Threshold selection in jump-discriminant filter for discretely observed jump processes

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Threshold estimation is one of the useful techniques in the inference for jump-type stochastic processes from discrete observations. In this method, a jump-discriminant filter is used to infer the continuous part and the jump part separately. Although there are several choices for the filter, statistics constructed via filters are often sensitive to the choice. This paper presents some numerical procedures for selecting a suitable filter based on observations.

Original languageEnglish
Pages (from-to)355-378
Number of pages24
JournalStatistical Methods and Applications
Volume19
Issue number3
DOIs
Publication statusPublished - 2010
Externally publishedYes

Fingerprint

Jump Process
Discriminant
Jump
Filter
Discrete Observations
Numerical Procedure
Stochastic Processes
Statistics
Jump process

Keywords

  • Asymptotic unbiasedness
  • Integrated-volatility
  • Jump-discriminant filter
  • Plug-in method
  • Threshold estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Threshold selection in jump-discriminant filter for discretely observed jump processes. / Shimizu, Yasutaka.

In: Statistical Methods and Applications, Vol. 19, No. 3, 2010, p. 355-378.

Research output: Contribution to journalArticle

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