The two unobservable state variables representing the short and long term factors introduced by Schwartz and Smith in  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.
|Name||Communications in Computer and Information Science|
|Conference||Research School on Statistics and Data Science 2019|
|Abbreviated title||RSSDS 2019|
|Period||24/07/19 → 26/07/19|
- Kalman Filter
- Parameter estimation
- Partially observed linear system