激进型投连险收益率波动特征及投资风格研判

文章以特定激进型投连险账户为研究样本,基于2004~2014年间的日交易数据,运用GARCH族模型对其收益率波动特征进行检验,并对其投资风格进行研判。研究发现,GED分布设定下的ARMA(1,1)-GARCH(1,1)模型拟合效果最佳;该收益率具有波动聚集、高风险高收益以及长记忆等特征,与我国股票市场具有杠杆效应特征不同,杠杆效应特征并不显著;此外,通过建立DCC-GARCH模型对其投资风格进行研判,发现该收益率与上证综合指数收益率的动态条件相关系数为正且较高,这印证了其激进型的投资风格。     

上证指数收益率的特征及其波动性分析

股票价格的频繁波动是股票市场最明显的特征之一。ARCH类模型可以很好地预测金融资产收益率的方差。通过对上证指数的统计分析表明,上证指数的收益率分布表现出非正态性,并存在自回归条件异方差的特征。利用ARCH类模型对上证指数的波动进行了拟合,结果表明GARCH(1,1)模型对上证指数波动具有较好的拟合效果。     

基于GARCH模型的沪深地产股波动性分析及预测

近年来GARCH类模型在预测波动率方面得到了广泛应用,鉴于股票和房地产两个市场对我国经济发展的重要性,所以选择沪深两市地产指数的收益率做波动性研究.丈章运用GARCH类模型对沪深地产指数收益率的波动进行了枯计和预测,结果表明沪深地产指数收益率的波动不存在杠杆效应,投资者投机目的较强,M-Z回归和损失函数评价结果显示,GARCH(1,1)-M模型的样本外预测刚效果是最好的,但不能准确预测非常大的波动.     

具有GARCH（1，1）-正态误差项的非对称单位根检验研究

摘　　要:理论研究表明许多经济变量呈现出非对称的门限自回归（TAR）或动态门限自回归（M-TAR）数据生成机制,因而非对称单位根检验就成为该领域的主要研究方向之一。本文对非对称单位根检验Enders-Granger方法在GARCH（1,1）-正态误差项下的检验水平与检验势作了系统的仿真研究。研究表明：GARCH（1,1）-正态误差项的TAR或M-TAR模型会对该方法的检验水平和检验势产生重要影响。     

波动率模型在中国股市中的应用研究

文章对上证综合指数收益率和深证成分指数收益率进行统计分析,运用GARCH,EGARCH,TARCH模型对其进行建模,发现股票收益率序列所存在的尖峰厚尾现象、波动聚类特性以及杠杆效应,通过比较不同的模型发现非对称模型的拟合效果最为理想;另外通过采用三种不同的损失函数评价各类模型的预测效果,结果表明,非对称模型样本外预测的能力也是最强的.     

基于ARCH类模型的基金市场波动性研究

近年来,随着基金产品的广泛推出,基金市场迅猛发展,投资基金的市场收益率也呈现出一定的波动性.本文选取上证基金指数为研究对象,运用ARCH模型族进行实证分析.结果表明,上证基金指数收益率表现出非正态性和条件异方差的特征.GARCH(1,1)模型对上证基金指数的波动具有很好的拟合效果.     

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。     

运用GARCH模型对CRB能源价格指数序列的拟合与预测

基于SAS软件系统,运用GARCH模型分析方法模拟美国CRB能源价格指数对数序列波动情况。实证结果表明:CRB能源价格指数对数序列存在自相关性和异方差性;用GARCH(1,1)模型能较好的模拟CRB能源价格指数波动特征。     

我国新商品期货品种的波动特征研究

文章运用GARCH模型和GARCH—M模型研究我国2007—2008年间新推出的商品期货（锌、菜籽油、线型低密度聚乙烯和黄金）的波动聚集性、持续性、非对称性以及风险溢价等特征。实证结果表明：四种新商品期货价格波动均表现出较强的聚集性和持续性特征。其中,聚乙烯期货的波动呈现出正向冲击比负向冲击对价格影响曼大的非对称波动特征,菜籽油期货的风险与收益之间呈现出显著的负相关关系,而其它期货品种则没有呈现上述明显定量特征。     

上海证券市场GARCH效应检验和模型选择

文章利用1999年10月8日至2003年11月7日上证综合指数每日的收盘价数据对其进行了GARCH效应的检验,结果表明上海证券市场股价的波动存在着显著的GARCH效应,并且存在非对称的情况。在具体的模型选择上以EGARCH(1,1)较好,并且在研究中发现GARCH-M模型不适合模拟我国上海证券市场股价的波动情况。     

Impact study of volatility modelling of Bangladesh stock index using non-normal density

This article examines a wide variety of popular volatility models for stock index return, including the random walk (RW), autoregressive, generalized autoregressive conditional heteroscedasticity (GARCH), and asymmetric GARCH models with normal and non-normal (Student's t and generalized error) distributional assumption. Fitting these models to the Chittagong stock index return data from the period 2 January 1999 to 29 December 2005, we found that the asymmetric GARCH/GARCH model fits better under the assumption of non-normal distribution than under normal distribution. Non-parametric specification tests show that the RW-GARCH, RW-TGARCH, RW-EGARCH, and RW-APARCH models under the Student's t-distributional assumption are significant at the 5% level. Finally, the study suggests that these four models are suitable for the Chittagong Stock Exchange of Bangladesh. We believe that this study would be of great benefit to investors and policy makers at home and abroad.     

Parameter changes in GARCH model

A new method for detecting the parameter changes in generalized autoregressive heteroskedasticity GARCH (1,1) model is proposed. In the proposed method, time series observations are divided into several segments and a GARCH (1,1) model is fitted to each segment. The goodness-of-fit of the global model composed of these local GARCH (1,1) models is evaluated using the corresponding information criterion (IC). The division that minimizes IC defines the best model. Furthermore, since the simultaneous estimation of all possible models requires huge computational time, a new time-saving algorithm is proposed. Simulation results and empirical results both indicate that the proposed method is useful in analysing financial data.     

A robust closed-form estimator for the GARCH(1,1) model

In this paper we extend the closed-form estimator for the generalized autoregressive conditional heteroscedastic (GARCH(1,1)) proposed by Kristensen and Linton [A closed-form estimator for the GARCH(1,1) model. Econom Theory. 2006;22:323–337] to deal with additive outliers. It has the advantage that is per se more robust that the maximum likelihood estimator (ML) often used to estimate this model, it is easy to implement and does not require the use of any numerical optimization procedure. The robustification of the closed-form estimator is done by replacing the sample autocorrelations by a robust estimator of these correlations and by estimating the volatility using robust filters. The performance of our proposal in estimating the parameters and the volatility of the GARCH(1,1) model is compared with the proposals existing in the literature via intensive Monte Carlo experiments and the results of these experiments show that our proposal outperforms the ML and quasi-maximum likelihood estimators-based procedures. Finally, we fit the robust closed-form estimator and the benchmarks to one series of financial returns and analyse their performances in estimating and forecasting the volatility and the value-at-risk.     

基于EGARCH-M模型的沪深300股指期货跨期套利研究——一种修正的协整关系

现有的基于协整股指期货跨期套利策略主要利用GARCH模型进行建模。该模型忽略了杠杆效应的存在,也未考虑条件方差可能影响协整方程。在引入EGARCH-M模型进行套利研究的基础上,提出一种新的协整关系——修正的协整;利用沪深300股指期货合约的每分钟收盘价进行实证分析,结果表明:正态EGARCH-M模型对数据的拟合效果优于传统的GARCH模型,通过设定合理的交易机制可以获得良好的套利结果;非正态的EGARCH-M模型在拟合效果和捕捉套利机会方面都比正态模型具有更好的表现,且套利效果也有显著提高。     

Forecasting interest rates: a comparative assessment of some second-generation nonlinear models

Modeling and forecasting of interest rates has traditionally proceeded in the framework of linear stationary methods such as ARMA and VAR, but only with moderate success. We examine here three methods, which account for several specific features of the real world asset prices such as nonstationarity and nonlinearity. Our three candidate methods are based, respectively, on a combined wavelet artificial neural network (WANN) analysis, a mixed spectrum (MS) analysis and nonlinear ARMA models with Fourier coefficients (FNLARMA). These models are applied to weekly data on interest rates in India and their forecasting performance is evaluated vis-à-vis three GARCH models [GARCH (1,1), GARCH-M (1,1) and EGARCH (1,1)] as well as the random walk model. Both the WANN and MS methods show marked improvement over other benchmark models, and may thus hold out several potentials for real world modeling and forecasting of financial data.     

Conditional correlation in asset return and GARCH intensity model

In an asset return series, there is a conditional asymmetric dependence between current return and past volatility depending on the current return’s sign. To take into account the conditional asymmetry, we introduce new models for asset return dynamics in which frequencies of the up and down movements of asset price have conditionally independent Poisson distributions with stochastic intensities. The intensities are assumed to be stochastic recurrence equations of the GARCH type to capture the volatility clustering and the leverage effect. We provide an important linkage between our model and existing GARCH, explain how to apply maximum likelihood estimation to determine the parameters in the intensity model and show empirical results with the S&P 500 index return series.     

Finite nonparametric grach model for foreign exchange volatility

GARCH model has been commonly used to describe the volatility of foreign exchange returns, which typically depends on returns many lags before, While the GARCH model provides a simple geometric decaying structure for persistence in time, it restricts tiie impact of variables to Quadratic functions. A finite nonparametric GARCH model is proposed that allows the variables' impact to be a smooth function of any form. A direct local polynomial estimation method for this finite GARCH model is proposed based on results on proportional additive model, and is applied to the German Mark (DEM)/US Dollar (USD) daily returns data. Estimators uf both the decaying rate and the impact function are obtained. Diagnostics show satisfactory out-of-sampie prediction based on the proposed model, which helps to better understand the dynamics of foreign exchange volatility.