Template-type: ReDIF-Paper 1.0
Author-Name: Shelton Peiris
Author-Workplace-Name: School of Mathematics and Statistics University of Sydney, Australia.
Author-Name: Manabu Asai
Author-Email: m-asai@soka.ac.jp
Author-Workplace-Name:Faculty of Economics Soka University, Japan.
Author-Name: Michael McAleer
Author-Email: michael.mcaleer@gmail.com
Author-Workplace-Name: Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute, Erasmus 
	School of Economics Erasmus University Rotterdam and Tinbergen Institute, The Netherlands and Department of Quantitative 
	Economics Complutense University of Madrid, Spain.
Title: Estimating and forecasting generalized fractional Long memory stochastic volatility models
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.
Classification-JEL: C18, C21, C58
Keywords: Stochastic volatility, GARCH models, Gegenbauer Polynomial, Long Memory, Spectral Likelihood, Estimation, Forecasting.
Length: 25 pages 
Creation-Date: 2016-06
Number: 2016-08
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1608.txt
File-URL: https://eprints.ucm.es/id/eprint/38110/1/1608.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1608