Fast calibration of two?factor models for energy option pricing

Energy companies need efficient procedures to perform market calibration of stochastic models for commodities. If the Black framework is chosen option pricing, bottleneck computation variance asset. commodities are commonly represented by multi-factor linear models, whose obeys a matrix Lyapunov differential equation. In this paper, analytical and numerical methods derive discussed: approach shown be more straightforward than ad-hoc derivations found in literature can readily extended higher-dimensional models. A case study presented, where two-factor mean-reverting model embedded into formulae parameters calibrated against listed options. The method compared, showing that former makes 14 times faster. Python implementation proposed available as open-source software on GitHub.