Abstract
The development of long-memory stochastic volatility (LMSV) models has increased the interest in the estimation of persistent processes observed with added noise. This paper investigates the performance of semi-parametric methods for estimating the long-memory-parameter in the long-range dependence plus noise case and demonstrates improvements obtained by preliminary smoothing and aggregation of the series.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
