Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/46473
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | Modelling long-range-dependent Gaussian processes with application in continuous-time financial models |
Author: | Gao, J. |
Citation: | Journal of Applied Probability, 2004; 41(2):467-482 |
Publisher: | Applied Probability Trust |
Issue Date: | 2004 |
ISSN: | 0021-9002 1475-6072 |
Abstract: | <jats:p>This paper considers a class of continuous-time long-range-dependent Gaussian processes. The corresponding spectral density is assumed to have a general and flexible form, which covers some important and special cases. For example, the spectral density of a continuous-time fractional stochastic differential equation is included. A modelling procedure is then established through estimating the parameters involved in the spectral density by using an extended continuous-time version of the Gauss–Whittle objective function. The resulting estimates are shown to be strongly consistent and asymptotically normal. An application of the modelling procedure to the identification and modelling of a fractional stochastic volatility is discussed in some detail.</jats:p> |
Keywords: | Continuous-time model diffusion process long-range dependence parameter estimation stochastic volatility |
Description: | 2004 © Applied Probability Trust |
DOI: | 10.1239/jap/1082999079 |
Description (link): | http://projecteuclid.org/euclid.jap/1082999079 |
Published version: | http://dx.doi.org/10.1239/jap/1082999079 |
Appears in Collections: | Aurora harvest Economics publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.