On parameter estimation of stochastic volatility models from stock data using particle filter - Application to AEX index -

ShinIchi Aihara, Arunabha Bagchi, S. Saha

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    Abstract

    We consider the problem of estimating stochastic volatility from stock data. The estimation of the volatility process of the Heston model is not in the usual framework of the filtering theory. Discretizing the continuous Heston model to the discrete-time one, we can derive the exact volatility filter and realize this filter with the aid of particle filter algorithm. In this paper, we derive the optimal importance function and construct the particle filter algorithm for the discrete-time Heston model. The parameters contained in system model are also estimated by constructing the augmented states for the system and parameters. The developed method is applied to the real data (AEX index).
    Original languageUndefined
    Pages (from-to)17-27
    Number of pages11
    JournalInternational journal of innovative computing, information and control
    Volume5
    Issue number1
    Publication statusPublished - Jan 2009

    Keywords

    • IR-68223
    • METIS-264076
    • Particle filter
    • Stochastic volatility
    • Parameter estimation
    • Heston model
    • MSC-11K45
    • EWI-16187

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