基于GARCH族模型的中国旅游酒店板块指数收益率波动分析

运用GARCH族模型分析旅游酒店板块指数日收益率的波动特征,研究表明：旅游酒店板块收益率是一个平稳过程,其波动具有“聚集”现象和“非对称效应”。GARCH（2,1）模型比GARCH（1,1）模型更好地消除了收益率序列的异方差性;TARCH（2,1）模型的拟合效果最好;GARCH—M模型和非对称的CARCH（1,1）模型都不适用于描述收益率的波动特征。     

基于ANN-GARCH模型的中国股市收益非对称波动拟合能力研究

在传统的非对称GARCH模型上加入ANN逻辑项,从而提高了模型的非对称性描述能力。不同于"黑箱"式的神经网络计算,这种方法的ANN逻辑项是可见、可分析的。通过对上证综指、深证综指和恒生指数的实证研究,发现三个市场都存在"杠杆效应"。研究表明:ANN-GARCH模型总体上比传统模型的拟合效果更好,预测能力更强。     

对混合模型经济预测效率的考证

本文将季节乘积ARIMA模型及混合模型方法运用于经济时间序列,运用SPSS10.0实证了两种方法建模和预测的过程和效率。从预测结果可见,本文所介绍的混合模型算法比单独使用ARIMA季节乘积模型辨识精度高,对于含有趋势性和周期性的经济时间序列辨识、预测及降低组合模型的预测误差具有一定的实用价值。     

股价预测的GM(1,1)模型

本文应用灰色系统理论,对股票价格变化建立GM(1,1)预测模型,并进行了实证分析.结果表明,把股票价格动态变化过程看作一个灰色系统,利用所建立的模型可较好地预测股票价格的短期发展变化趋势;同时通过与用ARIMA模型预测的拟合比较,表明在对股票价格作短期预测时,用GM(1,1)模型进行预测比用ARIMA模型进行预测具有更高的精确度.     

基于非参数GARCH模型的汇率波动性预测

文章利用非参数GARCH模型来预测人民币汇率的波动性,并且与参数GARCH族模型的预测结果进行比较。理论上,非参数GARCH模型避免了参数GARCH族模型形式上的错误设定,具有稳健性。文章选择美元和日元兑人民币汇率的日对数收益率来进行预测,预测结果综合表明非参数GARCH模型具有最强的预测能力。     

基于已实现和极差波动率标准的沪深300指数波动率模型研究

对波动率预测模型的研究备受学术界的关注,文章以沪深300指数的5分钟高频真实交易数据为研究对象,利用8个损失函数和Diebold Marinao检验较为全面的探讨评价了GARCH族模型对其预测能力。结果发现长记忆性模型的预测效果普遍好于短记忆性GARCH模型,且FIEGARCH是长记忆性模型中预测效果较好的,这对学术界和实务界进行风险测度及对我国资本市场的风险控制都具有现实意义。     

GARCH(1,1)模型的M-H估计及其应用

参数GARCH模型是最常用的度量金融市场波动性的模型。文章对残差基于正态分布的GARCH(1,1)模型通过构造M-H算法对其参数进行了估计,并给出了基于沪市股指收益率数据的实证分析。结果表明:基于M-H算法估计的GARCH模型比基于极大似然估计(ML)方法估计的GARCH模型具有更好的拟合效果和预测能力。     

基于混频已实现GARCH模型的波动预测与VaR度量

本文将Hansen等（2012）的Realized GARCH模型扩展为包含日内收益率、日收益率以及已实现波动率的混频已实现GARCH模型（M-Realized GARCH模型）。该模型将日内交易分为前后两段,引入了混频均值方程,并对混频均值方程的残差分别建立条件波动率方程和已实现日波动率方程。本文采用2013-2016年沪深300指数混频数据,分别在扰动项服从正态分布、t分布和广义误差分布的假设下,采用损失函数、SPA检验、kupiec检验和动态分位数检验法,对GARCH、Realized GARCH和M-Realized GARCH模型的波动率预测和VaR度量效果对比研究,得出M-Realized GARCH模型能提高预测精度,且VaR实际失败率与理论失败率一致,失败发生之间不相关。最后,本文利用Block bootstrap方法抽样得到混频数据,模拟证明了M-Realized GARCH模型比Realized GARCH模型具有更高的预测精度。     

基于MCMC方法的GARCH模型参数估计

参数GARCH模型是最常用的度量金融市场波动性的模型.运用马尔科夫蒙特卡罗(MCMC)方法对残差基于正态分布的GARCH(1,1)的参数进行估计,由沪市股指收益率数据的实证分析结果表明:基于马尔科夫蒙特卡罗(MCMC)方法估计的GARCH模型比基于极大似然估计(ML)方法估计的GARCH模型具有更好的拟合效果和预测能力.     

正态方差混合分布新息GARCH模型的EM估计

近来,人们对实际数据使用厚尾分布进行建模颇感兴趣。一种流行的考虑就是所谓的广义自回归条件异方差(GARCH)模型。不幸的是,在一些应用中正态新息的GARCH模型的尾部不够厚。文章提出新息为正态方差混合分布的GARCH模型并给出了使用EM算法对模型参数作估计的步骤。结果表明,新息为正态方差混合新息分布的GARCH模型比正态新息的GARCH模型有更厚的尾部,因而更能捕捉实际数据中的厚尾特征。文章还以上证指数为例阐述了这一结论。     

Frequentist model averaging for envelope models

The envelope method produces efficient estimation in multivariate linear regression, and is widely applied in biology, psychology, and economics. This paper estimates parameters through a model averaging methodology and promotes the predicting abilities of the envelope models. We propose a frequentist model averaging method by minimizing a cross-validation criterion. When all the candidate models are misspecified, the proposed model averaging estimator is proved to be asymptotically optimal. When correct candidate models exist, the coefficient estimator is proved to be consistent, and the sum of the weights assigned to the correct models, in probability, converges to one. Simulations and an empirical application demonstrate the effectiveness of the proposed method.     

A test of separate hypotheses for comparing linear mixed models with non nested fixed effects

As researchers increasingly rely on linear mixed models to characterize longitudinal data, there is a need for improved techniques for selecting among this class of models which requires specification of both fixed and random effects via a mean model and variance-covariance structure. The process is further complicated when fixed and/or random effects are non nested between models. This paper explores the development of a hypothesis test to compare non nested linear mixed models based on extensions of the work begun by Sir David Cox. We assess the robustness of this approach for comparing models containing correlated measures of body fat for predicting longitudinal cardiometabolic risk.     

The Dantzig Selector in Cox's Proportional Hazards Model

Abstract. The Dantzig selector (DS) is a recent approach of estimation in high‐dimensional linear regression models with a large number of explanatory variables and a relatively small number of observations. As in the least absolute shrinkage and selection operator (LASSO), this approach sets certain regression coefficients exactly to zero, thus performing variable selection. However, such a framework, contrary to the LASSO, has never been used in regression models for survival data with censoring. A key motivation of this article is to study the estimation problem for Cox's proportional hazards (PH) function regression models using a framework that extends the theory, the computational advantages and the optimal asymptotic rate properties of the DS to the class of Cox's PH under appropriate sparsity scenarios. We perform a detailed simulation study to compare our approach with other methods and illustrate it on a well‐known microarray gene expression data set for predicting survival from gene expressions.     

MODELLING EPIDEMIC DISEASES

This paper traces the development of mathematical models for epidemics from the 18th century to the present day. The models are shown to be of use in predicting and controlling the spread of infection.     

国债回购率的定价模型研究

一、引言国债回购是金融衍生市场中一种最常用、最普通的交易品种 ,国债回购交易的不断发展是推动整个国债市场顺利运行的重要手段。同时也是整个资本市场的重要组成部分。国债回购是远期合约交易的方式之一。所谓国债回购交易是指某一投资者在卖出债券的同时 ,约定于某一特定日期再以预先约定的价格将债券购回的合约交易。如果投资者Ａ从投资者Ｂ那里购得一个回购 ,则称投资者Ｂ成交一个“反回购”。在西方国家里 ,利用国债回购市场进行“回购”和“反回购”交易最多的是国债一级自营商、大的商业银行、投资银行、基金管理机构和中央银行 ,…     

Volatility forecasting of financial time series using wavelet based exponential generalized autoregressive conditional heteroscedasticity model

Abstract

An improved forecasting model by merging two different computational models in predicting future volatility was proposed. The model integrates wavelet and EGARCH model where the pre-processing activity based on wavelet transform is performed with de-noising technique to eliminate noise in observed signal. The denoised signal is then feed into EGARCH model to forecast the volatility. The predictive capability of the proposed model is compared with the existing EGARCH model. The results show that the hybrid model has increased the accuracy of forecasting future volatility.     

A comparison of scientific and engineering criteria for Bayesian model selection

Given a set of possible models for variables X and a set of possible parameters for each model, the Bayesian estimate of the probability distribution for X given observed data is obtained by averaging over the possible models and their parameters. An often-used approximation for this estimate is obtained by selecting a single model and averaging over its parameters. The approximation is useful because it is computationally efficient, and because it provides a model that facilitates understanding of the domain. A common criterion for model selection is the posterior probability of the model. Another criterion for model selection, proposed by San Martini and Spezzafari (1984), is the predictive performance of a model for the next observation to be seen. From the standpoint of domain understanding, both criteria are useful, because one identifies the model that is most likely, whereas the other identifies the model that is the best predictor of the next observation. To highlight the difference, we refer to the posterior-probability and alternative criteria as the scientific criterion (SC) and engineering criterion (EC), respectively. When we are interested in predicting the next observation, the model-averaged estimate is at least as good as that produced by EC, which itself is at least as good as the estimate produced by SC. We show experimentally that, for Bayesian-network models containing discrete variables only, the predictive performance of the model average can be significantly better than those of single models selected by either criterion, and that differences between models selected by the two criterion can be substantial.     

Bayesian comparison of private and common values in structural second-price auctions

Private and common values (CVs) are the two main competing valuation models in auction theory and empirical work. In the framework of second-price auctions, we compare the empirical performance of the independent private value (IPV) model to the CV model on a number of different dimensions, both on real data from eBay coin auctions and on simulated data. Both models fit the eBay data well with a slight edge for the CV model. However, the differences between the fit of the models seem to depend to some extent on the complexity of the models. According to log predictive score the IPV model predicts auction prices slightly better in most auctions, while the more robust CV model is much better at predicting auction prices in more unusual auctions. In terms of posterior odds, the CV model is clearly more supported by the eBay data.     

Longitudinal data analysis of animal growth via multivariate dynamic models

We propose models to analyze animal growth data with the aim of estimating and predicting quantities of biological and economical interest such as the maturing rate and asymptotic weight. It is also studied the effect of environmental factors of relevant influence in the growth process. The models considered in this paper are based on an extension and specialization of the dynamic hierarchical model (Gamerman & Migon, 1993) to a non–linear growth curve setting, where some of the growth curve parameters are considered exchangeable among the units. The inference for these models are approximate conjugate analysis based on Taylor series expansions and linear Bayes procedures     

Models Comparison for Step-Stress Accelerated Life Testing

In this article, four basic models for step-stress accelerated life testing are introduced and compared: cumulative exposure model (CEM), linear cumulative exposure model (LCEM), tampered random variable model (TRVM), and tampered failure rate model (TFRM). Limitations of the four models are also introduced for better use of the models.