EconPapers    
Economics at your fingertips  
 

Nonparametric estimation of a scalar diffusion model from discrete time data: a survey

Christian Gourieroux, Hung T. Nguyen () and Songsak Sriboonchitta ()
Additional contact information
Hung T. Nguyen: New Mexico State University
Songsak Sriboonchitta: Chiang Mai University

Annals of Operations Research, 2017, vol. 256, issue 2, 203-219

Abstract: Abstract In view of rapid developments on nonparametric estimation of the drift and volatility functions in scalar diffusion models in financial econometrics, from discrete-time observations, we provide, in this paper, a survey of its state-of-the-art with new insights into current practices, as well as elaborating on our own recent contributions. In particular, in presenting the main principles of estimation for both stationary and nonstationary cases, we show the possibility to estimate nonparametrically the drift and volatility functions without distinguishing these two frameworks.

Keywords: Diffusion model; Local time; Low frequency data; Nonlinear canonical analysis; Prediction operator (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s10479-016-2273-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:annopr:v:256:y:2017:i:2:d:10.1007_s10479-016-2273-6

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla ().

 
Page updated 2019-11-06
Handle: RePEc:spr:annopr:v:256:y:2017:i:2:d:10.1007_s10479-016-2273-6