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Binkowski, Karol [Author]; He, Peilun [Author]; Kordzakhia, Nino [Author]; Shevchenko, Pavel V. [Author]

On the Parameter Estimation in the Schwartz-Smith’s Two-Factor Model

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  • Media type: E-Book
  • Title: On the Parameter Estimation in the Schwartz-Smith’s Two-Factor Model
  • Contributor: Binkowski, Karol [Author]; He, Peilun [Author]; Kordzakhia, Nino [Author]; Shevchenko, Pavel V. [Author]
  • Published: [S.l.]: SSRN, [2021]
  • Published in: Binkowski K., He P., Kordzakhia N., Shevchenko P. (2019) On the Parameter Estimation in the Schwartz-Smith’s Two-Factor Model. In: Nguyen H. (eds) Statistics and Data Science. RSSDS 2019. Communications in Computer and Information Science ; vol 1150. Springer, Singapore
  • Extent: 1 Online-Ressource (14 p)
  • Language: English
  • DOI: 10.2139/ssrn.3898286
  • Identifier:
  • Keywords: Kalman Filter ; parameter estimation ; partially observed linear system
  • Origination:
  • Footnote: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments 2019 erstellt
  • Description: 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 un- known model parameters. The parameter identification problem arising within the likelihood function in the KF has been addressed by introduc- ing an additional constraint. The obtained model parameter estimates are the conditional Maximum Likelihood Estimators (MLEs) evaluated within the KF. Consistency of the conditional MLEs is studied. The methodology has been tested on simulated data
  • Access State: Open Access