Testing for parameter constancy in GARCH() models

We propose a test for a change in the parameters of a GARCH(p,q) model. The test is based on approximate likelihood scores and does not require the observations to have finite variance. We show that the test has asymptotically correct size under weak assumptions on model errors.     

An application of vector GARCH model in semiconductor demand planning

Demand planning has been the key to supply chain management in semiconductor industry. With an appropriate weight assignment scheme, the planning accuracy resulting from combinational forecasts can be improved by merging several individual candidate methods. In this paper we discuss the applicability of vector generalized autoregressive conditional heteroskedasticity (GARCH) model to determine the optimal combinational weights of component forecasts, where the conditional variances and correlations of forecast errors from candidate methods are represented and estimated by a maximum-likelihood procedure. The asymptotical properties of parameter estimators for GARCH model are investigated by simulation experiments. An example of the proposed method to real time series of electronic products demonstrates that this weight-varying combinational method produces less prediction errors, compared to other commonly used forecasting approaches that are based on single model selection criteria or fixed weights.     

On asymptotic theory for multivariate GARCH models

The paper investigates the asymptotic theory for a multivariate GARCH model in its general vector specification proposed by Bollerslev, Engle and Wooldridge (1988) [4], known as the VEC model. This model includes as important special cases the so-called BEKK model and many versions of factor GARCH models, which are often used in practice. We provide sufficient conditions for strict stationarity and geometric ergodicity. The strong consistency of the quasi-maximum likelihood estimator (QMLE) is proved under mild regularity conditions which allow the process to be integrated. In order to obtain asymptotic normality, the existence of sixth-order moments of the process is assumed.     

On dynamics of volatilities in nonstationary GARCH models

This paper precisely characterizes asymptotic behaviors of the volatilities in nonstationary GARCH(1, 1) models. After suitable renormalization, it is shown that the volatility converges in distribution to a non-degenerate limit. This provides more insight into the dynamics of volatilities in nonstationary GARCH models.     

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.     

A conditional-SGT-VaR approach with alternative GARCH models

This paper proposes a conditional technique for the estimation of VaR and expected shortfall measures based on the skewed generalized t (SGT) distribution. The estimation of the conditional mean and conditional variance of returns is based on ten popular variations of the GARCH model. The results indicate that the TS-GARCH and EGARCH models have the best overall performance. The remaining GARCH specifications, except in a few cases, produce acceptable results. An unconditional SGT-VaR performs well on an in-sample evaluation and fails the tests on an out-of-sample evaluation. The latter indicates the need to incorporate time-varying mean and volatility estimates in the computation of VaR and expected shortfall measures.     

多维GARCH模型的半参数有效估计

多元GARCH模型的估计一般采用拟极大似然法(quasi maximum likehood),对于这种方法估计的相合性及渐近正态性已经被很多学者证实,然而对于新息列的分布不是多元正态时,这种估计的有效性还没人研究,本文从拟极大似然估计得到的参数相合估计入手,提出用非参数方法估计多元新息列的分布.     

GARCH模型参数变化的残量检验

本文研究GARCH模型参数变化的检验问题. 给出残量累积和统计量, 在原假设下得到了统计量的极限分布; 模拟结果表明残量检验可以弥补Kim, Cho和Lee (2000)\ucite{1}提出的平方累积和检验的某些不足, 比如经验势函数值过低的问题.     

基于GARCH模型的石油价格变动模拟

石油是一种特殊的商品,是国家重要的战略物资,世界各国都十分重视其价格变动问题,因为油价变化会影响到各国经济发展,甚至国家安全。因此,本文采用GARCH模型,通过基于Gibbs抽样的MCMC方法分析了国际市场石油价格的分布特征,对石油价格波动的异方差特性进行描述和模拟,实证分析结果说明从石油价格波动序列峰度系数和平方价格波动序列自相关函数的描述来看,基于t分布的模型模拟效果优于基于正态分布的模型,这一结论反映了石油价格波动序列的分布特性。     

Investigating asymptotic properties of vector nonlinear time series models

Analyses and simulations of vector nonlinear time series typically run into weeks or even months because the methods used are computationally intensive. Statisticians have been known to base empirical results on a relatively small number of simulation replications, sacrificing precision and reliability of results in the interest of time and productivity. The simulations are amenable for parallelization. However, parallel computing technology has not yet been widely used in this specific research area. This paper proposes an approach to the parallelization of statistical simulation codes to address the challenge of long running times. Requiring minimal code revision, this approach takes advantage of recent advances in dynamic loop scheduling to achieve high performance on general-purpose clusters, even with the presence of unpredictable load imbalance factors. Preliminary results of applying this approach in the simulation of normal white noise and threshold autoregressive model obtains efficiencies in the range 95%-98% on 8-64 processors. Furthermore, previously unobserved properties of the statistical procedures for the models are uncovered by the simulation.     

基于GARCH模型族的中国股市波动性预测

收益与风险历来都是投资者与研究者所关注的问题 .本文选取 GA RCH、TGARCH和 EGARCH模型来拟合中国股市的波动性 .实证分析结果表明 ,中国股市的波动具有显著的波动聚类性与持续性 ;由 E-GARCH模型所预测的上证 30指数、上证综合指数和深证成份指数未来一天的波动要明显优于 GARCH和TGARCH模型的对应值 ,而对香港恒生指数 ,三种模型的预测结果无显著的差异 .     

Inference for 2-D GARCH models

The purpose of this paper is, in the first step, to consider a class of GMM estimators with interesting asymptotic properties and a reasonable number of computations for two dimensionally indexed Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. In the second step, we use the central limit theorem of Huang (1992) for spatial martingale differences to establish the LAN property for general two-dimensional discrete models on a regular grid with Gaussian errors. We then apply this result to the spatial GARCH model and derive the limit distribution of the maximum likelihood estimators of the parameters. Results of numerical simulations are presented.     

一类混合的非对称的GARCH模型的性质研究

本文提出了一类混合的非对称的GARCH模型(MAGARCH),利用随机差分方程的一些结果,研究了该模型的平稳性条件和尾行为,还讨论了MAGARCH(K;1,1)的矩的情况.     

基于GARCH模型考虑股本稀释作用的权证定价

考虑到认购权证对股本有稀释作用,把对认购权证定价转化为一个看涨期权的定价,运用GARCH模型得出看涨期权标的资产波动率的近似经验分布,根据期权定价的Black-Scholes公式,得出认购权证价格的近似分布.     

GARCH型过程相关控制图

将 GARCH模型引入到自相关质量过程控制图中 ,提出了 GARCH型控制图 ,为方差随时间变化的过程提供了处理工具 ,可以解决实际工作中因设备、原材料或操作因素使质量波动水平发生变化的控制问题     

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.     

GARCH模型变点的Ratio检验

研究GARCH模型参数变点的Ratio检验.首先构造了基于残量累积平方和的Ratio统计量,推导了原假设下统计量的极限分布,其次采用Monte Carlo方法检验其有效性,最后以数据为例进一步说明该方法的实用性.     

GARCH模型在股票市场风险计量中的应用

本文以上证综指的日收益率为研究对象,运用GARCH模型簇分析上海股市日收益率波动的条件异方差性,计算每天的V aR值.实证研究表明,GARCH模型的V aR计算方法对我国股市风险的管理有较好的效果.     

Estimation for nearly unit root processes with GARCH errors

In this paper the limiting distribution of the least square estimate for the autoregressive coefficient of a nearly unit root model with GARCH errors is derived. Since the limiting distribution depends on the unknown variance of the errors, an empirical likelihood ratio statistic is proposed from which confidence intervals can be constructed for the nearly unit root model without knowing the variance. To gain an intuitive sense for the empirical likelihood ratio, a small simulation for the asymptotic distribution is given.     

A new nonlinear formulation for GARCH models

In this note we deduce a new mathematical representation, based on a discrete-time nonlinear state–space formulation, to characterize Generalized AutoRegresive Conditional Heteroskedasticity (GARCH) models. The purpose pursued by this article is to use the models presented herein to develop estimation techniques which are also valid in the situation when observations are missing.