HAL : halshs-00270708, version 1

Fiche détaillée

Récupérer au format BibTeX - EndNote - TEI - RefWorks -

Non-stationarity and meta-distribution

Dominique Guegan 1, 2

(03/2008)

In this paper we deal with the problem of non-stationarity encountered in a lot of data sets, mainly in financial and economics domains, coming from the presence of multiple seasonnalities, jumps, volatility, distorsion, aggregation, etc. Existence of non-stationarity involves spurious behaviors in estimated statistics as soon as we work with finite samples. We illustrate this fact using Markov switching processes, Stopbreak models and SETAR processes. Thus, working with a theoretical framework based on the existence of an invariant measure for a whole sample is not satisfactory. Empirically alternative strategies have been developed introducing dynamics inside modelling mainly through the parameter with the use of rolling windows. A specific framework has not yet been proposed to study such non-invariant data sets. The question is difficult. Here, we address a discussion on this topic proposing the concept of meta-distribution which can be used to improve risk management strategies or forecasts.

1 :	Centre d'économie de la Sorbonne (CES)
	CNRS : UMR8174 – Université Panthéon-Sorbonne - Paris I
2 :	Ecole d'Économie de Paris - Paris School of Economics (EEP-PSE)
	Ecole d'Économie de Paris

Discipline

:

Sciences de l'Homme et Société/Economie et finances Sciences de l'Homme et Société/Méthodes et statistiques Mathématiques/Probabilités Mathématiques/Statistiques Statistiques/Théorie

Mots-Clés : Non-stationarity – switching processes – SETAR processes – jumps – forecast – risk management – copula – probability distribution function.

halshs-00270708, version 1

http://halshs.archives-ouvertes.fr/halshs-00270708/fr/

oai:halshs.archives-ouvertes.fr:halshs-00270708

Contributeur : Lucie Label <>

Soumis le : Lundi 7 Avril 2008, 11:53:21

Dernière modification le : Mardi 21 Avril 2009, 14:19:22