Pairwise Markov models for stock index forecasting

Abstract : Common well-known properties of time series of financial asset values include volatility clustering and asymmetric volatility phenomenon. Hidden Markov models (HMMs) have been proposed for modeling these characteristics, however, due to their simplicity, HMMs may lack two important features. We identify these features and propose modeling financial time series by recent Pairwise Markov models (PMMs) with a finite discrete state space. PMMs are extended versions of HMMs and allow a more flexible modeling. A real-world application example demonstrates substantial gains of PMMs compared to the HMM
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Communication dans un congrès
EUSIPCO 2017 : 25th European Signal Processing Conference, Aug 2017, Kos Island, Greece. EURASIP Proceedings EUSIPCO 2017 : 25th European Signal Processing Conference, pp.2095 - 2099, 2017
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https://hal.archives-ouvertes.fr/hal-01611333
Contributeur : Médiathèque Télécom Sudparis & Institut Mines Télécom Business School <>
Soumis le : jeudi 5 octobre 2017 - 16:49:12
Dernière modification le : jeudi 11 janvier 2018 - 06:27:35

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  • HAL Id : hal-01611333, version 1

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Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski. Pairwise Markov models for stock index forecasting. EUSIPCO 2017 : 25th European Signal Processing Conference, Aug 2017, Kos Island, Greece. EURASIP Proceedings EUSIPCO 2017 : 25th European Signal Processing Conference, pp.2095 - 2099, 2017. 〈hal-01611333〉

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