Bayesian Inference for the Jump-Diffusion Model with M Jumps

Maciej Kostrzewski

Communications in Statistics - Theory and Methods, 2014, vol. 43, issue 18, 3955-3985

Abstract: In this article, we propose a new class of models—jump-diffusion models with M jumps (JD(M)J). These structures generalize the discretized arithmetic Brownian motion (for logarithmic rates of return) and the Bernoulli jump-diffusion model. The aim of this article is to present Bayesian tools for estimation and comparison of JD(M)J models. Presented methodology is illustrated with two empirical studies, employing both simulated and real-world data (the S&P100 Index).

Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2012.755202 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:43:y:2014:i:18:p:3955-3985

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2012.755202

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().