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Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory

Manabu Asai, Michael McAleer and Shelton Peiris

No EI2017-29, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

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 re- alized 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 infor- mation 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. For estimating 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 Poly-nomial; Seasonality; Whittle Likelihood (search for similar items in EconPapers)
JEL-codes: C18 C21 C58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
Date: 2017-11-01
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Working Paper: Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory (2017) Downloads
Working Paper: Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory (2017) Downloads
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