Estimating and forecasting generalized fractional Long memory stochastic volatility models

Shelton Peiris, Manabu Asai and Michael McAleer
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Shelton Peiris: School of Mathematics and Statistics University of Sydney, Australia.

No 2016-08, Documentos de Trabajo del ICAE from Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico

Abstract: In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.

Keywords: Stochastic volatility; GARCH models; Gegenbauer Polynomial; Long Memory; Spectral Likelihood; Estimation; Forecasting. (search for similar items in EconPapers)
JEL-codes: C18 C21 C58 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2016-06
New Economics Papers: this item is included in nep-ets and nep-for
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https://eprints.ucm.es/id/eprint/38110/1/1608.pdf (application/pdf)

Related works:
Journal Article: Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models (2017)
Working Paper: Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models (2016)
Working Paper: Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models (2016)
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