Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/145351
Authors: 
Peiris, Shelton
Asai, Manabu
McAleer, Michael
Year of Publication: 
2016
Series/Report no.: 
Tinbergen Institute Discussion Paper 16-044/IV
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.
Subjects: 
Stochastic volatility
GARCH models
Gegenbauer Polynomial
Long Memory
Spectral Likelihood
Estimation
Forecasting
JEL: 
C18
C21
C58
Document Type: 
Working Paper

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