Nonlinear Structure (nonlinear + structure)

Distribution by Scientific Domains

Selected Abstracts

Northern Atlantic Oscillation effects on the temporal and spatial dynamics of green spruce aphid populations in the UK

Summary 1The role of climate variability in determining the spatial and temporal patterns of numerical fluctuations is a central problem in ecology. The influence of the North Atlantic Oscillation (NAO) index on the population dynamics and spatial synchrony of the green spruce aphid Elatobium abietinum across the UK was shown. 2Fifteen overlapping time series within the UK were analysed; we used nonparametric models for determining the feedback nonlinear structure and the climatic effects. The spatial synchrony of these populations and the relationship between synchrony and NAO was estimated. 3From the 15 time series across the UK, 11 showed positive and significant NAO effects. In most of the cases the NAO effects were nonlinear showing strong negative effects of low values. The NAO variation improve the explained variance of the first-order feedback models in 14·5%; ranging from 0% to 48%. All data showed strong-nonlinear (concave) feedback structure. In most of the localities the explained variance by the first-order feedback was about 50,60%. 4The spatial synchrony of the per capita growth rates and residuals is high across long distances for those populations affected by NAO. The correlation function predicts a spatial scale of synchrony of about 350,400 km for NAO influenced populations. 5We think that simple population theoretical models describing the link between NAO fluctuations and green spruce aphid dynamics may be fundamental for predicting and simulating the consequences of different climatic scenarios of the future. [source]

Nonlinear dynamics in high-frequency intraday financial data: Evidence for the UK long gilt futures market

David G. McMillan
Recent research investigating the properties of high-frequency financial data has suggested that the stochastic nonlinearity widely present in such data may be characterized by heterogeneous components in conditional volatility, and nonlinear dependence of threshold autoregressive form due to market frictions. This article tests for the presence of such effects in intraday long gilt futures returns on the UK LIFFE market. Tests against the null of linearity indicate the significance of smooth transition autoregressive nonlinearities in such returns at the 5-min frequency, which entails a first-order autoregressive process with switching intercept. This nonlinear structure is robust to the presence of asymmetric and component structures in conditional variance, and consistent with the existence of heterogeneous traders facing different levels of transaction costs, noise trader risk, or capital constraints. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:1037,1057, 2002 [source]

Dynamic stability of the three-dimensional axisymmetric Navier-Stokes equations with swirl

Thomas Y. Hou
In this paper, we study the dynamic stability of the three-dimensional axisymmetric Navier-Stokes Equations with swirl. To this purpose, we propose a new one-dimensional model that approximates the Navier-Stokes equations along the symmetry axis. An important property of this one-dimensional model is that one can construct from its solutions a family of exact solutions of the three-dimensionaFinal Navier-Stokes equations. The nonlinear structure of the one-dimensional model has some very interesting properties. On one hand, it can lead to tremendous dynamic growth of the solution within a short time. On the other hand, it has a surprising dynamic depletion mechanism that prevents the solution from blowing up in finite time. By exploiting this special nonlinear structure, we prove the global regularity of the three-dimensional Navier-Stokes equations for a family of initial data, whose solutions can lead to large dynamic growth, but yet have global smooth solutions. © 2007 Wiley Periodicals, Inc. [source]

Robust modelling of DTARCH models

Yer Van Hui
Summary, Autoregressive conditional heteroscedastic (ARCH) models and its extensions are widely used in modelling volatility in financial time series. One of the variants, the double-threshold autoregressive conditional heteroscedastic (DTARCH) model, has been proposed to model the conditional mean and the conditional variance that are piecewise linear. The DTARCH model is also useful for modelling conditional heteroscedasticity with nonlinear structures such as asymmetric cycles, jump resonance and amplitude-frequence dependence. Since asset returns often display heavy tails and outliers, it is worth studying robust DTARCH modelling without specific distribution assumption. This paper studies DTARCH structures for conditional scale instead of conditional variance. We examine L1 -estimation of the DTARCH model and derive limiting distributions for the proposed estimators. A robust portmanteau statistic based on the L1 -norm fit is constructed to test the model adequacy. This approach captures various nonlinear phenomena and stylized facts with desirable robustness. Simulations show that the L1 -estimators are robust against innovation distributions and accurate for a moderate sample size, and the proposed test is not only robust against innovation distributions but also powerful in discriminating the delay parameters and ARCH models. It is noted that the quasi-likelihood modelling approach used in ARCH models is inappropriate to DTARCH models in the presence of outliers and heavy tail innovations. [source]