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Adaptive Filtering for Stochastic Volatility by Using Exact Sampling
Tokyo University of Science, Japan.
University of Twente, Enschede, Netherlands.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
2013 (English)In: 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2013), 2013, 326-335 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2013. 326-335 p.
Keyword [en]
Particle Filter, Stochastic Volatility, Parameter Identification, Adaptive Filter
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-103985DOI: 10.5220/0004454703260335ISBN: 978-989-8565-70-9 (print)OAI: oai:DiVA.org:liu-103985DiVA: diva2:693549
Conference
10th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2013), 29-31 Juli 2013, Reykjavik, Iceland
Available from: 2014-02-04 Created: 2014-02-04 Last updated: 2014-02-13

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Saha, Saikat

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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Language
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • text
  • asciidoc
  • rtf