Estimating volatility and model parameters of stochastic volatility models with jumps using particle filter

ShinIchi Aihara, Arunabha Bagchi, S. Saha

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    3 Citations (Scopus)
    218 Downloads (Pure)

    Abstract

    Despite the success of particle filter, there are two factors which cause difficulties in its implementation. The first one is the choice of importance functions commonly used in the literature which are far from being optimal. The second one is the combined state and parameter estimation problem. In a widely used Heston model on stochastic volatility in financial literature, we are able to circumvent both these problems. To reflect the most realistic situation, we also include jump in the stochastic volatility model. Numerical results show the effectiveness of the algorithms.
    Original languageUndefined
    Title of host publication17th IFAC World Congress
    Place of PublicationSeoul
    PublisherIFAC
    Pages6490-6495
    Number of pages6
    ISBN (Print)978-3-902661-00-5
    DOIs
    Publication statusPublished - Jul 2008
    Event17th IFAC World Congress 2008 - Seoul, Korea, Republic of
    Duration: 5 Jul 200812 Jul 2008
    Conference number: 17

    Publication series

    Name
    PublisherIFAC
    Volume17, Part 1

    Conference

    Conference17th IFAC World Congress 2008
    CountryKorea, Republic of
    CitySeoul
    Period5/07/0812/07/08

    Keywords

    • EWI-13358
    • IR-64948
    • METIS-252042

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