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Open AccessFeature PaperArticle

Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models

by Shelton Peiris 1, Manabu Asai 2,* and Michael McAleer 3,4,5,6,7
1
School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia
2
Faculty of Economics, Soka University, Tokyo 192-8577, Japan
3
Department of Quantitative Finance, National Tsing Hua University, Hsinchu 300, Taiwan
4
Discipline of Business Analytics, University of Sydney Business School, Darlington, NSW 2006, Australia
5
Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, The Netherlands
6
Department of Quantitative Economics, Complutense University of Madrid, 28040 Madrid, Spain
7
Institute of Advanced Studies, Yokohama National University, Yokohama, Kanagawa 240-8501, Japan
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2017, 10(4), 23; https://doi.org/10.3390/jrfm10040023
Received: 30 October 2017 / Revised: 4 December 2017 / Accepted: 8 December 2017 / Published: 12 December 2017
(This article belongs to the Collection Feature Papers of JRFM)
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. View Full-Text
Keywords: stochastic volatility; GARCH models; Gegenbauer polynomial; long memory; spectral likelihood; estimation; forecasting stochastic volatility; GARCH models; Gegenbauer polynomial; long memory; spectral likelihood; estimation; forecasting
MDPI and ACS Style

Peiris, S.; Asai, M.; McAleer, M. Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models. J. Risk Financial Manag. 2017, 10, 23.

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