Adaptive filtering for stochastic volatility by using exact sampling

ShinIchi Aihara; Arunabha Bagchi; S. Saha

doi:10.5220/0004454703260335

Adaptive filtering for stochastic volatility by using exact sampling

ShinIchi Aihara
, Arunabha Bagchi
, S. Saha

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

Abstract

We study the sequential identification problem for Bates stochastic volatility model, which is widely used as the model of a stock in finance. By using the exact simulation method, a particle filter for estimating stochastic volatility is constructed. The systems parameters are sequentially estimated with the aid of parallel filtering algorithm. To improve the estimation performance for unknown parameters, the new resampling procedure is proposed. Simulation studies for checking the feasibility of the developed scheme are demonstrated.

Original language	Undefined
Title of host publication	Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2013)
Publisher	SCITEPRESS
Pages	326-335
Number of pages	10
ISBN (Print)	978-989-8565-70-9
DOIs	https://doi.org/10.5220/0004454703260335
Publication status	Published - 2013
Event	10th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2013 - University of Reykjavik, Reykjavik, Iceland Duration: 29 Jul 2013 → 31 Jul 2013 Conference number: 10 http://www.icinco.org/?y=2013

Publication series

Name
Publisher	SciTePress
Volume	1

Conference

Conference	10th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2013
Abbreviated title	ICINCO 2013
Country/Territory	Iceland
City	Reykjavik
Period	29/07/13 → 31/07/13
Internet address	http://www.icinco.org/?y=2013

Keywords

EWI-24086
METIS-300213
IR-88293

Access to Document

10.5220/0004454703260335

http://eprints.eemcs.utwente.nl/secure2/24086/01/AiharaBagchiICELANDbis%5B2%5D.pdf

Cite this

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title = "Adaptive filtering for stochastic volatility by using exact sampling",

abstract = "We study the sequential identification problem for Bates stochastic volatility model, which is widely used as the model of a stock in finance. By using the exact simulation method, a particle filter for estimating stochastic volatility is constructed. The systems parameters are sequentially estimated with the aid of parallel filtering algorithm. To improve the estimation performance for unknown parameters, the new resampling procedure is proposed. Simulation studies for checking the feasibility of the developed scheme are demonstrated.",

keywords = "EWI-24086, METIS-300213, IR-88293",

author = "ShinIchi Aihara and Arunabha Bagchi and S. Saha",

note = "10.5220/0004454703260335 ; 10th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2013 ; Conference date: 29-07-2013 Through 31-07-2013",

year = "2013",

doi = "10.5220/0004454703260335",

language = "Undefined",

isbn = "978-989-8565-70-9",

publisher = "SCITEPRESS",

pages = "326--335",

booktitle = "Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2013)",

url = "http://www.icinco.org/?y=2013",

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Aihara, S, Bagchi, A & Saha, S 2013, Adaptive filtering for stochastic volatility by using exact sampling. in Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2013). SCITEPRESS, pp. 326-335, 10th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2013, Reykjavik, Iceland, 29/07/13. https://doi.org/10.5220/0004454703260335

Adaptive filtering for stochastic volatility by using exact sampling. / Aihara, ShinIchi; Bagchi, Arunabha; Saha, S.
Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2013). SCITEPRESS, 2013. p. 326-335.

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

TY - GEN

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AU - Saha, S.

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N2 - We study the sequential identification problem for Bates stochastic volatility model, which is widely used as the model of a stock in finance. By using the exact simulation method, a particle filter for estimating stochastic volatility is constructed. The systems parameters are sequentially estimated with the aid of parallel filtering algorithm. To improve the estimation performance for unknown parameters, the new resampling procedure is proposed. Simulation studies for checking the feasibility of the developed scheme are demonstrated.

AB - We study the sequential identification problem for Bates stochastic volatility model, which is widely used as the model of a stock in finance. By using the exact simulation method, a particle filter for estimating stochastic volatility is constructed. The systems parameters are sequentially estimated with the aid of parallel filtering algorithm. To improve the estimation performance for unknown parameters, the new resampling procedure is proposed. Simulation studies for checking the feasibility of the developed scheme are demonstrated.

KW - EWI-24086

KW - METIS-300213

KW - IR-88293

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DO - 10.5220/0004454703260335

M3 - Conference contribution

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BT - Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2013)

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