On correlated measurement errors in the Schwartz–Smith two-factor model

Han Jun S. (), Kordzakhia Nino, Shevchenko Pavel V. and Trück Stefan
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Han Jun S.: Department of Mathematics and Statistics, Macquarie University, Macquarie Park NSW 2109, Australia
Kordzakhia Nino: Department of Mathematics and Statistics, Macquarie University, Macquarie Park NSW 2109, Australia
Shevchenko Pavel V.: Department of Actuarial Studies and Business Analytics, Macquarie University, Macquarie Park NSW 2109, Australia
Trück Stefan: Department of Actuarial Studies and Business Analytics, Macquarie University, Macquarie Park NSW 2109, Australia

Dependence Modeling, 2022, vol. 10, issue 1, 108-122

Abstract: The Schwartz–Smith two-factor model is commonly used for pricing of derivatives in commodity markets. For estimating and forecasting the term structures of futures prices, the logarithm of commodity spot price is represented as the sum of short- and long-term factors being the unobservable state variables. The futures prices derived as functions of the spot price lead to the simultaneous set of measurement equations, which is used for joint estimation of unobservable state variables and the model parameters through a filtering procedure. We propose a modified model where the error terms in the measurement equations are assumed to be serially correlated. In addition, for comparative analysis, the modelling of the logarithmic returns of futures prices is also considered. Out-of-sample prediction performances of two proposed models were illustrated using European Unit Allowances (EUA) futures prices from January 2017 to April 2021. Historically, this period corresponds to the second half of Phase III, and the beginning of Phase IV of the European Union Emission Trading System (EU-ETS).

Keywords: pricing; futures; commodity; CO2 emission allowances; Kalman filter; correlation; maximum likelihood estimation; linear state-space model (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:10:y:2022:i:1:p:108-122:n:1

DOI: 10.1515/demo-2022-0106

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