Download full text
(914.2Kb)
Citation Suggestion
Please use the following Persistent Identifier (PID) to cite this document:
https://nbn-resolving.org/urn:nbn:de:0168-ssoar-201031
Exports for your reference manager
Econometric estimation in long–range dependent volatility models: theory and practice
[journal article]
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 behaviours may be fitted to a continuous-time fractional stochastic model. The estimation procedure proposed in this p... view more
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 behaviours 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.... view less
Classification
Economics
Free Keywords
continuous–time model; diffusion process; long–range dependence; stochastic volatility
Document language
English
Publication Year
2008
Page/Pages
p. 72-83
Journal
Journal of Econometrics, 147 (2008) 1
DOI
https://doi.org/10.1016/j.jeconom.2008.09.035
Status
Postprint; peer reviewed
Licence
PEER Licence Agreement (applicable only to documents from PEER project)