Tid: 8 september, 2017, kl 14.00-14.30.

Föredragshållare: Simon Carmelid

Titel: Calibrating the Hull-White model using Adjoint Algorithmic Differentiation

Abstract This thesis includes a brief introduction to Adjoint Algorithmic Differentiation (AAD), accompanied by numerical examples, step-by-step explanations and runtime comparisons to a finite difference method. In order to show the applicability of AAD in a stochastic setting, it is also applied in the calculation of the arbitrage free price and partial derivatives of a European call option, where the underlying stock is has Geometric Browninan motion dynamics. Finally the Hull-White model is calibrated using a set of zero coupon bonds, and a set of swaptions. Using AAD, the partial derivatives of the model are found and used in a Newton-Raphson method in order to find the market?s implied volatility. The end result is a Monte Carlo simulated short rate curve and its derivatives with respect to the calibration parameters, i.e. the zero coupon bond and swaption prices.

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