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Communication Dans Un Congrès Année : 2019

Maximum-entropy Scattering Models for Financial Time Series

Résumé

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.
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Dates et versions

hal-04023780 , version 1 (10-03-2023)

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Roberto Leonarduzzi, Gaspar Rochette, Jean-Phillipe Bouchaud, Stephane Mallat. Maximum-entropy Scattering Models for Financial Time Series. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, Brighton, United Kingdom. pp.5496-5500, ⟨10.1109/ICASSP.2019.8683734⟩. ⟨hal-04023780⟩
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