ESTIMATION OF STAR-GARCH MODELS WITH ITERATIVELY WEIGHTED LEAST SQUARES

Murat Midilic ()
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Murat Midilic: -

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

Abstract: This study applies the Iteratively Weighted Least Squares (IWLS) algorithm to a Smooth Transition Autoregressive (STAR) model with conditional variance. Monte Carlo simulations are performed to measure the performance of the algorithm, to compare its performance with the performances of established methods in the literature, and to see the effect of initial value selection method. Simulation results show that low bias and mean squared error are received for the slope parameter estimator from the IWLS algorithm when the real value of the slope parameter is low. In an empirical illustration, STAR-GARCH model is used to forecast daily US Dollar/Australian Dollar and FTSE Small Cap index returns. 1-day ahead out-of-sample forecast results show that forecast performance of the STAR-GARCH model improves with the IWLS algorithm and the model performs better that the benchmark model.

Keywords: STAR; GARCH; iteratively weighted least squares; Australian Dollar; FTSE (search for similar items in EconPapers)
JEL-codes: C15 C51 C53 C58 C87 F31 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2016-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:rug:rugwps:16/918

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