乘积误差模型的渐近性质

在较弱的条件下,从条件过程和无条件过程两个角度讨论了乘积误差模型解的渐近性质,得到了条件过程收敛于无条件过程的充分条件,任意阶矩有限的充要条件以及外生变量与内生变量持续性的充要条件.所得到的结论不但适用于已得到应用的平稳乘积误差模型,也适用于包含单位根的乘积误差模型和满足条件的其他类型的非平稳过程.     

带有重尾扰动项的非线性自回归模型

讨论在非线性自回归模型中平稳解边际尾概率与扰动项尾概率之间的关系. 证明了在保证模型平稳遍历的条件下, 一维平稳解的边际分布具有重尾概率性质, 并且与扰动项重尾概率指标相同.     

非对称广义自回归条件异方差的新模型

本文提出了一个新的非对称广义自回归条件异方差的新模型,证明了该模型宽平稳及其最简模型偶数价矩存在的充要条件。     

扩展自回归条件久期模型的概率性质

本文给出了扩展自回归条件久期模型(Augmented Autoregressive Conditional Duration Model,简记为AACD Model)严平稳遍历的充分必要条件.在一定的正则条件下研究了模型矩的性质.最后我们证明AACD过程在保证严平稳遍历的条件下,其边际分布具有重尾的概率性质.     

时间序列模型L_1-估计量的极限分布:平稳自回旧模型

时间序列模型的L_1-估计问题是非常重要的．这些估计量的许多性质都被研究过．然而，仍有一些基本问题有待解决．本文将给出平稳自回归模型L_1-估计量的极限分布．     

随机时间序列模型在正态MA(2)时的平稳性条件

对于随机时间序列模型Xt=φtXt-1 ε，由于当{φ}在不同的情况下．具有不同的平稳性质．本文利用特征函数和矩的关系讨论了模型Xt=φXt-1 εt，在序列{φt)为正态MA(2)条件时有平稳解的充分必要条件，并利用递归方法给出了较详细的证明．     

论一类资源最优配置问题及应用

本文考虑了一类资源最优配置问题.应用Kuhn-Tucher定理得到了这类问题最优解的充要条件.我们应用这个条件来考虑一类从工业投资、教育投资等问题中导出的最优投资模型,得到了这个问题最优解的充要条件,应用这个条件导出了求解这个模型的具有时间复杂度为o(mn)的多项式型新算法.     

中立双曲型时滞偏微分方程解振动的充要条件

本文讨论一类线性中立双曲型时滞微分方程解的振动性质，获得了其一切解振动的充要条件．     

含阻尼项双曲型时滞偏微分方程解振动的充要条件

本讨论一类含阻尼项双曲时滞微分方程解的振动性质，获得了一切解振动的充要条件．     

离散广义系统的平稳振荡

为了研究离散广义系统的平稳振荡，本文利用广义Ｌｙａｐｕｎｏｖ函数方法，给出了一个ｍ周期解存在的充要条件，得出了离散广义系统的周期解的存在性、唯一性、稳定性的有关定理，进而研究具有某种分解的复杂离散广义系统的平稳振荡问题，方法简单易行．     

金融市场波动性CARR类模型与GARCH类模型的比较研究

在CARR模型基础上提出它的衍生模型ABSCARR模型,并利用广义误差分布(GED)讨论了它们的条件残差分布问题,最后运用CARR类模型对高频金融时间序列进行了实证分析.研究结果表明:CARR及其衍生模型在高频金融时间序列的价格波动性捕捉方面具有良好的效果,而GED的引入可以很好的用于分析CARR模型具体的条件分布情况,而CARR模型的条件残差分布应该并非只有指数分布与威布尔分布两种形式.     

Birnbaum‐Saunders autoregressive conditional range model applied to stock index data

This article proposes a new approach to the conditional autoregressive range (CARR) model using the Birnbaum‐Saunders (BS) distribution. The model aims to develop volatility clustering, which incorporates extreme fluctuations, using a time‐varying evolution of the range process called the BSCARR model. Furthermore, diagnosis analysis tools for diagnosis analysis were developed to evaluate the goodness of fit, such as residual analysis, global influence measures based on Cook's distance, and local influence analysis. For illustrative purposes, three real financial market indices are analyzed. A comparison with classical CARR models was also carried out in these examples. The results indicated that the proposed model outperformed some existing models in the literature, especially a recent CARR model based on the gamma distribution even under the presence of atypical cases (observed values).     

A scalar dynamic conditional correlation model: Structure and estimation

The dynamic conditional correlation(DCC) model has been widely used for modeling the conditional correlation of multivariate time series by Engle(2002). However, the stationarity conditions have been established only recently and the asymptotic theory of parameter estimation for the DCC model has not yet to be fully discussed. In this paper, we propose an alternative model, namely the scalar dynamic conditional correlation(SDCC) model. Sufficient and easily-checked conditions for stationarity, geometric ergodicity, andβ-mixing with exponential-decay rates are provided. We then show the strong consistency and asymptotic normality of the quasi-maximum-likelihood estimator(QMLE) of the model parameters under regular conditions.The asymptotic results are illustrated by Monte Carlo experiments. As a real-data example, the proposed SDCC model is applied to analyzing the daily returns of the FSTE(financial times and stock exchange) 100 index and FSTE 100 futures. Our model improves the performance of the DCC model in the sense that the Li-Mc Leod statistic of the SDCC model is much smaller and the hedging efficiency is higher.     

Nonlinear Least Squares Estimation of Log-ACD Models

This paper studies a nonlinear least squares estimation method for the logarithmic autoregressive conditional duration (Log-ACD) model. We establish the strong consistency and asymptotic normality for our estimator under weak moment conditions suitable for applications involving heavy-tailed distributions. We also discuss inference for the Log-ACD model and Log-ACD models with exogenous variables. Our results can be easily translated to study Log-GARCH models. Both simulation study and real data analysis are conducted to show the usefulness of our results.     

The Covariance Adjusted Location Linear Discriminant Function for Classifying Data with Unequal Dispersion Matrices in Different Locations

Classification between two populations dealing with both continuous and binary variables is handled by splitting the problem into different locations. Given the location specified by the values of the binary variables, discrimination is performed using the continuous variables. The location probability model with homoscedastic across location conditional dispersion matrices is adopted for this problem. In this paper, we consider presence of continuous covariates with heterogeneous location conditional dispersion matrices. The continuous covariates have equal location specific mean in both populations. Conditional homoscedasticity fails when strong interaction between the continuous and binary variables is present. A plug-in covariance adjusted rule is constructed and its asymptotic distribution is derived. An asymptotic expansion for the overall error rate is given. The result is extended to include binary covariates.     

马氏调制费率的带干扰风险模型

考虑了一类具有马氏调制的带干扰连续时间风险模型,得到了该模型下其条件Gerber-Shiu折现罚金函数所满足的积分方程,Laplace变换及渐近解.在两状态情形下,当索赔额的分布为有理数情况时得到了条件Gerber-Shiu折现罚金函数的具体表达式并给出了数值例子     

一类四阶非线性奇摄动方程的边值问题

利用匹配渐近展开法,讨论了一类四阶非线性方程的具有两个边界层的奇摄动边值问题．引进伸长变量,根据边界条件与匹配原则,在一定的可解性条件下,给出了外部解和左右边界层附近的内层解,得到了该问题的二阶渐近解,并举例说明了这类非线性问题渐近解的存在性．     

Confidence Intervals of Variance Functions in Generalized Linear Model

In this paper we introduce an appealing nonparametric method for estimating variance and conditional variance functions in generalized linear models （GLMs）, when designs are fixed points and random variables respectively, Bias-corrected confidence bands are proposed for the （conditional） variance by local linear smoothers. Nonparametric techniques are developed in deriving the bias-corrected confidence intervals of the （conditional） variance. The asymptotic distribution of the proposed estimator is established and show that the bias-corrected confidence bands asymptotically have the correct coverage properties. A small simulation is performed when unknown regression parameter is estimated by nonparametric quasi-likelihood. The results are also applicable to nonparamctric autoregressive times series model with heteroscedastic conditional variance.     

重尾情形下NA随机变量的随机加权和

In 2003, Tang Qihe et al. obtained a simple asymptotic formula for independent identically distributed （i.i.d.） random variables with heavy tails. In this paper, under certain moment conditions, we establish a formula as the same as Tang’s, when random variables are negatively associated （NA）.     

Estimation of the offspring mean in a supercritical branching process with non-stationary immigration

In this paper, we consider the conditional least squares estimator (CLSE) of the offspring mean of a branching process with non-stationary immigration based on the observation of population sizes. In the supercritical case, assuming that the immigration variables follow known distributions, conditions guaranteeing the strong consistency of the proposed estimator will be derived. The asymptotic normality of the estimator will also be proved. The proofs are based on direct probabilistic arguments, unlike the previous papers, where functional limit theorems for the process were used.