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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Editors	Kostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
Publisher	IEEE Computer Society
ISBN (Electronic)	9781509007462
DOIs	https://doi.org/10.1109/MLSP.2016.7738821
Publication status	Published - 8 Nov 2016
Externally published	Yes
Event	26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy Duration: 13 Sept 2016 → 16 Sept 2016

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume	2016-November
ISSN (Print)	2161-0363
ISSN (Electronic)	2161-0371

Conference

Conference	26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Country/Territory	Italy
City	Vietri sul Mare, Salerno
Period	13/09/16 → 16/09/16

Keywords

Assumed density filtering
Gaussian quadrature
Markov-switching stochastic volatility models
Quasi-maximum likelihood
Stochastic volatility

Access to Document

10.1109/MLSP.2016.7738821

Cite this

Gorynin, I., Monfrini, E., & Pieczynski, W. (2016). Unsupervised learning of Markov-switching stochastic volatility with an application to market data. In 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. editor / 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,

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doi = "10.1109/MLSP.2016.7738821",

language = "English",

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

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Gorynin, I, Monfrini, E & Pieczynski, W 2016, Unsupervised learning of Markov-switching stochastic volatility with an application to market data. in 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, Italy, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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

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DO - 10.1109/MLSP.2016.7738821

M3 - Conference contribution

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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

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A2 - Uncini, Aurelio

A2 - Palmieri, Francesco A. N.

A2 - Larsen, Jan

PB - IEEE Computer Society

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Gorynin I, Monfrini E , Pieczynski W. Unsupervised learning of Markov-switching stochastic volatility with an application to market data. In Diamantaras K, Uncini A, Palmieri FAN, Larsen J, editors, 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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