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

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  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

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.

Suggested Citation

  • Dominique Guegan, 2007. "Global and local stationary modelling in finance: theory and empirical evidence," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00187875, HAL.
    • Handle: RePEc:hal:cesptp:halshs-00187875
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00187875
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    Cited by:

    1. D. Guegan & J. Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 421-430.
    2. Schnaubelt, Matthias, 2019. "A comparison of machine learning model validation schemes for non-stationary time series data," FAU Discussion Papers in Economics 11/2019, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

    More about this item

    Keywords

    Non-stationarity; distribution function; copula; long-memory; switching; SETAR; Stopbreak models; fonction de distribution; Non stationnarité; copule; processus longue mémoire; Commutation; Modèles StopBreak; Cumulants; Théorie de l'estimation;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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