金融资产收益率尾概率估计研究

在对金融资产进行投资时,投资者所关注的问题往往是金融资产收益率发生大波动的概率,简称尾概率.本文利用大偏差定理对此概率如何进行估计进行深入研究.将收益率按其尾部的分布特征分成三类,分别对其进行研究,得到三种不同的估计公式.本文对收益率序列存在相关性、收益率是多元随机变量情况下的尾概率估计问题也进行了分析.     

沪深股市大盘指数收益率分布尾部的实证研究

通过H ill估计的改进方法对上证综合指数和深圳成分指数的收益率分布的尾部指数进行了参数估计,用χ2检验验证了指数的稳定性及其置信区间.在此基础上提出用尾部指数估计尾概率,达到风险控制的目的.实证研究表明,沪深大盘指数收益率分布具有肥尾的特征,但并不服从无限方差分布.     

基于Zipf律的尾部特征分析及VaR计算

分布的尾部特征分析在许多领域都非常重要，估计和分辨尾部服从幂律特征还是指数特征非常重要。在本文中，我们提出了在分析数据的Zipf幂律的基础上来分辨尾部特征的方法。通过实证分析，我们得出了上证指数收益率的确存在具有尺度不变性的Zipf幂律现象，然后分布的尾部特征就被确定，并得到了尾部指数的一种简单的估计方法，最后对该市场的在险价值（VaR）进行了计算和分析。     

重尾分布的尾部指数估计及沪深股市实证分析

重尾分布尾部指数α的估计依赖于样本中所用顺序统计量个数k的选取.本文介绍了估计α时选择k的两类不同的方法:Sum-plot方法和Bootstrap方法,并对Hall提出的Bootstrap方法作了改进,称为M-Bootstrap方法.本文利用上述方法对已知分布进行Monte-Carlo模拟,研究它们的可行性,然后对上海和深圳两市股指数据进行了实证分析.计算结果表明,上海和深圳股指收益率具有重尾性.是右偏态的,右尾厚于左尾.通过几种方法计算的结果比较发现Sum-plot方法和M-Bootstrap方法在估计重尾指数上精确性较高一些,而且不受异常值的影响.     

股票收益率和新息的尾部估计

利用极值理论来考虑上证综指收益率的尾部.为了选择合理的超越门限,采用平均剩余函数和De-Haan矩估计相结合的方法.在学生t分布和广义误差分布的新患假设下,用GARCH和EGARCH新息的ARMA模型拟合指数收益率,并且使用极值理论的极大似然方法估计模型残差的尾指,估计结果表明收益率的尾指和模型的残差尾指基本一致.     

Copula函数的选择：方法与应用

针对目前Copula函数在实际应用中的选择问题,本文通过非参数法得到了它们的分布函数图及其经验分布图并进行了比较,然后利用一种解析法对其进一步的选择,并通过Q-Q图比较了各种模型的拟合程度,最后进行了拟合优度检验,得到了最优的Copula。最后对国内的上证A股指数和上证B股指数进行了实证分析,结果体现了该方法的有效性。     

汽车交通事故损失分布的尾部估计

由于巨额损失对保险公司的影响非常大,费率厘定过程中对极值分布的研究非常重视。本文从极值理论的角度出发,以上海市虹口区2003年的汽车交通事故损失数据为样本,探讨了损失分布的尾部估计方法,并利用该地区2006年的汽车交通事故损失对结论进行了验证。研究结果发现,广义帕雷托分布确实对损失额的尾部提供了较好的拟合,但这依赖于门槛值的恰当选择。传统的门槛值选择方法主观性较强,而通过重复多次的交叉验证技巧,我们可以估计广义帕雷托分布的最合适门槛值位置。     

上海股市信息散布过程实证分析

本文基于日内高频数据实证分析了上海A股、B股市场的收益率和交易量之间的同时和动态关系,实证结果表明在上海股市信息渐达假说较之混合分布假说更有一定的适用性,而且A股市场的信息不对称程度要大于B股市场,并进而带来了低效率。     

基于SGT分布的ES估计、后验分析及在沪深股市中应用

由于VaR可能低估尾部风险,巴塞尔委员会在第三次巴塞尔协议~([1])中建议将ES取代VaR作为主要的风险度量工具,因此,有必要提出更精确且稳健的ES估计模型。鉴于股票收益率序列通常同时呈现出尖峰、厚尾、偏斜等特征,为更全面地刻画这些特征,本文采用具有三个形状参数的广义偏t分布(Skewed Generalized T Distribution,SGT)刻画收益率序列的分布形状,该分布囊括了多种常见的主流分布,通过结合能够刻画收益率序列杠杆性的EGARCH模型来估计收益率序列的ES,然后使用Du和Escanciano~([2])最近提出的ES后验分析方法对其稳健性进行评估。在实证研究中,本文将该模型用于估计我国上证综指和深圳成指的日ES,结果表明,本文提出的EGARCH-SGT模型相比常见的基于偏t分布和学生t分布的EGARCH模型明显呈现出对收益率序列更好的拟合效果,且基于该模型估计的ES顺利通过了后验分析,表现出较好的稳健性。     

沪深股市收益率的尾部相关函数

尾部相关性是相关性分析中重要的一类,利用度量尾部相关性的指标χ,χ-以及尾部相关函数ρ(θ)来分析尾部相关性,并给出ρ(θ)的一种非参数估计方法.通过这两种方法研究上证综合指数和深证成分指数日收盘指数对数收益率在损失情况下的尾部相关性,结果表明两市指数日对数收益率具有很强的尾部相关性.     

带删失结构的第二类Tobit模型的两步参数估计法

本文首先简要介绍一类样本选择模型并且对其研究方法进行回顾,然后基于样本选择模型,提出本文所研究的模型:带删失结构的第二类Tobit模型,提出一种新的两步估计法来对模型的参数进行估计。并且对提出的方法进行数据模拟,数据模拟结果表明估计的效果良好。     

基于参数估计线性回归模型选择的研究

关于线性回归模型选择 ,[1 ]中介绍了许多方法 ,他们均基于残差平方和下建立的选择准则 .本文试基于参数估计的理论给出一种方法 ,从参数估计的优良性质上来说 ,我们认为是合理的 .同时给出了计算方法及应用实例 .     

部分线性单指标模型的复合分位数回归及变量选择

本文提出复合最小化平均分位数损失估计方法 (composite minimizing average check loss estimation,CMACLE)用于实现部分线性单指标模型(partial linear single-index models,PLSIM)的复合分位数回归(composite quantile regression,CQR).首先基于高维核函数构造参数部分的复合分位数回归意义下的相合估计,在此相合估计的基础上,通过采用指标核函数进一步得到参数和非参数函数的可达最优收敛速度的估计,并建立所得估计的渐近正态性,比较PLSIM的CQR估计和最小平均方差估计(MAVE)的相对渐近效率.进一步地,本文提出CQR框架下PLSIM的变量选择方法,证明所提变量选择方法的oracle性质.随机模拟和实例分析验证了所提方法在有限样本时的表现,证实了所提方法的优良性.     

Weak signals in high‐dimensional regression: Detection,estimation and prediction

Regularization methods, including Lasso, group Lasso, and SCAD, typically focus on selecting variables with strong effects while ignoring weak signals. This may result in biased prediction, especially when weak signals outnumber strong signals. This paper aims to incorporate weak signals in variable selection, estimation, and prediction. We propose a two‐stage procedure, consisting of variable selection and postselection estimation. The variable selection stage involves a covariance‐insured screening for detecting weak signals, whereas the postselection estimation stage involves a shrinkage estimator for jointly estimating strong and weak signals selected from the first stage. We term the proposed method as the covariance‐insured screening‐based postselection shrinkage estimator. We establish asymptotic properties for the proposed method and show, via simulations, that incorporating weak signals can improve estimation and prediction performance. We apply the proposed method to predict the annual gross domestic product rates based on various socioeconomic indicators for 82 countries.     

Bootstrap bandwidth selection in kernel density estimation from a contaminated sample

In this paper we consider kernel estimation of a density when the data are contaminated by random noise. More specifically we deal with the problem of how to choose the bandwidth parameter in practice. A theoretical optimal bandwidth is defined as the minimizer of the mean integrated squared error. We propose a bootstrap procedure to estimate this optimal bandwidth, and show its consistency. These results remain valid for the case of no measurement error, and hence also summarize part of the theory of bootstrap bandwidth selection in ordinary kernel density estimation. The finite sample performance of the proposed bootstrap selection procedure is demonstrated with a simulation study. An application to a real data example illustrates the use of the method. This research was supported by ‘Projet d’Actions de Recherche Concertées’ (No. 98/03-217) from the Belgian government. Financial support from the IAP research network nr P5/24 of the Belgian State (Federal Office for Scientific, Technical and Cultural Affairs) is also gratefully acknowledged.     

One-leg variable-coefficient formulas for ordinary differential equations and local–global step size control

In this paper we discuss a class of numerical algorithms termed one-leg methods. This concept was introduced by Dahlquist in 1975 with the purpose of studying nonlinear stability properties of multistep methods for ordinary differential equations. Later, it was found out that these methods are themselves suitable for numerical integration because of good stability. Here, we investigate one-leg formulas on nonuniform grids. We prove that there exist zero-stable one-leg variable-coefficient methods at least up to order 11 and give examples of two-step methods of orders 2 and 3. In this paper we also develop local and global error estimation techniques for one-leg methods and implement them with the local–global step size selection suggested by Kulikov and Shindin in 1999. The goal of this error control is to obtain automatically numerical solutions for any reasonable accuracy set by the user. We show that the error control is more complicated in one-leg methods, especially when applied to stiff problems. Thus, we adapt our local–global step size selection strategy to one-leg methods.     

产品可靠度的E-Bayes估计

提出了参数估计的一种新方法——E-Bayes估计法.对Pascal分布,给出了可靠度的E-Bayes估计的定义(在先验分布中有一个超参数情形),在此基础上给出了可靠度的E-Bayes估计,并给出了可靠度的E-Bayes估计性质——E-Bayes估计和多层Bayes估计的关系.最后,给出了模拟算例,结果表明本文提出的E-Bayes估计法可行且便于工程应用.     

Moment Estimation for Multivariate Extreme Value Distribution in a Nested Logistic Model

This paper considers multivariate extreme value distribution in a nested logistic model. The dependence structure for this model is discussed. We find a useful transformation that transformed variables possess the mixed independence. Thus, the explicit algebraic formulae for a characteristic function and moments may be given. We use the method of moments to derive estimators of the dependence parameters and investigate the properties of these estimators in large samples via asymptotic theory and in finite samples via computer simulation. We also compare moment estimation with a maximum likelihood estimation in finite sample sizes. The results indicate that moment estimation is good for all practical purposes.     

Plug-in method for nonparametric regression

The problem of bandwidth selection for non-parametric kernel regression is considered. We will follow the Nadaraya–Watson and local linear estimator especially. The circular design is assumed in this work to avoid the difficulties caused by boundary effects. Most of bandwidth selectors are based on the residual sum of squares (RSS). It is often observed in simulation studies that these selectors are biased toward undersmoothing. This leads to consideration of a procedure which stabilizes the RSS by modifying the periodogram of the observations. As a result of this procedure, we obtain an estimation of unknown parameters of average mean square error function (AMSE). This process is known as a plug-in method. Simulation studies suggest that the plug-in method could have preferable properties to the classical one. Supported by the MSMT: LC 06024.     

Double penalized variable selection procedure for partially linear models with longitudinal data

Based on the double penalized estimation method,a new variable selection procedure is proposed for partially linear models with longitudinal data.The proposed procedure can avoid the effects of the nonparametric estimator on the variable selection for the parameters components.Under some regularity conditions,the rate of convergence and asymptotic normality of the resulting estimators are established.In addition,to improve efficiency for regression coefficients,the estimation of the working covariance matrix is involved in the proposed iterative algorithm.Some simulation studies are carried out to demonstrate that the proposed method performs well.