Exotic Derivatives and Deep Learning

2018 (English)

Student thesis

This thesis investigates the use of Artificial Neural Networks (ANNs)for calculating present values, Value-at-Risk and Expected Shortfall ofoptions, both European call options and more complex rainbow options. Theperformance of the ANN is evaluated by comparing it to a second-order Taylorpolynomial using pre-calculated sensitivities to certain risk-factors. Amultilayer perceptron approach is chosen based on previous literature andapplied to both types of options. The data is generated from a financial risk-managementsoftware for both call options and rainbow options along with the relatedTaylor approximations. The study shows that while the ANN outperforms theTaylor approximation in calculating present values and risk measures forcertain movements in the underlying risk-factors, the general conclusion isthat an ANN trained and evaluated in accordance with the method in this studydoes not outperform a Taylor approximation even if it is theoretically possiblefor the ANN to do so. The important conclusion of the study is that the ANNseems to be able to learn to calculate present values that otherwise requireMonte Carlo simulation. Thus, the study is a proof of concept that requiresfurther development for implementation.