On the estimation of non linear functions in stochastic volatility models

Abstract

This paper focuses on the inference of suitable generally non linear functions in stochastic volatility models. In this context, in order to estimate the variance of the proposed estimators, a moving block bootstrap (MBB) approach is suggested and discussed. Under mild assumptions, we show that the MBB procedure is weakly consistent. Moreover, a methodology to choose the optimal length block in the MBB is proposed. Some examples and simulations on the model are also made to show the performance of the proposed procedure.     

Variance (Non) Causality in Multivariate GARCH

This paper extends the current literature on the variance-causality topic providing the coefficient restrictions ensuring variance noncausality within multivariate GARCH models with in-mean effects. Furthermore, this paper presents a new multivariate model, the exponential causality GARCH. By the introduction of a multiplicative causality impact function, the variance causality effects becomes directly interpretable and can therefore be used to detect both the existence of causality and its direction; notably, the proposed model allows for increasing and decreasing variance effects. An empirical application evidences negative causality effects between returns and volume of an Italian stock market index future contract.     

深圳证券交易所不同指数收益率的波动比较

股票价格指数的波动的大小通常代表了它的风险程度,研究股指的波动对风险管理、投资组合以及价格预测有着十分重要的意义。文章采用时间序列分析方法,对深圳证券交易所的三种价格指数建立GARCH模型,并对这三种指数的波动率进行比较。     

基于FCM的动态结合全局图像阈值分割

全局阈值分割对于小目标物效果不理想,动态阈值容易产生阴影等干扰,但综合考虑全局阈值和动态阈值可以达到比较理想的结果。模糊C均值算法用于灰度图像分割是一种非监督模糊聚类后再标定的过程,该文在不明显增加运算量的前提下,利用模糊C均值自动聚类的功能分别得到全局阈值和动态阈值,完成对阈值矩阵的构造和图像的分割。     

动静态属性数据相结合的客户分类方法研究

研究了一种客户动态、静态属性数据相结合的客户分类方法。提出了客户时间序列的加权处理方法,并应用客户时间序列的统计特征作为聚类特征向量,采用混合式遗传算法对客户聚类,使每一类客户具有相似的时序特征。在此基础上将聚类结果与客户的静态属性数据相结合,对客户进一步分类。实验结果表明,与传统的基于静态属性数据的客户分类方法相比,本文的方法提高了客户分类的准确性。     

Fuzzy K-means clustering models for triangular fuzzy time trajectories

We focus our attention on the classification of fuzzy time trajectories with triangular membership function, described by a given set of individuals. To this purpose, we adopt a fullyinformational approach, explicitly recognizing the informational nature shared by the ingredients of the classification procedure: the observed data (Empirical Information) and the classification model (Theoretical Information). In particular, by supposing that the informational paradigm has a fuzzy nature, we suggest three fuzzy clustering models allowing the classification of the triangular fuzzy time trajectories, based on the analysis of the cross sectional and/or longitudinal characteristics of their components (centers and spreads). Two applicative examples are illustrated.     

基于Web使用挖掘的个性化服务系统

个性化服务系统是一种由多种WEB挖掘技术构成的基于用户使用的站点个性化系统。该系统使用事务聚类、使用聚类和关联规则技术等数据挖掘技术分析用户访问模式,并结合用户当前访问情况提供实时化个性服务。实验结果说明,个性化服务系统具有较好的性能。     

甘肃省县域经济发展水平空间差异分析

甘肃省包括86个县区,受地理位置、气候条件、资源状况、经济基础各种因素的影响,各县区之间的经济发展水平差异显著但又有很多相似之处。为了定量揭示甘肃省县域经济发展水平在空间上的分布现状、差异以及相似性,笔者拟运用多元统计方法对甘肃省县域经济发展水平的空间差异分析     

波动率的持续性：基于GARCH模型的成交量解释

文章将成交量变量合理地分解为由好、坏消息分别引致的两部分,结合GARCH模型,研究了成交量对波动率持续性的解释,并得出如下结论:成交量变量对波动率的持续性具有一定的解释力;信息对波动率的影响具有不对称性,坏消息比好消息的影响大;将成交量分解成由好、坏消息分别引致的两部分之后,能够更进一步地解释波动率的持续性。     

基于复杂网络的投资组合优化研究

本文基于复杂网络的局部聚类系数改进了传统的全局最小方差投资组合模型。首先通过股票对数收益率的相关系数矩阵构造股票关联网络,然后计算股票关联网络的局部聚类系数,最后通过全局最小方差模型确定最佳投资组合。将改进后的模型应用于A股市场,经过夏普比率、信息比率和欧米茄比率的对比分析得出改进后的投资组合模型在样本外的表现优于传统的全局最小方差投资组合模型。