基于权函数的GARCH模型稳健建模及实证分析

异常值的存在会对时间序列波动率模型的识别及参数估计会产生重要影响,采用Tukey双权法权函数对被拟合相关序列模型的残差进行变换,再将变换后的残差序列对波动率模型进行稳健识别及建模,模拟及实证分析表明稳健识别及估计方法具有很好的耐抗性,而且能更好的捕捉到资产收益率的波动性.     

有效矩估计在中国股票市场的应用研究

对期权定价模型的一类拓展模型-随机波动率(SV)模型,由于模型中存在不可观测的随机波动因素,并且其精确似然函数很难得到,于是提出了一种基于标的资产价格历史数据的有效矩估计(EMM)方法,此方法是把观测数据映射到简化的辅助模型GARCH(1,1)上,并计算辅助模型得分用以建立矩条件,实现SV模型参数的有效估计.利用这一方法对中国股市进行了波动分析,得出了较好的结果.     

基于随机波动模型的沪深股市波动分析——以06,07年度沪深股指为例

基于马尔科夫链蒙特卡罗(MCMC)模拟的贝叶斯(Bayes)分析方法,应用随机波动(SV)模型实证分析06、07年度中国股票市场指数的波动性,并对比沪市与深市的股指,对不同形式的SV模型的参数进行估计,对结论作出合理的解释.     

基于Weibull分布的最大风速变点估计

利用三参数的Weibull分布分析安徽砀山气象站年最大风速数据,建立似然比变点模型和回归变点模型,并对年最大风速序列的变点进行检验和估计.考虑非气象因素对年最大风速序列的影响,选取A气象站附近数个气象站的年最大风速序列作为参考序列,通过比值法,消除气象因素的影响.计算结果表明,由似然比变点模型可以估计出A气象站最大风速变点出现在2003年,由回归变点模型估计出的变点出现在2009年和2003年.结合安徽砀山气象站实际迁移和海拔变化的历史沿革,可以发现,基于Weibull分布的变点模型成功检测出了2009年台站迁移和2003年前后台站迁移且海拔显著变化的情况.     

基于小波变换的LMSV模型对人民币汇率波动的研究

将小波引入到LMSV模型波动长记忆性的估计与检验中,提出了基于小波变换的LMSV模型波动长记忆性的伪极大似然估计法和波动长记忆性的检验方法,并对各汇率波动序列长记忆效应的大小程度进行了验证．结果表明各汇率波动序列存在长记忆效应．人民币对美元的汇率波动序列受历史信息的影响程度最高．     

基于Gibbs抽样的贝叶斯稳健ARMA模型研究

针对ARMA模型建模过程中模型识别和参数估计易受观测值异常点影响问题,构建了同时考虑加性异常点和更新性异常点的ARMA模型．运用基于Gibbs抽样的Markov Chain Monte Carlo贝叶斯方法,估计稳健ARMA模型参数,同步确定观测值中异常点的位置,辨别异常点类型．并利用我国人口自然增长数据进行仿真分析,研究结果表明：贝叶斯方法能够有效地识别ARMA序列的异常点．     

基于ICA-SV模型的金融市场协同波动溢出分析及实证研究

对于动态投资组合与风险管理来说,测定波动溢出效应是非常重要的.已有的文献证明SV模型比GARCH模型能够更好地刻画金融市场的波动,使用SV模型研究两个金融市场间波动溢出的文献并不多见,而使用SV模型研究多个金融市场对一个金融市场协同波动溢出的文献则更为少见.本文以独立成分表示金融市场波动的协同指标,提出了独立成分SV模型(ICA-SV),并研究了多个金融市场对一个金融市场的协同波动溢出,实证结果验证了ICA-SV模型在分析金融市场协同波动溢出是可行的.     

厚尾随机波动率模型的贝叶斯参数估计及实证研究

针对现有时间序列模型难以刻画参数渐变性的问题,对厚尾随机波动(SV)模型的参数估计方法进行了推广,采用基于贝叶斯的MCMC方法,选取2013年5月~2016年6月这一经历多轮震荡的上证指数作为实证分析对象,构造了基于Gibbs抽样的MCMC过程进行仿真分析.结果显示,以卡方分布作为厚尾参数的先验分布能够有效地描述数据波动的厚尾特征,并且能得到较高精度的参数估计结果.结果表明,厚尾SV模型能有效反映出我国股市尖峰厚尾和波动长期记忆性的特征.     

存货质押融资质押物价格变化MRS-GRACH模型研究

针对质押物价格收益序列存在的结构转换特征,对常系数ARCH模型进行改进,引入一个变化服从马尔科夫过程的状态随机变量反映价格收益不同的波动状况,从而构建了质押物价格收益MRS-GARCH模型.实例研究表明MRS-GARCH模型能够刻画现实中质押物价格收益波动结构动态变化过程,同时能够识别外界不可见因素对收益波动的影响力度,MRS-GARCH模型较GARCH模型在拟合及预测价格收益波动方面具有更准确的效果.     

中国黄金期货市场波动特征及模型预测研究

选取上海期货交易所黄金期货价格指数日内10分钟高频收益数据,构造了经调整的已实现极差波动率估计序列,利用6类GARCH模型建模分析,描述了黄金期货价格指数的波动特征.运用多种损失函数比较了GARCH类模型样本外波动率预测精度的优劣,并在此基础上,采用一种渐进正态分布检验法评估了GARCH类模型的预测效果.结果显示,黄金期货已实现极差波动率估计序列具有尖峰厚尾、集聚性、持续性等特征.对于黄金期货市场,ACD-GARCH模型具有相对最好的波动率预测能力.     

Threshold variable selection of asymmetric stochastic volatility models

A threshold stochastic volatility (SV) model is used for capturing time-varying volatilities and nonlinearity. Two adaptive Markov chain Monte Carlo (MCMC) methods of model selection are designed for the selection of threshold variables for this family of SV models. The first method is the direct estimation which approximates the model posterior probabilities of competing models. Using parallel MCMC sampling to estimate these probabilities, the best threshold variable is selected with the highest posterior model probability. The second method is to use the deviance information criterion to compare among these competing models and select the best one. Simulation results lead us to conclude that for large samples the posterior model probability approximation method can give an accurate approximation of the posterior probability in Bayesian model selection. The method delivers a powerful and sharp model selection tool. An empirical study of five Asian stock markets provides strong support for the threshold variable which is formulated as a weighted average of important variables.     

Diagnostic Plots for Robust Multivariate Methods

Robust techniques for multivariate statistical methods—such as principal component analysis, canonical correlation analysis, and factor analysis—have been recently constructed. In contrast to the classical approach, these robust techniques are able to resist the effect of outliers. However, there does not yet exist a graphical tool to identify in a comprehensive way the data points that do not obey the model assumptions. Our goal is to construct such graphics based on empirical influence functions. These graphics not only detect the influential points but also classify the observations according to their robust distances. In this way the observations are divided into four different classes which are regular points, nonoutlying influential points, influential outliers, and noninfluential outliers. We thus gain additional insight in the data by detecting different types of deviating observations. Some real data examples will be given to show how these plots can be used in practice.     

SV模型下的期权定价和风险计量

本文利用SV（Stochastic Variance）模型对期权基础资产的收益过程进行统计描述,在同时给出期权定价和市场风险计量之后,又给出定价置信区间和风险置信区间的估计。文中对SV模型作了分析和比较,利用自适应滤波方法对模型的建立和参数的估计给出了简单的方法,最后还对SV模型作了模拟分析并计算了期权定价和风险计量的一个例子。     

中国股市量价关系分析中的后验分布构造与模拟

研究中国股票市场中的两个重要指标:股票价格与交易量,随机波动模型具有长期波动性预测能力,只是由于参数估计的困难而没有受到重视.随着马尔可夫链蒙特卡罗(MCMC)方法和计算机计算能力的提高,这种困难是可以克服的.采用基于马尔可夫链蒙特卡罗(MCMC)模拟技术的贝叶斯估计方法,在基于引入预期交易量和非预期交易量的随机波动模型下,对模型参数进行后验分布的构造,并以2005年1月-2012年5月的上证综合指数的每日收盘指数及相应的日成交量序列为样本,通过实证仿真得到参数结果值.结果表明,非预期交易量对股市价格的影响要大于预期交易量.     

基于区间数的拓展DEMATEL方法及其应用研究

DEMATEL方法(决策试验与试验评估法)是一种运用图论与矩阵工具进行系统因素分析的方法。但此方法对系统因素间关系的评价仅限于实数域内,往往不适合描述现实生活中系统因素间复杂的影响关系。鉴于区间数能更有效地描述复杂的现象,本文将传统的DEMATEL方法拓展到了区间数领域,来弥补DEMATEL方法的这一不足。为此,本文建立了区间数初始直接影响矩阵,借助于区间数的运算法则和可能度排序,计算出区间数综合影响矩阵,并对系统因素进行分析,从而,提出区间数DEMATEL方法。然后采用区间数DEMATEL方法识别IT外包中知识转移影响因素,得到IT外包中知识转移过程中的原因影响因素、结果影响因素以及每个影响因素的重要程度,以此为IT外包人员给予相应的建议,同时也验证了该方法的实效性。     

Measures of influence for the functional linear model with scalar response

This paper studies how to identify influential observations in the functional linear model in which the predictor is functional and the response is scalar. Measurement of the effects of a single observation on estimation and prediction when the model is estimated by the principal components method is undertaken. For that, three statistics are introduced for measuring the influence of each observation on estimation and prediction of the functional linear model with scalar response that are generalizations of the measures proposed for the standard regression model by [D.R. Cook, Detection of influential observations in linear regression, Technometrics 19 (1977) 15-18; D. Peña, A new statistic for influence in linear regression, Technometrics 47 (2005) 1-12] respectively. A smoothed bootstrap method is proposed to estimate the quantiles of the influence measures, which allows us to point out which observations have the larger influence on estimation and prediction. The behavior of the three statistics and the quantile estimation bootstrap based method is analyzed via a simulation study. Finally, the practical use of the proposed statistics is illustrated by the analysis of a real data example, which show that the proposed measures are useful for detecting heterogeneity in the functional linear model with scalar response.     

The influence of individual claims on the chain-ladder estimates: Analysis and diagnostic tool

The chain-ladder method is a widely used technique to forecast the reserves that have to be kept regarding claims that are known to exist, but for which the actual size is unknown at the time the reserves have to be set. In practice it can be easily seen that even one outlier can lead to a huge over- or underestimation of the overall reserve when using the chain-ladder method. This indicates that individual claims can be very influential when determining the chain-ladder estimates. In this paper the effect of contamination is mathematically analyzed by calculating influence functions in the generalized linear model framework corresponding to the chain-ladder method. It is proven that the influence functions are unbounded, confirming the sensitivity of the chain-ladder method to outliers. A robust alternative is introduced to estimate the generalized linear model parameters in a more outlier resistant way. Finally, based on the influence functions and the robust estimators, a diagnostic tool is presented highlighting the influence of every individual claim on the classical chain-ladder estimates. With this tool it is possible to detect immediately which claims have an abnormally positive or negative influence on the reserve estimates. Further examination of these influential points is then advisable. A study of artificial and real run-off triangles shows the good performance of the robust chain-ladder method and the diagnostic tool.     

Logistic回归模型的影响分析

Logistic回归模型的影响分析是Logistic回归诊断研究中的重要内容。常用的分析方法都是轮换地删除数据点后的逐步判断,而这个判断的过程主要体现在模型的诊断图上。鉴于此,通过构造诊断统计量来有效地开发诊断图成为影响分析的核心内容,并由此能较为准确地探寻出模型的强影响点。本文通过对Logistic回归模型帽子矩阵的分解以及对轮换地删除数据点后的系数估计的相对变化量进行加权,得出Logistic回归模型诊断图使其能比传统的诊断图更准确地判断出模型的强影响点。     

Influence Diagnostics in Partially Varying-Coefficient Models

When a real-world data set is fitted to a specific type of models,it is often encountered that oneor a set of observations have undue influence on the model fitting,which may lead to misleading conclusions.Therefore,it is necessary for data analysts to identify these influential observations and assess their impacton various aspects of model fitting.In this paper,one type of modified Cook's distances is defined to gaugethe influence of one or a set observations on the estimate of the constant coefficient part in partially varying-coefficient models,and the Cook's distances are expressed as functions of the corresponding residuals andleverages.Meanwhile,a bootstrap procedure is suggested to derive the reference values for the proposed Cook'sdistances.Some simulations are conducted,and a real-world data set is further analyzed to examine theperformance of the proposed method.The experimental results are satisfactory.