Global and local stationary modelling in finance : theory and empirical evidence
AbstractIn 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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Bibliographic InfoPaper 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.
Length: 46 pages
Date of creation: Apr 2007
Date of revision:
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Non-stationarity; distribution function; copula; long-memory; switching; SETAR; Stopbreak models; cumulants; estimation.;
Other versions of this item:
- 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.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-12-01 (All new papers)
- NEP-CFN-2007-12-01 (Corporate Finance)
- NEP-ECM-2007-12-01 (Econometrics)
- NEP-ETS-2007-12-01 (Econometric Time Series)
- NEP-RMG-2007-12-01 (Risk Management)
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- Dominique GuÃ©gan & Jing Zhang, 2006.
"Change analysis of dynamic copula for measuring dependence in multivariate financial data,"
Cahiers de la Maison des Sciences Economiques, UniversitÃ© PanthÃ©on-Sorbonne (Paris 1)
b06090, UniversitÃ© PanthÃ©on-Sorbonne (Paris 1).
- D. Guegan & J. Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Quantitative Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 10(4), pages 421-430.
- Dominique Guegan & Jing Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00368334, HAL.
- Dominique Guegan & Jing Zhang, 2006. "Change analysis of dynamic copula for measuring dependence in multivariate financial data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00189141, HAL.
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