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Estimating and forecasting generalized fractional Long memory stochastic volatility models

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Peiris, Shelton y Asai, Manabu y McAleer, Michael (2016) Estimating and forecasting generalized fractional Long memory stochastic volatility models. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 08, 2016, ISSN: 2341-2356 ]

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Resumen

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.


Tipo de documento:Documento de trabajo o Informe técnico
Palabras clave:Stochastic volatility, GARCH models, Gegenbauer Polynomial, Long Memory, Spectral Likelihood, Estimation, Forecasting.
Materias:Ciencias > Estadística > Probabilidades
Ciencias Sociales > Economía > Econometría
JEL:C18, C21, C58
Título de serie o colección:Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)
Volumen:2016
Número:08
Código ID:38110
Depositado:14 Jun 2016 11:59
Última Modificación:14 Jun 2016 11:59

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