Unsupervised learning of Markov-switching stochastic volatility with an application to market data

Ivan Gorynin; Emmanuel Monfrini; Wojciech Pieczynski

doi:10.1109/MLSP.2016.7738821

Unsupervised learning of Markov-switching stochastic volatility with an application to market data

Université Paris-Saclay

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

We introduce a new method for estimating the regime-switching stochastic volatility models from the historical prices. Our methodology is based on a novel version of the assumed density filter (ADF). We estimate the switching model by maximizing the quasi-likelihood function of our ADF. The simulation experiments show the efficiency of our method. Then we analyze different market price histories for consistency with a regime-shifting model.

langue originale	Anglais
titre	2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
rédacteurs en chef	Kostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
Editeur	IEEE Computer Society
ISBN (Electronique)	9781509007462
Les DOIs	https://doi.org/10.1109/MLSP.2016.7738821
état	Publié - 8 nov. 2016
Modification externe	Oui
Evénement	26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italie Durée: 13 sept. 2016 → 16 sept. 2016

Série de publications

Nom	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume	2016-November
ISSN (imprimé)	2161-0363
ISSN (Electronique)	2161-0371

Une conférence

Une conférence	26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Pays/Territoire	Italie
La ville	Vietri sul Mare, Salerno
période	13/09/16 → 16/09/16

Accès au document

10.1109/MLSP.2016.7738821

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Gorynin, I., Monfrini, E., & Pieczynski, W. (2016). Unsupervised learning of Markov-switching stochastic volatility with an application to market data. Dans K. Diamantaras, A. Uncini, F. A. N. Palmieri, & J. Larsen (eds.), 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings Article 7738821 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol 2016-November). IEEE Computer Society. https://doi.org/10.1109/MLSP.2016.7738821

Gorynin, Ivan ; Monfrini, Emmanuel ; Pieczynski, Wojciech. / Unsupervised learning of Markov-switching stochastic volatility with an application to market data. 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings. Editeur / Kostas Diamantaras ; Aurelio Uncini ; Francesco A. N. Palmieri ; Jan Larsen. IEEE Computer Society, 2016. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP).

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title = "Unsupervised learning of Markov-switching stochastic volatility with an application to market data",

abstract = "We introduce a new method for estimating the regime-switching stochastic volatility models from the historical prices. Our methodology is based on a novel version of the assumed density filter (ADF). We estimate the switching model by maximizing the quasi-likelihood function of our ADF. The simulation experiments show the efficiency of our method. Then we analyze different market price histories for consistency with a regime-shifting model.",

keywords = "Assumed density filtering, Gaussian quadrature, Markov-switching stochastic volatility models, Quasi-maximum likelihood, Stochastic volatility",

author = "Ivan Gorynin and Emmanuel Monfrini and Wojciech Pieczynski",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings ; Conference date: 13-09-2016 Through 16-09-2016",

year = "2016",

month = nov,

day = "8",

doi = "10.1109/MLSP.2016.7738821",

language = "English",

series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",

publisher = "IEEE Computer Society",

editor = "Kostas Diamantaras and Aurelio Uncini and Palmieri, \{Francesco A. N.\} and Jan Larsen",

booktitle = "2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings",

}

Gorynin, I, Monfrini, E & Pieczynski, W 2016, Unsupervised learning of Markov-switching stochastic volatility with an application to market data. Dans K Diamantaras, A Uncini, FAN Palmieri & J Larsen (eds), 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings., 7738821, IEEE International Workshop on Machine Learning for Signal Processing, MLSP, VOL. 2016-November, IEEE Computer Society, 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings, Vietri sul Mare, Salerno, Italie, 13/09/16. https://doi.org/10.1109/MLSP.2016.7738821

Unsupervised learning of Markov-switching stochastic volatility with an application to market data. / Gorynin, Ivan; Monfrini, Emmanuel ; Pieczynski, Wojciech.
2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings. Ed. / Kostas Diamantaras; Aurelio Uncini; Francesco A. N. Palmieri; Jan Larsen. IEEE Computer Society, 2016. 7738821 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol 2016-November).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Unsupervised learning of Markov-switching stochastic volatility with an application to market data

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AU - Monfrini, Emmanuel

AU - Pieczynski, Wojciech

PY - 2016/11/8

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N2 - We introduce a new method for estimating the regime-switching stochastic volatility models from the historical prices. Our methodology is based on a novel version of the assumed density filter (ADF). We estimate the switching model by maximizing the quasi-likelihood function of our ADF. The simulation experiments show the efficiency of our method. Then we analyze different market price histories for consistency with a regime-shifting model.

AB - We introduce a new method for estimating the regime-switching stochastic volatility models from the historical prices. Our methodology is based on a novel version of the assumed density filter (ADF). We estimate the switching model by maximizing the quasi-likelihood function of our ADF. The simulation experiments show the efficiency of our method. Then we analyze different market price histories for consistency with a regime-shifting model.

KW - Assumed density filtering

KW - Gaussian quadrature

KW - Markov-switching stochastic volatility models

KW - Quasi-maximum likelihood

KW - Stochastic volatility

UR - https://www.scopus.com/pages/publications/85001955835

U2 - 10.1109/MLSP.2016.7738821

DO - 10.1109/MLSP.2016.7738821

M3 - Conference contribution

AN - SCOPUS:85001955835

T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP

BT - 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings

A2 - Diamantaras, Kostas

A2 - Uncini, Aurelio

A2 - Palmieri, Francesco A. N.

A2 - Larsen, Jan

PB - IEEE Computer Society

T2 - 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings

Y2 - 13 September 2016 through 16 September 2016

ER -

Gorynin I, Monfrini E , Pieczynski W. Unsupervised learning of Markov-switching stochastic volatility with an application to market data. Dans Diamantaras K, Uncini A, Palmieri FAN, Larsen J, rédacteurs en chef, 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings. IEEE Computer Society. 2016. 7738821. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). doi: 10.1109/MLSP.2016.7738821

Unsupervised learning of Markov-switching stochastic volatility with an application to market data

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