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.

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the Japan Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science, and the Australian Academy of Science, the Australian Research Council, National Science Council, Ministry of Science and Technology (MOST), Taiwan, and the Japan Society for the Promotion of Science, the Faculty of Economics at Soka University
Econometric Institute Research Papers
Econometric Intstitute

Asai, M., McAleer, M., & Peiris, S. (2017). Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory (No. EI2017-29). Econometric Institute Research Papers. Retrieved from