Year: 2015
Type: article
Abstract: We discuss how maximum entropy methods may be applied to the reconstruction of Markov processes underlying empirical time series and compare this approach to usual frequency sampling. It is shown that, in low dimension, there exists a subset of the space of stochastic matrices for which the MaxEnt method is more efficient than sampling, in the sense that shorter historical samples have to be considered to reach the same accuracy. Considering short samples is of particular interest when modelling smoothly non-stationary processes, which provides, under some conditions, a powerful forecasting tool. The method is illustrated for a discretized empirical series of exchange rates.
Source: Europhysics Letters (EPL)
Authors: G. Chliamovitch, A. Dupuis, A. Golub, B. Chopard
Institution: University of Geneva
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: bronze
Funder: European Commission
OpenAlex ID: W2006249928