@inproceedings{64eb74c97e714b9da96202ed0b3219a7,
title = "On the parameter estimation in the Schwartz-Smith's two-factor model",
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.",
keywords = "Kalman Filter, Parameter estimation, Partially observed linear system",
author = "Karol Binkowski and Peilun He and Nino Kordzakhia and Pavel Shevchenko",
year = "2019",
doi = "10.1007/978-981-15-1960-4_16",
language = "English",
isbn = "9789811519598",
series = "Communications in Computer and Information Science",
publisher = "Springer, Springer Nature",
pages = "226--237",
editor = "Hien Nguyen",
booktitle = "Statistics and data science",
address = "United States",
note = "Research School on Statistics and Data Science 2019, RSSDS 2019 ; Conference date: 24-07-2019 Through 26-07-2019",
}