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Modelling Asymmetric Behaviour in Time Series: Identification Through PSO

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

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  • Claudio Pizzi

    (University Ca’ Foscari of Venezia, Department of Economics)

  • Francesca Parpinel

    (University Ca’ Foscari of Venezia, Department of Economics)

Abstract

In this work we propose an estimation procedure of a specific TAR model in which the actual regime changes depending on both the past value and the specific past regime of the series. In particular we consider a system that switches between two regimes, each of which is a linear autoregressive of order p. The switching rule, which drives the process from one regime to another one, depends on the value assumed by a delayed variable compared with only one threshold, with the peculiarity that even the thresholds change according to the regime in which the system lies at time t − d. This allows the model to take into account the possible asymmetric behaviour typical of some financial time series. The identification procedure is based on the Particle Swarm Optimization technique.

Suggested Citation

  • Claudio Pizzi & Francesca Parpinel, 2014. "Modelling Asymmetric Behaviour in Time Series: Identification Through PSO," Springer Books, in: Marco Corazza & Claudio Pizzi (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 265-276, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-02499-8_24
    DOI: 10.1007/978-3-319-02499-8_24
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