Calibration of GARCH models using concurrent accelerated random search

Julkaisun otsikon käännös: Calibration of GARCH models using concurrent accelerated random search

Juliane Müller, Juho Kanniainen, Robert Piche

    Tutkimustuotos: ArtikkeliScientificvertaisarvioitu

    103 Lataukset (Pure)

    Abstrakti

    This paper investigates a global optimization algorithm for the calibration of stochastic volatility models. Two GARCH models are considered, namely the Leverage and the Heston-Nandi model. Empirical information on option prices is used to minimize a loss function that reflects the option pricing error. It is shown that commonly used gradient based optimization procedures may not lead to a good solution and often converge to a local optimum. A concurrent approach where several optimizers (“particles”) execute an accelerated random search (ARS) procedure has been introduced to thoroughly explore the whole parameter domain. The number of particles influences the solution quality and computation time, leading to a trade-off between these two factors. In order to speed up the computation, distributed computing and variance reduction techniques are employed. Tests show that the concurrent ARS approach clearly outperforms the standard gradient based method.
    Julkaisun otsikon käännösCalibration of GARCH models using concurrent accelerated random search
    AlkuperäiskieliEnglanti
    Sivut522-534
    Sivumäärä13
    JulkaisuApplied Mathematics and Computation
    Vuosikerta221
    DOI - pysyväislinkit
    TilaJulkaistu - 2013
    OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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