混合性(Mixing)在时间序列和空间计量经济学研究中起着重要的作用,许多时间序列文献都假设其模型中的变量服从混合过程.然而,目前尚无关于判定空间计量经济模型所生成数据是否满足混合性质的准则.基于Doukhan(1994)的思想,基于若干常见...混合性(Mixing)在时间序列和空间计量经济学研究中起着重要的作用,许多时间序列文献都假设其模型中的变量服从混合过程.然而,目前尚无关于判定空间计量经济模型所生成数据是否满足混合性质的准则.基于Doukhan(1994)的思想,基于若干常见假设,我们建立了一系列准则,用于判定不规则格点上的线性空间过程是否满足α-混合性.我们将这些准则应用于建立由空间自回归模型、空间误差模型、矩阵指数空间模型以及基于潜在被解释变量的空间计量经济模型(例如空间样本选择模型)所生成被解释变量的α-混合性质.利用α-混合性质,我们建立了Flores-Lagunes et al.(2012)提出的空间样本选择模型的估计量的大样本性质.展开更多
Value-at-Risk(VaR)and expected shortfall(ES)are two key risk measures in financial risk management.Comparing these two measures has been a hot debate,and most discussions focus on risk measure properties.This paper us...Value-at-Risk(VaR)and expected shortfall(ES)are two key risk measures in financial risk management.Comparing these two measures has been a hot debate,and most discussions focus on risk measure properties.This paper uses independent data and autoregressive models with normal or t-distribution to examine the effect of the heavy tail and dependence on comparing the nonparametric inference uncertainty of these two risk measures.Theoretical and numerical analyses suggest that VaR at 99%level is better than ES at 97.5%level for distributions with heavier tails.展开更多
In this paper,the problem of identifying autoregressive-moving-average systems under random threshold binary-valued output measurements is considered.With the help of stochastic approximation algorithms with expanding...In this paper,the problem of identifying autoregressive-moving-average systems under random threshold binary-valued output measurements is considered.With the help of stochastic approximation algorithms with expanding truncations,the authors give the recursive estimates for the parameters of both the linear system and the binary sensor.Under reasonable conditions,all constructed estimates are proved to be convergent to the true values with probability one,and the convergence rates are also established.A simulation example is provided to justify the theoretical results.展开更多
本文在α-混合序列假设下,基于半参数变系数模型研究条件期望分位数风险价值(expectile-based value at risk,EVaR)的风险度量.此模型不仅考虑了风险因素的影响,还可以动态描述风险影响及交互效应.同时,EVaR比经典的风险在险价值(quanti...本文在α-混合序列假设下,基于半参数变系数模型研究条件期望分位数风险价值(expectile-based value at risk,EVaR)的风险度量.此模型不仅考虑了风险因素的影响,还可以动态描述风险影响及交互效应.同时,EVaR比经典的风险在险价值(quantile-based value at risk,QVaR)具有更直观、更易于计算的良好性质,而且对于资产分布的尾部损失更加敏感,在度量极端风险情形下,相对于QVaR更为有效和方便.本文采用三阶段估计的方法,分别对变系数部分和常系数部分的参数进行估计,并且给出3个阶段中每个估计的相合性和渐近正态性.为了节省计算时间,提高计算效率,本文采用一步估计的算法,减少迭代所需的时间.由于时间序列样本是非独立样本,建立这些统计量的大样本性质时带来了更大的困难.有别于独立同分布的观察数据,本文利用大小块分割方法发展α-混合序列的极限理论,获得了基于金融时间序列数据建立的模型参数和非参数估计的统计渐近性质.在数值模拟中,本文给出3个模型假设下变系数曲线估计和常系数估计的结果,无论是估计的精确度还是估计的稳健性,模拟结果都表明本文所提出的估计方法有优良的性质.实例则展示了本文所提出模型在上证指数的实际应用.展开更多
Based on left truncated and right censored dependent data, the estimators of higher derivatives of density function and hazard rate function are given by kernel smoothing method. When observed data exhibit α-mixing d...Based on left truncated and right censored dependent data, the estimators of higher derivatives of density function and hazard rate function are given by kernel smoothing method. When observed data exhibit α-mixing dependence, local properties including strong consistency and law of iterated logarithm are presented. Moreover, when the mode estimator is defined as the random variable that maximizes the kernel density estimator, the asymptotic normality of the mode estimator is established.展开更多
文摘混合性(Mixing)在时间序列和空间计量经济学研究中起着重要的作用,许多时间序列文献都假设其模型中的变量服从混合过程.然而,目前尚无关于判定空间计量经济模型所生成数据是否满足混合性质的准则.基于Doukhan(1994)的思想,基于若干常见假设,我们建立了一系列准则,用于判定不规则格点上的线性空间过程是否满足α-混合性.我们将这些准则应用于建立由空间自回归模型、空间误差模型、矩阵指数空间模型以及基于潜在被解释变量的空间计量经济模型(例如空间样本选择模型)所生成被解释变量的α-混合性质.利用α-混合性质,我们建立了Flores-Lagunes et al.(2012)提出的空间样本选择模型的估计量的大样本性质.
文摘Value-at-Risk(VaR)and expected shortfall(ES)are two key risk measures in financial risk management.Comparing these two measures has been a hot debate,and most discussions focus on risk measure properties.This paper uses independent data and autoregressive models with normal or t-distribution to examine the effect of the heavy tail and dependence on comparing the nonparametric inference uncertainty of these two risk measures.Theoretical and numerical analyses suggest that VaR at 99%level is better than ES at 97.5%level for distributions with heavier tails.
文摘In this paper,the problem of identifying autoregressive-moving-average systems under random threshold binary-valued output measurements is considered.With the help of stochastic approximation algorithms with expanding truncations,the authors give the recursive estimates for the parameters of both the linear system and the binary sensor.Under reasonable conditions,all constructed estimates are proved to be convergent to the true values with probability one,and the convergence rates are also established.A simulation example is provided to justify the theoretical results.
文摘本文在α-混合序列假设下,基于半参数变系数模型研究条件期望分位数风险价值(expectile-based value at risk,EVaR)的风险度量.此模型不仅考虑了风险因素的影响,还可以动态描述风险影响及交互效应.同时,EVaR比经典的风险在险价值(quantile-based value at risk,QVaR)具有更直观、更易于计算的良好性质,而且对于资产分布的尾部损失更加敏感,在度量极端风险情形下,相对于QVaR更为有效和方便.本文采用三阶段估计的方法,分别对变系数部分和常系数部分的参数进行估计,并且给出3个阶段中每个估计的相合性和渐近正态性.为了节省计算时间,提高计算效率,本文采用一步估计的算法,减少迭代所需的时间.由于时间序列样本是非独立样本,建立这些统计量的大样本性质时带来了更大的困难.有别于独立同分布的观察数据,本文利用大小块分割方法发展α-混合序列的极限理论,获得了基于金融时间序列数据建立的模型参数和非参数估计的统计渐近性质.在数值模拟中,本文给出3个模型假设下变系数曲线估计和常系数估计的结果,无论是估计的精确度还是估计的稳健性,模拟结果都表明本文所提出的估计方法有优良的性质.实例则展示了本文所提出模型在上证指数的实际应用.
文摘Based on left truncated and right censored dependent data, the estimators of higher derivatives of density function and hazard rate function are given by kernel smoothing method. When observed data exhibit α-mixing dependence, local properties including strong consistency and law of iterated logarithm are presented. Moreover, when the mode estimator is defined as the random variable that maximizes the kernel density estimator, the asymptotic normality of the mode estimator is established.