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Global and local stationary modelling in finance : theory and empirical evidence

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Abstract

In this paper we deal with the problem of non-stationarity encountered in a lot of data sets coming from existence of multiple seasonnalities, jumps, volatility, distorsion, aggregation, etc. We study the problem caused by these non stationarities on the estimation of the sample autocorrelation function and give several examples of models for which spurious behaviors is created by this fact. It concerns Markov switching processes, Stopbreak models and SETAR processes. Then, new strategies are suggested to study locally these data sets. We propose first a test based on the k-the cumulants and mainly the construction of a meta-distribution based on copulas for the data set which will permit to take into account all the non-stationarities. This approach suggests that we can be able to do risk management for portfolio containing non stationary assets and also to obtain the distribution function of some specific models.

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File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2007/B07053.pdf
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Bibliographic Info

Paper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number b07053.

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Length: 46 pages
Date of creation: Apr 2007
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Handle: RePEc:mse:cesdoc:b07053

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Keywords: Non-stationarity; distribution function; copula; long-memory; switching; SETAR; Stopbreak models; cumulants; estimation.;

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Cited by:
  1. Dominique Guégan & Jing Zhang, 2006. "Change analysis of dynamic copula for measuring dependence in multivariate financial data," Cahiers de la Maison des Sciences Economiques, Université Panthéon-Sorbonne (Paris 1) b06090, Université Panthéon-Sorbonne (Paris 1).

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