Energy Trading

Using MATLAB® and Simulink® products, analysts respond to changing demands and operational constraints by developing and adapting models that manage energy assets and build commodity trading strategies. From within the MATLAB environment, they can:

Model and Price Storage Assets with Greater Accuracy

Quantitative analysts model, price, and optimize portfolios of storage contracts and physical assets using prebuilt optimization algorithms in MATLAB that include a variety of solvers—constrained or unconstrained linear, nonlinear, and binary integer—and global methods, such as genetic algorithms and simulated annealing. They connect to proprietary or third-party commercial optimization routines through the MATLAB application programming interface.

Price Energy Options

Energy contracts involve managing variable amounts of gas or electricity. Constraints on how much of a commodity can be traded make customized contracts such as swing options difficult to value, risk manage, and hedge. With Monte Carlo capabilities and binomial and trinomial tree methods in MATLAB, energy traders can price contracts, incorporate constraints into risk calculations, and calculate metrics such as earnings at risk.