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The problem at hand is the integration of expert forecasts for plane prices into a fully calibrated basic economy. The economy is simulated through an Economic Scenario Generator (ESG), which includes macroeconomic processes, interest rate term structures, etc.. By defining the available best-case, worst-case, and mid-case forecasts to correspond to the 95%, the 50% and the 5% quantiles of the plane price distribution, one could describe the problem with the following optimization setting: min (βc,αc,i,δc′,σc,i) {∥ (Q((IT,S) ⊙ Pc,i,T,SR (βc,αc,i,δ′c,γ′c,σc,i),q) - FM ⊙ W∥F2} The tilded matrices represent simulation results, i.e. they have the dimension timesteps T and scenarios S. The function Q(MT,S,q): RTċS × [0, 1]q → RTċq is mapping a matrix MT,S of simulated scenarios with dimension (T × S) onto each timestep's quantiles q, resulting in a matrix of dimension T × q. FM is the (T × q) matrix of expert forecasts, and W is a (T × q) weighting matrix. ∥∥F denotes the Frobenius norm, and ⊙ is the element-wise multiplication. The economy simulation is computation-intensive, for which we take benefit of using GPGPU techniques. The optimisation part is also a high-dimensional computationintensive problem, for which we use a natural computing approach using Differential Evolution.

High Performance Computing and Economic Scenario Generation: Integrating Expert Forecasts into Plane Price Modeling