Estimation and strict stationarity testing of ARCH processes based on weighted least squares

This paper proposes a unified unconstrained two-stage weighted least squares estimate (2S-WLSE) theory for both stationary and nonstationary ARCH(1) processes. Without assuming strict stationarity, we show that the unconstrained 2S-WLSE of the conditional variance slope ARCH(1) parameter is consistent and asymptotically Gaussian and has the same asymptotic variance as its unconstrained quasi-maximum likelihood counterpart. Moreover, a consistent estimate of the asymptotic variance of the 2S-WLSE is provided irrespective of the stationarity requirement. As a result, strict stationarity testing of the ARCH process is considered. A numerical illustration on simulated and real data assesses the theory in finite samples.     

Local Unit Roots and Global Stationarity of TARMA Models

In this paper the (strict and weak) stationarity of threshold autoregressive moving average models is discussed. After examining the strict stationarity, mainly based on the random coefficient autoregressive representation of the model, we provide sufficient conditions for its weak stationarity that allow to obtain a wider stationarity region with respect to some previous results given in the literature. These conditions are discussed to distinguish between global and local stationarity, whose relation has been considered in detail. The threshold process has been further evaluated to face the problem related to the so called existence of a threshold structure in the data generating process that is strictly related to the stationarity and has significant relevance when the parameters of the model have to be estimated.     

Asymptotic inference of unstable periodic ARCH processes

This paper studies asymptotic properties of the quasi maximum likelihood and weighted least squares estimates (QMLE and WLSE) of the conditional variance slope parameters of a strictly unstable ARCH model with periodically time varying coefficients (PARCH in short). The model is strictly unstable in the sense that its parameters lie outside the strict periodic stationarity domain and its boundary. Obtained from the regression form of the PARCH, the WLSE is a variant of the least squares method weighted by the square of the conditional variance evaluated at any fixed value in the parameter space. In calculating the QMLE and WLSE, the conditional variance intercepts are set to any arbitrary values not necessarily the true ones. The theoretical finding is that the QMLE and WLSE are consistent and asymptotically Gaussian with the same asymptotic variance irrespective of the fixed conditional variance intercepts and the weighting parameters. So because of its numerical complexity, the QMLE may be dropped in favor of the WLSE which enjoys closed form.     

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.     

基于DCC-GARCH模型的金属期货市场与外汇、货币市场的动态相关性研究

在本文中主要是把DCC-GARCH模型,引入到金属期货市场分别与外汇市场和货币市场之间的动态相关性领域进行研究。通过对LME铜分别与RMB/USD、USD/EU、JPY/USD三个汇率之间的动态相关性研究,研究结果表明:金属期货市场与外汇市场之间有一定的动态相关性,但是不是很强烈;通过对LME铜分别与RMB-USD、USD-EU、JPY-/USD三个利差变化率之间的动态相关性研究,结果表明:金属期货市场与货币市场之间的动态相关性并不明显。     

Study of the higher-order asymptotic behavior of quantum field expansions in the theory of two-dimensional fully developed turbulence

We use a simplified (0+1)-dimensional theory to develop approaches for studying the higher-order asymptotic behavior of quantum field expansions in the two-dimensional theory of fully developed turbulence. We consider the asymptotic behavior of the correlation function in the small-time limit in the theory of fully developed turbulence and derive and investigate the stationarity equation. We show that the perturbation series in this limit has a finite convergence radius.     

Asymptotic Shapes of large Cells in Random Tessellations

We establish a close relationship between isoperimetric inequalities for convex bodies and asymptotic shapes of large random polytopes, which arise as cells in certain random mosaics in d-dimensional Euclidean space. These mosaics are generated by Poisson hyperplane processes satisfying a few natural assumptions (not necessarily stationarity or isotropy). The size of large cells is measured by a class of general functionals. The main result implies that the asymptotic shapes of large cells are completely determined by the extremal bodies of an inequality of isoperimetric type, which connects the size functional and the expected number of hyperplanes of the generating process hitting a given convex body. We obtain exponential estimates for the conditional probability of large deviations of zero cells from asymptotic or limit shapes, under the condition that the cells have large size. This work was supported in part by the European Network PHD, FP6 Marie Curie Actions, RTN, Contract MCRN-511953. Received: May 2005 Accepted: September 2005     

Heteroscedastic mixture transition distribution (HMTD) model

The paper further studies the heteroscedastic mixture transition distribution (HMTD) model introduced by Berchtold. Both the expectation and the standard deviation of each component are written as functions of the past of the process. The stationarity conditions are derived. An expectation conditional maximization (ECM) algorithm is used and shown to work well for estimation, the model selection problem is addressed, and the formulaes for computing the observed information matrix are derived. The shape changing feature of conditional distributions makes the model capable of modelling time series with asymmetric or multimodal distribution. The model is applied to several simulated and real datasets with satisfactory results.     

非对称广义自回归条件异方差的新模型

本文提出了一个新的非对称广义自回归条件异方差的新模型,证明了该模型宽平稳及其最简模型偶数价矩存在的充要条件。     

基于CAViaR的DCC模型及其对中国股市的实证研究

VaR是金融风险度量方面研究的热点.CAViaR模型可以用来直接计算单个资产的VaR,DCC模型可以用于刻画资产间的相关性.结合这两个模型,通过分位数估计方差的方法,提出了基于CAViaR的DCC模型来计算投资组合的VaR.对中国股市的实证研究表明其具有更好的效果.     

Kernel estimation for stationary density of Markov chains with general state space

Let {X _n } _n ≥0 be a Markov chain with stationary distributionf(x)ν(dx), ν being a σ-finite measure onE⊂R ^d . Under strict stationarity and mixing conditions we obtain the consistency and asymptotic normality for a general class of kernel estimates off(·). When the assumption of stationarity is dropped these results are extended to geometrically ergodic chains. Partially supported by CAPES. Partially supported by CNPq, PROCAD/CAPES, PRONEX/FAPDF and FINATEC/UnB.     

Nonparametric conditional efficiency measures: asymptotic properties

Cazals et al. (J. Econom. 106: 1–25, 2002), Daraio and Simar (J. Prod. Anal. 24: 93–121, 2005; Advanced Robust and Nonparametric Methods in Efficiency Analysis, 2007a; J. Prod. Anal. 28: 13–32, 2007b) developed a conditional frontier model which incorporates the environmental factors into measuring the efficiency of a production process in a fully nonparametric setup. They also provided the corresponding nonparametric efficiency measures: conditional FDH estimator, conditional DEA estimator. The two estimators have been applied in the literature without any theoretical background about their statistical properties. The aim of this paper is to provide an asymptotic analysis (i.e. asymptotic consistency and limit sampling distribution) of the conditional FDH and conditional DEA estimators.     

Confidence Intervals of Variance Functions in Generalized Linear Model

In this paper we introduce an appealing nonparametric method for estimating variance and conditional variance functions in generalized linear models （GLMs）, when designs are fixed points and random variables respectively, Bias-corrected confidence bands are proposed for the （conditional） variance by local linear smoothers. Nonparametric techniques are developed in deriving the bias-corrected confidence intervals of the （conditional） variance. The asymptotic distribution of the proposed estimator is established and show that the bias-corrected confidence bands asymptotically have the correct coverage properties. A small simulation is performed when unknown regression parameter is estimated by nonparametric quasi-likelihood. The results are also applicable to nonparamctric autoregressive times series model with heteroscedastic conditional variance.     

条件自回归极差模型的渐近性质

在误差项独立同分布的条件下，本文讨论了条件自回归极差模型条件解和无条件解的渐近性质．利用随机游动的极限性质得到了条件解收敛于无条件解的充分条件，任意阶矩有限的充要条件以及外生变量与内生变量持续性的充要条件．所得到的结论适用于已得到应用的平稳条件自回归极差模型，也适用于包含单位根的模型和满足条件的其他类型的非平稳过程，为模型的统计推断提供了理论基础．     

Impact of correlation crises in risk theory: Asymptotics of finite-time ruin probabilities for heavy-tailed claim amounts when some independence and stationarity assumptions are relaxed

In the renewal risk model, several strong hypotheses may be found too restrictive to model accurately the complex evolution of the reserves of an insurance company. In the case where claim sizes are heavy-tailed, we relax the independence and stationarity assumptions and extend some asymptotic results on finite-time ruin probabilities, to take into account possible correlation crises like the one recently bred by the sub-prime crisis: claim amounts, in general assumed to be independent, may suddenly become strongly positively dependent. The impact of dependence and non-stationarity is analyzed and several concrete examples are given.     

中国股市行业板块的动态相关性建模——基于DCC—MVGARCH模型

相关性的讨论是现代金融分析的重要内容,行业板块的相关分析是组合投资的关键步骤。基于能刻画动态相关的DCC-MVGARCH模型对我国波动剧烈的六个股市行业板块进行了相关性研究,结果表明：十个板块相关性序列可看成常量,Pearson相关系数仍能刻画其相对大小,这为机构投资者按Pearson相关系数进行组合构建提供了实证依据;五个动态相关性序列是宽平稳而非严平稳的,适合采用随机过程建模以实现预测,另外动态相关性与时变波动率存在一定的关系,当波动率增强时,相关性有随之增大的趋势。     

Impact of correlation crises in risk theory: Asymptotics of finite-time ruin probabilities for heavy-tailed claim amounts when some independence and stationarity assumptions are relaxed

In the renewal risk model, several strong hypotheses may be found too restrictive to model accurately the complex evolution of the reserves of an insurance company. In the case where claim sizes are heavy-tailed, we relax the independence and stationarity assumptions and extend some asymptotic results on finite-time ruin probabilities, to take into account possible correlation crises like the one recently bred by the sub-prime crisis: claim amounts, in general assumed to be independent, may suddenly become strongly positively dependent. The impact of dependence and non-stationarity is analyzed and several concrete examples are given.     

Heavy-tailed limits for medium size jobs and comparison scheduling

We study the conditional sojourn time distributions of processor sharing (PS), foreground background processor sharing (FBPS) and shortest remaining processing time first (SRPT) scheduling disciplines on an event where the job size of a customer arriving in stationarity is smaller than exactly k≥0 out of the preceding m≥k arrivals. Then, conditioning on the preceding event, the sojourn time distribution of this newly arriving customer behaves asymptotically the same as if the customer were served in isolation with a server of rate (1−ρ)/(k+1) for PS/FBPS, and (1−ρ) for SRPT, respectively, where ρ is the traffic intensity. Hence, the introduced notion of conditional limits allows us to distinguish the asymptotic performance of the studied schedulers by showing that SRPT exhibits considerably better asymptotic behavior for relatively smaller jobs than PS/FBPS. Inspired by the preceding results, we propose an approximation to the SRPT discipline based on a novel adaptive job grouping mechanism that uses relative size comparison of a newly arriving job to the preceding m arrivals. Specifically, if the newly arriving job is smaller than k and larger than m−k of the previous m jobs, it is routed into class k. Then, the classes of smaller jobs are served with higher priorities using the static priority scheduling. The good performance of this mechanism, even for a small number of classes m+1, is demonstrated using the asymptotic queueing analysis under the heavy-tailed job requirements. We also discuss refinements of the comparison grouping mechanism that improve the accuracy of job classification at the expense of a small additional complexity. This work is supported by NSF Grant 0615126.     

Asymptotic theory of conditional inference for stochastic processes

This paper develops an approach to conditional inference for nonergodic stochastic process models by considering asymptotic properties. The context for most of the analysis is that of Le Cam's local asymptotic theory: in particular, the locally asymptotically mixed normal (LAMN) situation. An attempt has been made to evaluate local asymptotic properties of global procedures.