Garch(1,1) process can have arbitrarily heavy power tails

We study the structure of solutions of Kesten’s equation (1.5), where a, b ⩾ 0 are the coefficients of the GARCH(1,1) process in (1.1). We prove that, for any b ∈ (0, 1) and any κ > 0 small enough, there exists a stationary GARCH(1,1) process with tail index κ. The research was partially supported by the bilateral France-Lithuania scientific project Gilibert and the Lithuanian State Science and Studies Foundation, grant no. T-15/07. Published in Lietuvos Matematikos Rinkinys, Vol. 47, No. 2, pp. 196–210, April–June, 2007.     

Random Coefficient GARCH(1,1) Model With i.i.d. Coefficients

We introduce the GARCH(1,1) model with random i.i.d. coefficients. Conditions for the existence of a stationary solution of a random coefficient GARCH(1,1) equation are obtained. They generalize the well-known results of Nelson [14] and Terasvirta [18] in the case of constant (nonrandom) coefficients.__________Published in Lietuvos Matematikos Rinkinys, Vol. 44, No. 4, pp. 467–480, October–December, 2004.     

Whittle estimation in a heavy-tailed GARCH(1,1) model

The squares of a GARCH(p,q) process satisfy an ARMA equation with white noise innovations and parameters which are derived from the GARCH model. Moreover, the noise sequence of this ARMA process constitutes a strongly mixing stationary process with geometric rate. These properties suggest to apply classical estimation theory for stationary ARMA processes. We focus on the Whittle estimator for the parameters of the resulting ARMA model. Giraitis and Robinson (2000) show in this context that the Whittle estimator is strongly consistent and asymptotically normal provided the process has finite 8th moment marginal distribution.

We focus on the GARCH(1,1) case when the 8th moment is infinite. This case corresponds to various real-life log-return series of financial data. We show that the Whittle estimator is consistent as long as the 4th moment is finite and inconsistent when the 4th moment is infinite. Moreover, in the finite 4th moment case rates of convergence of the Whittle estimator to the true parameter are the slower, the fatter the tail of the distribution.

These findings are in contrast to ARMA processes with iid innovations. Indeed, in the latter case it was shown by Mikosch et al. (1995) that the rate of convergence of the Whittle estimator to the true parameter is the faster, the fatter the tails of the innovations distribution. Thus the analogy between a squared GARCH process and an ARMA process is misleading insofar that one of the classical estimation techniques, Whittle estimation, does not yield the expected analogy of the asymptotic behavior of the estimators.     

GARCH(1,1)模型及其在汇率条件波动预测中的应用

检验人民币/日元汇率与波动的时间序列特征,证实存在简单单位根过程及条件异方差性。计算表明,其汇率变化率的ARMA及ARMA/GARCH组合模型的建模不成立,GARCH、EGARCH、IGARCH模型的建模效果接近,且GARCH(1,1)拟合效果最好。GARCH(1,1)模型的跨度为一年的样本外条件异方差预测,显示出该年末汇率的震荡,与实际情况一致。GARCH(1,1)是汇率数据建娱的首选模型。     

A note on geometric ergodicity of autoregressive conditional heteroscedasticity (ARCH) model

For the pth-order linear ARCH model,

, where ₀ > 0, _i 0, I = 1, 2, …, p, {_t} is an i.i.d. normal white noise with E_t = 0, E_t² = 1, and _t is independent of {X_s, s < t}, Engle (1982) obtained the necessary and sufficient condition for the second-order stationarity, that is, ₁ + ₂ + ··· + _p < 1. In this note, we assume that _t has the probability density function p(t) which is positive and lower-semicontinuous over the real line, but not necessarily Gaussian, then the geometric ergodicity of the ARCH(p) process is proved under E_t² = 1. When _t has only the first-order absolute moment, a sufficient condition for the geometric ergodicity is also given.     

GARCH(1,1)模型的稳健估计比较及应用

首先阐述了GARCH(1,1)模型稳健估计的构造方法,然后在模型有无异常值扩散效应约束和异常值比例不同的情况下,比较了传统QMLE估计和多种稳健M估计的表现,结果表明:在数据无异常值下,QMLE估计较优;随着异常值比例增加,稳健Andrew估计表现更好;模型施加异常值扩散效应约束对估计有一定改善但不显著.最后选取波动程度不同的两个阶段沪深300指数的收益率,用模型拟合进行了实例比较,在波动程度较大时,Andrew估计效果较优,在波动相对平稳时,LAD估计较优.     

改进灰导数的GM(1,1)幂模型

为了提高灰色GM(1,1)幂模型的拟合精度,讨论了灰色GM(1,1)幂模型灰导数的白化问题.以白化微分方程为基础,利用梯形公式白化灰导数,得到了改进的GM(1,1)幂模型.实例分析结果表明改进的GM(1,1)幂模型具有更高的预测和拟合精度.     

GM(1,1)模型适用域讨论及模型的改进

在已有灰色系统理论的基础上,讨论了GM(1,1)模型的适用域,明确界定了GM(1,1)模型的有效区域和禁区,并提出了GM(1,1)模型的一种改进形式——离散灰色预测DGM(1,1)模型.通过对我国经济增长的实证分析说明了该模型的有效性和可靠性.研究结果表明,提出的DGM(1,1)模型可作为灰色预测的一种精确模型,因此,为我国经济增长预测提供了一种新的方法,对当前我国经济的理性增长具有重要的指导意义.     

On the properties of small sample of GM(1,1) model

In grey prediction modeling, the more samples selected the more errors. This paper puts forward new explanations of “incomplete information and small sample” of grey systems and expands the suitable range of grey system theory. Based on the geometric sequence, it probes into the influence on the relative errors by selecting the different sample sizes. The research results indicate that to the non-negative increasing monotonous exponential sequence, the more samples selected, the more average relative errors. To the non-negative decreasing monotonous exponential sequence, a proper sample number exists that has the least average relative error. When the initial value of the sequence of raw data of new information GM(1,1) model changes, the development coefficient remains unchanged. The segmental correction new information GM(1,1) model (SNGM) can obviously improve the simulation accuracy. It puts forward the mathematic proofs that the small sample usually has more accuracy than the large sample when establishing GM(1,1) model in theory.     

基于模糊GM(1,1)模型的时间序列预测

提出了一种模糊GM(1,1)预测模型,即FGM(1,1)模型,该方法是在GM(1,1)模型中引入模糊成员函数,通过模糊成员函数对时间序列数据进行模糊化,达到数据优化选择,实现历史数据"重近轻远"的预测效果.仿真结果表明所提出的预测方法有效可靠,为提高预测精度提供了新的途径.     

以x~((1))(n)为初始条件的DGM(1,1)模型

根据灰色系统理论的新信息优先原理,建立了以x~((1))(n)为初始条件的离散灰色预测模型,证明了灰色预测模型与离散灰色预测模型分别在初始值优化前后的等价性,最后通过一个算例验证了模型的有效性与实用性.     

灰色GM(1,1)模型的改进模型在房地产价格指数预测中的应用

提出了一种结合非线性回归技术的灰色GM(1,1)模型的改进模型.利用我国的房地产价格指数预测作为研究对象,用以验证所提方法的有效性和准确性.根据实证结果,说明了新的改进模型有效提高了经典灰色模型的预测精度.     

A Weighted Goodness-of-Fit Test for GARCH(1,1) Specification

Suppose that (j) is the lag-j autocorrelation of the squared residuals computed from a realization of length n under the assumption that the observations follow a GARCH(1,1) model. We study the asymptotic distribution of the statistics of the form , where the _j are nonnegative summable weights and the matrix , can be estimated from the data. We show that, under weak assumptions on model errors, the statistic Q _n converges in distribution to , where the N _i are iid standard normal. We discuss choices of the weights _j for which the distribution of Q is tabulated. Our results lead to and provide a rigorous justification for Portmanteau goodness-of-fit tests for GARCH(1,1) specification.     

利用GM(1,1)模型预测曲线波形

针对给出的函数y=f(x),x∈[a,b],将其值域进行n等分,设yi为其中任一分点,对应x=xi(i=1,2,…,m),用GM(1,1)模型对序列{x1,x2,…,xm}进行预测,得到曲线y=f(x)在下一段时间与直线y=yi的交点位置.当GM(1,1)模型的误差较大时,可利用带有残差修正的GM(1,1)模型进行残差修正,以提高GM(1,1)模型预测值的精确度.     

GM(1,1)改进模型及其应用

根据 GM( 1 ,1 )灰色模型的指数特性 ,通过在区间上求积分给出了关于背景值的一个比较确切的计算公式 ,讨论了由此建立的 GM( 1 ,1 )改进模型的适用范围和预测精度 .结果表明改进模型比原 GM( 1 ,1 )模型适用性要强、模拟和预测精度要高 ,不仅适用于低增长序列、也适用于高增长序列 ,不仅适用于短期预测 ,同样也适用于中、长期预测     

非平稳时间序列分析的WAVELET—改进GM(1,1)组合方法及其应用

提出非平稳时间序列分析的WAVELET—改进GM(1,1)组合方法.首先利用Mallat算法对非平稳时间序列进行小波分解;然后采用能量阈值选择策略对高频系数进行处理,并将其与低频系数进行小波重构;最后运用改进的GM(1,1)方法预测.该方法不仅能充分拟合低频信息,而且可避免对高频信息的过拟合.实验结果证明,该方法比传统的非平稳时间序列预测方法具有更高的预测精度.     

单调序列的非齐次GM(1,1)模型

GM(1,1)模型的白化解为齐次指数形式,而一般数据呈非齐次指数形式,存在形式上的差异.本文运用非齐次级比与非齐次指数函数的对应关系,对原始序列中相邻数据做差处理,得到新的序列,将非齐次指数序列转换为齐次指数序列,再建立GM(1,1)模型.实例表明,运用初值优化和非齐次化能提高GM(1,1)模型的精度.     

Nilpotency of the Alternator Ideal of a Finitely Generated Binary (-1,1)-Algebra

We prove nilpotency of the alternator ideal of a finitely generated binary (-1,1)-algebra. An algebra is a binary (-1,1)-algebra if its every 2-generated subalgebra is an algebra of type (-1,1). While proving the main theorem we obtain various consequences: a prime finitely generated binary (-1,1)-algebra is alternative; the Mikheev radical of an arbitrary binary (-1,1)-algebra coincides with the locally nilpotent radical; a simple binary (-1,1)-algebra is alternative; the radical of a free finitely generated binary (-1,1)-algebra is solvable. Moreover, from the main result we derive nilpotency of the radical of a finitely generated binary (-1,1)-algebra with an essential identity.     

基于等间距化的非等间距灰色GM(1,1)模型及优化

针对现有非等间距GM(1,1)模型存在的不足,借鉴分段线性插值将非等间距序列等间距化的思想,以非等间距的方法建立了一种新的非等间距GM(1,1)模型,模型不需计算插值数据,可直接利用原始数据建模.然后通过赋予原始数据下标序列变换系数,利用平均模拟相对误差最小的原则确定各参数,建立优化后的非等间距GM(1,1)模型.最后通过算例测试和应用实例表明提出模型的有效性和可行性.