Modelling Asymmetric Behaviour in Time Series: Identification Through PSO

Claudio Pizzi () and Francesca Parpinel ()
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Claudio Pizzi: University Ca’ Foscari of Venezia, Department of Economics
Francesca Parpinel: University Ca’ Foscari of Venezia, Department of Economics

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2014, pp 265-276 from Springer

Abstract: 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.

Keywords: Particle Swarm Optimization; Particle Swarm Optimization Algorithm; Financial Time Series; Asymmetric Behaviour; Bear Market (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-02499-8_24

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DOI: 10.1007/978-3-319-02499-8_24

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