异方差理论、市场有效性理论等引入到股市预警系统框架中来     

基于条件概率积分变换的多元Copula函数选择

本文构建了基于条件概率积分变换的Copula函数选择方法,通过对条件概率积分变换下Anderson-Darling(AD)、Kolmogorov-Smirnov(KS)、Cramér-von Mises(CM)这三种统计量的比较,讨论在不同样本容量和变量维数下其对多种Copula函数的拟合效果。利用GSPTSE、INMEX.MX和NDX三大股指样本,将基于条件概率积分变换的Copula函数选择方法与核密度估计和极大似然估计选择法的效果进行系统比较。结果表明,基于条件概率积分变换的检验法可以有效解决多元Copula函数的选择问题,其拟合优度检验更精确、更稳定;核密度估计检验在大样本下比较稳定,而小样本下稳定性较差;相比之下,极大似然值检验法则不稳定。     

供应突发事件下基于CVaR的供应链订货决策及协调

供应突发事件下,引入条件风险值(conditional value at risk-CVa R)刻画了零售商的运营目标,构建了收益共享契约下的供应链订货模型,着重研究了CVa R下的供应链协调及零售商最优订货量对供应商可靠性及对其自身的风险规避系数的敏感性。研究表明:收益共享契约具有一定的鲁棒性,能协调突发事件风险下的供应链;风险规避型零售商的最优订货量总是不小于风险中性情况,且风险规避程度越高,订货量越大;最优订货量对供应商可靠性均值的敏感性不依赖于零售商的风险规避程度,且均值越小,最优订货量越大,这与风险中性情况是类似的;最优订货量对供应商可靠性标准差的敏感性则依赖于零售商的风险规避程度,当零售商的风险规避程度较高时,供应可靠性标准差越大,最优订货量越大,这与风险中性情况是相反的。     

融资融券交易与市场稳定性:基于动态视角的证据

通过构建“外生信息冲击的门限自回归条件密度(TARCD-X)”模型,进而从动态的视角考察了融资融券实施前后,市场涨跌和交易量增减4种重要的信息冲击对下一期市场稳定性的影响具有怎样的差异,以及融资融券实施后,融资融券余额变动作为新的信息冲击如何影响下一期市场的稳定性.以波动性、暴涨暴跌的不对称性和暴涨暴跌的频繁性3个指标衡量市场的稳定性,研究发现:(1)12种影响关系中,除交易量减小对市场波动性的冲击作用在融资融券实施后有所放大之外,其他11种影响关系均未出现不利变化;(2)融资融券余额的变动没有显著增加市场的波动性和暴涨暴跌的频繁性,但其与暴涨暴跌的不对称性存在显著的相关关系.后者为构建股市暴涨暴跌的预警指标提供了实证依据.     

超高频数据的日内效应调整方法研究

日内效应在金融高频数据研究中已被广泛证实,是一种日内周期性运动的动态效应,它影响了以微观金融指标为参数的计量模型的准确估计。基于金融超高频持续期数据,本文首先论述了日内效应调整的重要性,然后引入自适应映射(SOM)的方法对日内效应进行调整。SOM是一种基于神经网络学习的特征提取方法,能够动态识别高维数据中的结构特征,克服了静态调整方法的不足。最后通过建立基于自回归条件持续期模型(ACD)的蒙特卡罗模拟实验,比较了三种日内效应调整方法的效果。模拟结果表明SOM方法在日内效应调整中更为有效和稳定,特别适合大数据条件下的周期性结构分析。     

On generalized conditional cumulative residual inaccuracy measure

Abstract

Recently, the notion of cumulative residual Rényi’s entropy has been proposed in the literature as a measure of information that parallels Rényi’s entropy. Motivated by this, here we introduce a generalized measure of it, namely cumulative residual inaccuracy of order α. We study the proposed measure for conditionally specified models of two components having possibly different ages called generalized conditional cumulative residual inaccuracy measure. Several properties of generalized conditional cumulative residual inaccuracy measure including the effect of monotone transformation are investigated. Further, we provide some bounds on using the usual stochastic order and characterize some bivariate distributions using the concept of conditional proportional hazard rate model.     

Asymptotic normality of conditional density estimation under truncated,censored and dependent data

Abstract

In this paper, we focus on the left-truncated and right-censored model, and construct the local linear and Nadaraya-Watson type estimators of the conditional density. Under suitable conditions, we establish the asymptotic normality of the proposed estimators when the observations are assumed to be a stationary α-mixing sequence. Finite sample behavior of the estimators is investigated via simulations too.