Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/244535 
Year of Publication: 
2018
Series/Report no.: 
Working Paper No. 3/2018
Publisher: 
Örebro University School of Business, Örebro
Abstract: 
In this paper we investigate the parametric inference for the linear fractional stable motion in high and low frequency setting. The symmetric linear fractional stable motion is a three-parameter family, which constitutes a natural non-Gaussian analogue of the scaled fractional Brownian motion. It is fully characterised by the scaling parameter $\sigma>0$, the self-similarity parameter $H \in (0,1)$ and the stability index $\alpha \in (0,2)$ of the driving stable motion. The parametric estimation of the model is inspired by the limit theory for stationary increments L\'evy moving average processes that has been recently studied in \cite{BLP}. More specifically, we combine (negative) power variation statistics and empirical characteristic functions to obtain consistent estimates of $(\sigma, \alpha, H)$. We present the law of large numbers and some fully feasible weak limit theorems.
Subjects: 
fractional processes
limit theorems
parametric estimation
stable motion
JEL: 
C00
C13
Document Type: 
Working Paper

Files in This Item:
File
Size
825.68 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.