1. EconStor
  2. Humboldt-Universität zu Berlin
  3. Sonderforschungsbereich 373: Quantification and Simulation of Economic Processes, Humboldt-Universität Berlin
  4. Discussion Papers, Sonderforschungsbereich 373: Quantification and Simulation of Economic Processes, Humboldt-Universität Berlin
Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/62266 
Title: 

Bayesian estimation of NIG-parameters by Markov Chain Monte Carlo Methods

Authors: 
Lillestøl, Jostein
Year of Publication: 
2000
Series/Report no.: 
SFB 373 Discussion Paper No. 2000,112
Publisher: 
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes, Berlin
Abstract: 
The Normal Inverse Gaussian (NIG) distribution recently introduced by Barndorff-Nielsen (1997) is a promising alternative for modelling financial data exhibiting skewness and fat tails. In this paper we explore the Bayesian estimation of NIG-parameters by Markov Chain Monte Carlo Methods.
Subjects: 
Normal Inverse Gaussian distribution
Bayesian Analysis
Markov Chain Monte Carlo
Persistent Identifier of the first edition: 
urn:nbn:de:kobv:11-10048384
Document Type: 
Working Paper
Appears in Collections:
Discussion Papers, Sonderforschungsbereich 373: Quantification and Simulation of Economic Processes, Humboldt-Universität Berlin

Files in This Item:
File
Size
723885109.pdf
download file
202.47 kB
Download Statistics
BibTeX-Export





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