By: |
Dominique Guegan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, Ecole d'économie de Paris - Paris School of Economics - Université Panthéon-Sorbonne - Paris I) |
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. |
Date: |
2008–03 |
URL: |
http://d.repec.org/n?u=RePEc:hal:papers:halshs-00270708_v1&r=for |