Casas, Isabel and Gao, Jiti (2006): Econometric estimation in longrange dependent volatility models: Theory and practice. Published in: Journal of Econometrics , Vol. 147, No. 1 (November 2008): pp. 7283.

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Abstract
It is commonly accepted that some financial data may exhibit longrange dependence, while other financial data exhibit intermediaterange dependence or shortrange dependence. These behaviors may be fitted to a continuoustime fractional stochastic model. The estimation procedure proposed in this paper is based on a continuoustime version of the Gauss–Whittle objective function to find the parameter estimates that minimize the discrepancy between the spectral density and the data periodogram. As a special case, the proposed estimation procedure is applied to a class of fractional stochastic volatility models to estimate the drift, standard deviation and memory parameters of the volatility process under consideration. As an application, the volatility of the Dow Jones, S&P 500, CAC 40, DAX 30, FTSE 100 and NIKKEI 225 is estimated.
Item Type:  MPRA Paper 

Original Title:  Econometric estimation in longrange dependent volatility models: Theory and practice 
Language:  English 
Keywords:  Continuoustime model; diffusion process; longrange dependence; stochastic volatility 
Subjects:  C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C46  Specific Distributions ; Specific Statistics 
Item ID:  11981 
Depositing User:  jiti Gao 
Date Deposited:  09. Dec 2008 00:17 
Last Modified:  11. Feb 2013 20:58 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/11981 