Template-Type: ReDIF-Paper 1.0
Author-Name: Asai, M.
Author-Name-Last: Asai
Author-Name-First: Manabu
Author-Person: pas73
Author-Name: McAleer, M.J.
Author-Name-Last: McAleer
Author-Name-First: Michael
Author-Person: pmc90
Author-Name: Peiris, S.
Author-Name-Last: Peiris
Author-Name-First: Shelton
Title: Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory
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.
Length: 25
Creation-Date: 2017-11-01
File-URL: https://repub.eur.nl/pub/102576/EI2017-29.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI2017-29
Classification-JEL: C18, C21, C58
Keywords: Stochastic Volatility, Realized Volatility Measure, Long Memory, Gegenbauer Poly-nomial, Seasonality, Whittle Likelihood
Handle: RePEc:ems:eureir:102576