MULTIDIMENSIONAL CALIBRATION OF CRUDE OIL AND REFINED PRODUCTS VIA SEMIDEFINITE PROGRAMMING TECHNIQUES

Carolina Effio Saldivar (), José Herskovits (), Juan Pablo Luna () and Claudia Sagastizábal ()
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Carolina Effio Saldivar: Mechanical Engineering Graduate Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
José Herskovits: COPPE, Federal University of Rio de Janeiro and IME, Military Institute of Engineering, Rio de Janeiro, Brazil
Juan Pablo Luna: Industrial Engineering Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
Claudia Sagastizábal: Adjunct Researcher IMECC — UNICAMP, Campinas, Brazil

International Journal of Theoretical and Applied Finance (IJTAF), 2019, vol. 22, issue 01, 1-31

Abstract: To describe the joint dynamics of prices of crude oil and refined products we extend two-factor models to a multidimensional setting. The new model captures directly the general correlation structure between the different commodities in the form of certain covariance matrix. Since the associated state-space formulation makes use of such correlations, the feasible set of the resulting estimation problem includes the cone of positive semidefinite matrices. Tractability is ensured by means of an interior point method, specially tailored for nonlinear semidefinite programming problems. For different sets of historical prices of crude oil, heating oil, and gasoline, the empirical out-of-sample forecasts obtained with the approach proposed in this work systematically provide an excellent fit to data.

Keywords: Multidimensional Schwartz–Smith model; commodity calibration; semidefinite programming; interior point methods (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0219024918500565

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