Econometric estimation in long-range dependent volatility models: Theory and practice

Isabel Casas () and Jiti Gao

MPRA Paper from University Library of Munich, Germany

Abstract: It is commonly accepted that some financial data may exhibit long-range dependence, while other financial data exhibit intermediate-range dependence or short-range dependence. These behaviors may be fitted to a continuous-time fractional stochastic model. The estimation procedure proposed in this paper is based on a continuous-time 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.

Keywords: Continuous-time model; diffusion process; long-range dependence; stochastic volatility (search for similar items in EconPapers)
JEL-codes: C46 (search for similar items in EconPapers)
Date: 2006-10, Revised 2007-08
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Citations: View citations in EconPapers (1)

Published in Journal of Econometrics 1.147(2008): pp. 72-83

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Journal Article: Econometric estimation in long-range dependent volatility models: Theory and practice (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:11981

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