Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory

Manabu Asai, Shelton Peiris and Michael McAleer
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Shelton Peiris: School of Mathematics and Statistics University of Sydney, Australia.

No 2017-26, Documentos de Trabajo del ICAE from Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico

Abstract: In recent years fractionally differenced processes have received a great deal of attention due to their exibility in nancial applications with long memory. In this paper, we develop a new realized stochastic volatility (RSV) model with general Gegenbauer long memory (GGLM), which encompasses a new RSV model with seasonal long memory (SLM). The RSV model uses the information from returns and realized volatility measures simultaneously. The long memory structure of both models can describe unbounded peaks apart from the origin in the power spectrum. Forestimating the RSV-GGLM model, we suggest estimating the location parameters for the peaks of the power spectrum in the rst step, and the remaining parameters based on the Whittle likelihood in the second step. We conduct Monte Carlo experiments for investigating the nite sample properties of the estimators, with a quasi-likelihood ratio test of RSV-SLM model against theRSV-GGLM model. We apply the RSV-GGLM and RSV-SLM model to three stock market indices. The estimation and forecasting results indicate the adequacy of considering general long memory.

Keywords: Stochastic Volatility; Realized Volatility Measure; Long Memory; Gegenbauer Polynomial; Seasonality; Whittle Likelihood. (search for similar items in EconPapers)
JEL-codes: C18 C21 C58 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2017-11
New Economics Papers: this item is included in nep-ets and nep-ore
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Related works:
Journal Article: Realized stochastic volatility models with generalized Gegenbauer long memory (2020)
Working Paper: Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory (2017)
Working Paper: Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory (2017)
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