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, Camperdown, NSW 2006, Australia

JRFM, 2017, vol. 10, issue 4, 1-16

Abstract: This paper considers a flexible class of time series 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 corresponding statistical properties of this model, discuss the spectral likelihood estimation and investigate the finite sample properties via Monte Carlo experiments. We provide empirical evidence by applying the GLMSV model to three exchange rate return series and conjecture that the results of out-of-sample forecasts adequately confirm the use of GLMSV model in certain financial applications.

Keywords: stochastic volatility; GARCH models; Gegenbauer polynomial; long memory; spectral likelihood; estimation; forecasting (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
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)
Working Paper: Estimating and forecasting generalized fractional Long memory stochastic volatility models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:10:y:2017:i:4:p:23-:d:122610

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