Predictability and model selection in the context of ARCH models

Most of the methods used in the ARCH literature for selecting the appropriate model are based on evaluating the ability of the models to describe the data. An alternative model selection approach is examined based on the evaluation of the predictability of the models in terms of standardized prediction errors. Copyright © 2005 John Wiley & Sons, Ltd.     

Detecting and Diagnostic Checking Multivariate Conditional Heteroscedastic Time Series Models

Two tests for multivariate conditional heteroscedastic models are proposed. One is based on the cross-correlations of standardized squared residuals and the other is a score (Lagrange multiplier) test. The cross-correlations test can be used to detect the presence of multivariate conditional heteroscedasticity whereas the other test can be used for diagnostic checking. Simulation studies on the size and power of the test statistics are reported. The application of the tests is illustrated by an example using the S & P 500 and Sydney All Ordinary Indexes.     

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     

ARCH(0,1)系数中位无偏估计分布的渐近展开(英文)

This paper is concerned with the distributional properties of a median unbiased estimator of ARCH(0,1)coefficient.The exact distribution of the estimator can be easily derived,however its practical calculations are too heavy to implement, even though the middle range of sample sizes.Since the estimator is shown to have asymptotic normality,asymptotic expansions for the distribution and the percentiles of the estimator are derived as the refinements.Accuracies of expansion formulas are evaluated numerically,and the results of which show that we can effectively use the expansion as a fine approximation of the distribution with rapid calculations.Derived expansion are applied to testing hypothesis of stationarity,and an implementation for a real data set is illustrated.     

ARCH模型的多变点检验

研究自回归条件异方差(ARCH)模型的多变点检验问题.提出一种拟似然比检验统计量,并在原假设下给出统计量的极限分布.在假设检验过程中得到变点个数的一致估计.数值模拟与实例分析说明了方法的合理性.     

高频金融时间序列的模型化研究进展回顾

从高频和超高频金融数据的基本统计特征出发,回顾了(超)高频金融时间序列模型化研究的发展历程及相关特征,并详细介绍了高频数据模型研究中针对久期序列建立ACD模型族的研究与进展.对ACD模型族,介绍了两种主要类型:强ACD模型和弱ACD模型.最后展望了高频金融时间序列中ACD模型的研究.     

Using Random Quasi-Monte-Carlo Within Particle Filters,With Application to Financial Time Series

This article presents a new particle filter algorithm which uses random quasi-Monte-Carlo to propagate particles. The filter can be used generally, but here it is shown that for one-dimensional state-space models, if the number of particles is N, then the rate of convergence of this algorithm is N^?1. This compares favorably with the N^?1/2 convergence rate of standard particle filters. The computational complexity of the new filter is quadratic in the number of particles, as opposed to the linear computational complexity of standard methods. I demonstrate the new filter on two important financial time series models, an ARCH model and a stochastic volatility model. Simulation studies show that for fixed CPU time, the new filter can be orders of magnitude more accurate than existing particle filters. The new filter is particularly efficient at estimating smooth functions of the states, where empirical rates of convergence are N^?3/2; and for performing smoothing, where both the new and existing filters have the same computational complexity.     

Bootstrapping the nonparametric ARCH regression model

In this paper we introduce the nonparametric AR(1)–ARCH(1) model and show weak consistency of the Nadaraya–Watson estimators for the model. We propose a residual and a wild bootstrap method and prove weak consistency of the bootstrap estimators.     

不确定性因素对我国农村居民现金消费支出的影响分析

本文根据1978—2003年中国农村居民现金消费支出数据,综合考虑影响消费的主要因素来研究中国农村居民的现金消费行为。与以往研究不同的是,对于影响消费支出的不确定性因素,本文不是采用某个具体变量进行近似替代,而是采用刻画不确定性的条件异方差来描述。文章的结论是农村居民现金消费支出的主要影响因素并非收入,而是利率和不确定性因素,不确定性因素在1996年后表现得尤为突出。     

On the Stationary Version of the Generalized Hyperbolic ARCH Model

This paper finds conditions under which the generalized hyperbolic ARCH-type model is strictly stationary. Properties of the model are investigated and in particular an estimation procedure is proposed. The resulting stationary model provides with a robust non-Gaussian ARCH-type alternative.