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

Christian Gourieroux, Hung T. Nguyen, Songsak Sriboonchitta

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)203-219
Number of pages17
JournalAnnals of Operations Research
Volume256
Issue number2
DOIs
Publication statusPublished - 1 Sept 2017
Externally publishedYes

Keywords

  • Diffusion model
  • Local time
  • Low frequency data
  • Nonlinear canonical analysis
  • Prediction operator

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