Maximum-entropy Scattering Models for Financial Time Series
Roberto Leonarduzzi, Gaspar Rochette, Jean-Phillipe Bouchaud, Stéphane Mallat
Abstract
Modeling time series with complex statistical properties such as heavy-tails, long-range dependence, and temporal asymmetries remains an open problem. In particular, financial time series exhibit such properties. Existing models suffer from serious limitations and often rely on high-order moments. We introduce a wavelet-based maximum entropy model for such random processes, based on new scattering and phase-harmonic moments. We analyze the model's performance with a synthetic multifractal random process and real-world financial time series. We show that scattering moments capture heavy tails and multifractal properties without estimating high-order moments. Further, we show that additional phase-harmonic terms capture temporal asymmetries.
BibTeX
@inproceedings{icassp2019_maximumentropysc,
title = {Maximum-entropy Scattering Models for Financial Time Series},
author = {Roberto Leonarduzzi and Gaspar Rochette and Jean-Phillipe Bouchaud and Stéphane Mallat},
booktitle = {ICASSP 2019},
year = {2019}
}