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.     

ARCH模型分布变点监测与应用

分布变点监测是时间序列交点分析的一个重要内容.为将分布交点监测从线性时间序列模型拓展到非线性时间序列模型,提出一种经验特征函数型的统计量监测ARCH模型误差项平方的分布变点,给出了监测统计量在原假设下的极限分布,并证明了此方法的一致性,用Bootstrap重抽样方法获得了极限分布的临界值,并和Kolmogorov-smirnov型监测统计量进行了比较.模拟结果和实例分析说明了当已观测样本量较大时,采用经验特征函数型统计量监测效果较好.     

变系数ARCH模型绝对值序列的强大数定律

本文提出一种新的模型: 变系数ARCH模型, 其中系数是时间变量的函数. 这与在不同的时间区间上拟和模型时系数是不同的这一事实是相符的. 所以这一模型更符合实际, 更合理. 由于系数是时间变量的函数, 所以这一模型有许多潜在的优点, 它更具有灵活性. 在本文中我们主要研究变系数ARCH模型绝对值序列的强大数定律.     

厚尾相依序列的均值变点估计

本文研究了厚尾相依序列的均值变点估计. 证明了变点的CUSUM估计的一致性并得到了收敛速度.在方差无穷的情况下推广了H\'{a}jek--R\'{e}nyi不等式.     

至多一个分布变点的非参数检验及其渐近性质

本文研究了连续分布函数变点的假设检验问题,通过秩统计量和次序统计量方法,得到了相应的检验统计量及其渐近性质.     

跳跃度变点模型变点估计的收敛速度

对至多只有一个跳跃度变点τ[0的模型Xi=α+θI{[nτ0]＜i≤n}+εi,其中εi独立同分布,i=1,…,n.利用滑窗方法给出了强、弱相合性以及强、弱收敛速度,并进一步研究了在局部对立条件下变点估计的OP收敛速度.     

跳跃度变点估计的Op收敛速度

对至多只有一个跳跃度变点T0的变点模型Xi=a+θI{[nT0]＜i≤n+εi,i=1,2,…,n,假定{εi,i=1,2,…,n}是均值为0、方差有限的独立同分布误差序列,其中T0未知,称之为变点.在利用滑窗方法给出变点估计的基础上,进一步研究了局部对立假设条件下变点估计(T)的OP收敛速度.     

斜率变点估计的强收敛速度

对至多只有一个斜率变点的模型,在误差分布为非正态时,本文利用滑窗方法给出了变点估计的强、弱相合性和强、弱收敛速度,同对在局部对立假设下研究了变点估计的O_p收敛速度.     

位置参数变点估计的强收敛速度

对至多一个变点的位置参数变点模型,文中在运用滑窗方法给出了检测变点存在性的U-统计量基础上,进一步研究了变点估计量的强相合性和强收敛速度.     

单参数Pareto分布变点估计研究

研究单参数Pareto分布存在变点时的估计问题,分别利用极大似然估计法和贝叶斯方法对单参数Pareto分布的变点进行估计,并运用Matlab软件进行随机模拟,随机结果表明贝叶斯方法与极大似然估计相比,估计值更接近真值.     

Testing for parameter changes in ARCH models

The paper develops the asymptotic theory for CUSUM-type tests for a change point in parameters of an ARCH(∞) model. Special attention is given to asymptotics under local alternatives. Research partially supported by EPSRC grant GR/L/78222 from the University of Liverpool. Partially supported by NATO grant CRG 960503. Partially supported by the Lithuanian State Science and Studies Foundation, Grant K-014. Published in Lietuvos Matematikos Rinkinys, Vol. 39, No. 2, pp. 231–247, April–June, 1999.     

Maximum likelihood estimation of a change-point in the distribution of independent random variables: General multiparameter case

In a sequence ofn independent random variables the pdf changes fromf(x, 0) tof(x, 0 + δv_n⁻¹) after the firstnλ variables. The problem is to estimateλ (0, 1 ), where 0 and δ are unknownd-dim parameters andv_n → ∞ slower thann^1/2. Let_n denote the maximum likelihood estimator (mle) ofλ. Analyzing the local behavior of the likelihood function near the true parameter values it is shown under regularity conditions that ifn_n²(− λ) is bounded in probability asn → ∞, then it converges in law to the timeT_(δjδ)^1/2 at which a two-sided Brownian motion (B.M.) with drift1/2(δ′Jδ)^1/2ton(−∞, ∞) attains its a.s. unique minimum, whereJ denotes the Fisher-information matrix. This generalizes the result for small change in mean of univariate normal random variables obtained by Bhattacharya and Brockwell (1976,Z. Warsch. Verw. Gebiete37, 51–75) who also derived the distribution ofT_μ forμ > 0. For the general case an alternative estimator is constructed by a three-step procedure which is shown to have the above asymptotic distribution. In the important case of multiparameter exponential families, the construction of this estimator is considerably simplified.     

Inference of change-point in single index models

Single index models are widely used in medicine, econometrics and some other fields. In this paper, we consider the inference of a change point problem in single index models. Based on density-weighted average derivative estimation (ADE) method, we propose a statistic to test whether a change point exists or not. The null distribution of the test statistic is obtained using a permutation technique. The permuted statistic is rigorously shown to have the same distribution in the limiting sense under both null and alternative hypotheses. After the null hypothesis of no change point is rejected, an ADE-based estimate of the change point is proposed under assumption that the change point is unique. A simulation study confirms the theoretical results.     

Ergodicity of nonlinear first order autoregressive models

Criteria are derived for ergodicity and geometric ergodicity of Markov processes satisfyingX _n+1 =f(X _n )+(X _n ) _n+1 , wheref, are measurable, { _n } are i.i.d. with a (common) positive density,E| _n |>. In the special casef(x)/x has limits, , asx– andx+, respectively, it is shown that <1, <1, <1 is sufficient for geometric ergodicity, and that <-1, 1, 1 is necessary for recurrence.     

Power transformation and threshold modeling for ARCH innovations with applications to tests for ARCH structure

A new class of power-transformed threshold ARCH models is proposed as a threshold-asymmetric generalization of the nonlinear ARCH considered by Higgins and Bera [Internat. Econom. Rev. 33 (1992) 137]. This class is rich enough to include diverse nonlinear and nonsymmetric ARCH models which have been spelled out in the literature. Geometric ergodicity of the model and existence of stationary moments are studied. The model facilitates discussing ARCH structures and hence large sample tests for ARCH structures are investigated via local asymptotic normality approach. Semiparametric tests are also discussed for the case when the error density is unknown.     

Tail dependence of random variables from ARCH and heavy-tailed bilinear models

Discussed in this paper is the dependent structure in the tails of distributions of random variables from some heavy-tailed stationary nonlinear time series. One class of models discussed is the first-order autoregressive conditional heteroscedastic (ARCH) process introduced by Engle (1982). The other class is the simple first-order bilinear models driven by heavy-tailed innovations. We give some explicit formulas for the asymptotic values of conditional probabilities used for measuring the tail dependence between two random variables from these models. Our results have significant meanings in finance.     

On a multi-channel change-point problem

A continuous change-point problem is studied in which N independent diffusion processes X _j are observed. Each process X _j is associated with a “channel”, each has an unknown piecewise constant drift and the unit diffusion coefficient. All the channels are connected only by a common change-point of drift. As the result, a change-point problem is defined in which the unknown and unidentifiable drift forms a 2N-dimensional nuisance parameter. The asymptotics of the minimax rate in estimating the change-point is studied as N → ∞. This rate is compared with the case of the known drift. This problem is a special case of an open change-point detection problem in the high-dimensional diffusion with nonparametric drift.      

ARCH类模型研究及其在沪市A股中的应用

本文主要介绍ARCH(AutoregressiveConditionalHeteroskedasticity)模型、GARCH模型和E GARCH模型 ,分析这些模型的特点和适用范围 ,并在模型中引入t分布取代正态分布假设 ,最后利用这些模型对上证指数进行了实证分析。     

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.     

Estimating the Diffusion Coefficient for Diffusions Driven by fBm

When the Hurst coefficient of a fBm B _t ^H is greater than 1/2, it is possible to define a stochastic integral with respect to B _t ^H as the pathwise limit of Riemann sums. In this article we consider diffusion equations of the type X_t = x₀ + ₀ ^T (X_s) dB_s ^H. We then construct a simple-to-use estimator of the diffusion coefficient (x), based on the number of crossings of level x of the process X _t. We then study consistency in probability of this estimator and calculate convergence rates in probability.