Non-Parametric Regression Methods

The nature of the financial time series is complex, continuous interchange of stochastic and deterministic regimes. Therefore, it is difficult to forecast with parametric techniques. Instead of parametric models, we propose three techniques and compare with each other. Neural networks and support vector regression (SVR) are two universally approximators. They are data-driven non parametric models. ARCH/GARCH models are also investigated. Our assumption is that the future value of Istanbul Stock Exchange 100 index daily return depends on the financial indicators although there is no known parametric model to explain this relationship. This relationship comes from the technical analysis. Comparison shows that the multi layer perceptron networks overperform the SVR and time series model (GARCH).     

Asymmetric responses of international stock markets to trading volume

The major goal of this paper is to examine the hypothesis that stock returns and return volatility are asymmetric, threshold nonlinear, functions of change in trading volume. A minor goal is to examine whether return spillover effects also display such asymmetry. Employing a double-threshold GARCH model with trading volume as a threshold variable, we find strong evidence supporting this hypothesis in five international market return series. Asymmetric causality tests lend further support to our trading volume threshold model and conclusions. Specifically, an increase in volume is positively associated, while decreasing volume is negatively associated, with the major price index in four of the five markets. The volatility of each series also displays an asymmetric reaction, four of the markets display higher volatility following increases in trading volume. Using posterior odds ratio, the proposed threshold model is strongly favored in three of the five markets, compared to a US news double threshold GARCH model and a symmetric GARCH model. We also find significant nonlinear asymmetric return spillover effects from the US market.     

中国股市与汇市的波动溢出效应模型研究与实证分析

选取2009年1月5日-2012年5月22的人民币兑美元汇率和上证综指的日交易数据为对象,采用BEKK—GARCH模型,并结合LR似然比检验,对中国汇市与股市的波动溢出效应进行建模研究与实证分析．实证结果表明,整体样本存在汇率到上证指数的波动溢出,这种溢出是单向的;第二次汇改前,既不存在汇率到上证指数的波动溢出,也不存在上证指数到汇率的波动溢出;第二次汇改后,既存在汇率到上证指数的波动溢出,也存在上证指数到汇率的微弱的波动溢出．     

基于三类模型的四大银行股票收益率预测研究

运用时间序列分析的预测方法,对四大银行的股票日对数收益率序列进行拟合与预测分析,分别构建ARMA模型、GARCH模型以及ARMA-GARCH组合模型,通过模型比较,实证分析表明:在拟合效果上,ARMA-GARCH模型的拟合优度优于ARMA模型和GARCH模型;在预测效果上,ARMA模型的预测效果最优,ARMA-GARCH模型次之.     

中国股票市场波动特性的实证研究

本文以上证综指和深成分指数的日收益率为研究对象 ,应用 GARCH、TARCH模型理论 ,进一步分析了日收益率波动的条件异方差性、非对称性 ,同时比较了两个股票市场的不同波动特征     

基于混合Copula模型的投资组合风险分析

金融市场或股票之间的相关关系变化灵活多样,针对现实中往往需要考虑的是多个市场、股票的结构,采用Mixture-Copula模型来分析多元市场的相关性结构,进而构建了Multivariate-GARCH-Mixture-Copula,模型,并选取2002年1月1日至2011年12月31日上证工业指数、商业指数、地产指数三个行业指数序列的2425组数据利用该模型进行实证分析.分析表明,Multivariate-GARCH-Mixture-Copula模型能有效地应用于实际金融市场潜在结构的分析,对投资组合的风险研究有一定的参考意义.     

基于参数模型的EVaR风险度量计算方法及实证研究

针对EVaR(Expectile-based Value at Risk)风险度量提出了基于GARCH类和SV波动率模型的EVaR风险度量计算方法,即EVaR计算的参数模型方法.并基于模拟学生t分布时间序列数据,给出EVaR样本外预测的失败率检验方法:Kupiec失败率检验和动态分位数(DQ)检验法.与采用CARE(Conditional Autoregressive Expectile)模型的EVaR计算方法进行了对比研究,结果表明基于GARCH类模型和SV模型相对于基于CARE模型有更优的EVaR预测效果.选取2004年1月5日到2009年12月30日的国内外五个股票市场指数数据,针对日对数收益率进行了EVaR风险度量的实证研究,得出在金融危机期间,基于参数模型的EVaR预测要比基于CARE模型的EVaR预测更接近市场实际风险.     

基于GARCH模型的短期汇率预测

研究人民币对美元的汇率预测,通过对2010年7月1日至2013年11月30的周汇率平均值进行数据分析,发现其基本符合时间序列分析中的GARCH模型,因此采用该模型进行预测,预测结果比较成功。预测表明人民币呈现升值的趋势.     

不同市态下正态性转换时变VaR

VaR技术作为全球广为流行的金融风险管理技术,其测度的是极端情况下的风险头寸,但在传统假设下可能会极大地低估其值,这就会使得在实践中使用VaR值作为风险管理标准时面临更大的新的风险.考虑我国股市处于不同市场态势下对风险头寸的影响,就牛、熊市中分别估测VaR值.首先利用各种Delta-Gamma-Johnson转换函数对经验数据进行正态性调整.考虑通过转换机制调整后的经验数据仍然存在的异方差性特征,然后运用GARCH模型计算时变VaR值,以此来改善VaR的计算风险,探讨我国股票市场VaR技术的适用性和准确性.     

On spatial contagion and multivariate GARCH models

We propose a method for defining and measuring spatial contagion between two financial markets via conditional copulas. Some theoretical results on monotonicity and asymptotic properties of Gaussian copulas with respect to conditioning are presented. Next, we combine the spatial contagion approach with time series models. We investigate which model from a large family of multivariate GARCH is the best tool for modelling spatial contagion. In an empirical study, we show that among models designed for general fit, a two‐step model fitting procedure reduces the ability to describe the contagion effect. This is a feature of copula‐GARCH models. Copyright © 2013 John Wiley & Sons, Ltd.