Non-stationarity and meta-distribution

Dominique Guegan ()
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Dominique Guegan: Centre d'Economie de la Sorbonne et Paris School of Economics, https://cv.archives-ouvertes.fr/dominique-guegan

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

Abstract: 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

Keywords: Non-stationarity; switching processes; SETAR processes; jumps; forecast; risk management; copula; probability distribution function (search for similar items in EconPapers)
JEL-codes: C32 C51 G12 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2008-03
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-rmg
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ftp://mse.univ-paris1.fr/pub/mse/CES2008/B08026.pdf (application/pdf)

Related works:
Working Paper: Non-stationarity and meta-distribution (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:b08026

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