The nature of many real phenomena in physics, economics and finance is inherently non-linear. In this framework, classical time series models based on the linearity assumption are likely to fail in the task of providing an adequate description of the dynamic structure of the phenomena under study. This consideration has contributed to the increasing interest in non-linear time series models which has characterized the last two decades. At the same, the research in this field has also been greatly stimulated by the recent advances in data collection and computing technologies.

The rapidly evolving field of non-linear models for time series is offering a large amount of new models and techniques. Within this, much attention has been dedicated to some specific families of models which have been found to be particularly useful in real applications. Successful examples include, among the others, the class of ARCH models and their generalizations, the Regime Switching models, together with their multivariate extensions. Even if, in these years, some important issues related to the statistical analysis of non-linear time series have been successfully dealt with, there are still many open problems which are currently under study. The emphasis is on model selection, model specification, stochastic properties, such as stationarity and ergodicity, estimation, generation of multi-step ahead forecasts and evaluation of their properties.

We will cover methodological and computational aspects of non-linear time series analysis as well as empirical applications to real case studies.

Focus:

Models for the conditional mean
Models for the conditional variance
Stochastic Properties
Model selection
Estimation and testing
Diagnostics
Recent developments in forecasting
Forecast combination and evaluation
Fat tails
Empirical Applications

Co-Chairs:

Alessandra Amendola Department of Economics and Statistics University of Salerno 84084 Fisciano (SA) - Italy Tel: +39 089962207 Fax: +39 089962049 E-mail: alamendola@unisa.it	Siem Jan Koopman Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam, De Boelelaan 1105, NL 1081 HV Amsterdam The Netherlands Tel: +31 20 444 60 19 Fax: +31 20 444 60 20 E-mail: s.j.koopman@feweb.vu.nl
Christian Francq University Lille 3 Domaine Universitaire du Pont de Bois, BP 149 59653 Villeneuve d'Ascq Cedex France Tel: +33 3 20 41 60 00 Fax: +33 3 20 91 91 71 E-mail: freancqniv-lille3.fr	Wai-Sum Chan, Department of Statistics & Actuarial Science The University of Hong Kong Pokfulam Road, Hong Kong Tel:    +852 2857 8318 Fax:    +852 2858 9041 E-mail: chanws@hku.hk