On the parameter estimation in the Schwartz-Smith's two-factor model

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

The two unobservable state variables representing the short and long term factors introduced by Schwartz and Smith in [16] for risk-neutral pricing of futures contracts are modelled as two correlated Ornstein-Uhlenbeck processes. The Kalman Filter (KF) method has been implemented to estimate the "short" and "long" term factors jointly with unknown model parameters. The parameter identification problem arising within the likelihood function in the KF has been addressed by introducing an additional constraint. The obtained model parameter estimates are the Maximum Likelihood Estimators (MLEs) evaluated within the KF. Consistency of the MLEs is studied. The methodology has been tested on simulated data.
Original languageEnglish
Title of host publicationStatistics and data science
Subtitle of host publicationResearch School on Statistics and Data Science, RSSDS 2019 Melbourne, VIC, Australia, July 24–26, 2019 proceedings
EditorsHien Nguyen
Place of PublicationSingapore
PublisherSpringer, Springer Nature
Pages226-237
Number of pages12
ISBN (Electronic)9789811519604
ISBN (Print)9789811519598
DOIs
Publication statusPublished - 2019
EventResearch School on Statistics and Data Science 2019 - Melbourne, Australia
Duration: 24 Jul 201926 Jul 2019

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume1150
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

ConferenceResearch School on Statistics and Data Science 2019
Abbreviated titleRSSDS 2019
CountryAustralia
CityMelbourne
Period24/07/1926/07/19

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Keywords

  • Kalman Filter
  • Parameter estimation
  • Partially observed linear system

Cite this

Binkowski, K., He, P., Kordzakhia, N., & Shevchenko, P. (2019). On the parameter estimation in the Schwartz-Smith's two-factor model. In H. Nguyen (Ed.), Statistics and data science: Research School on Statistics and Data Science, RSSDS 2019 Melbourne, VIC, Australia, July 24–26, 2019 proceedings (pp. 226-237). (Communications in Computer and Information Science; Vol. 1150). Singapore: Springer, Springer Nature. https://doi.org/10.1007/978-981-15-1960-4_16