This project will be focusing on derivative pricing using classical financial mathematics methods as well as on novel approaches of applying machine learning (ML) techniques to valuation. Students will start from understanding market data, risk factors and derivative valuation. A simple Monte Carlo (MC) simulation engine will be built by the students at this stage. Then two popular ML techniques, Gaussian Processes and Deep Learning, will be applied to derivative pricing. Finally, a thorough analysis and comparison between ML techniques and classical methods will be conducted. The MC simulation engine built in the beginning of the project will be used to generate training data. We will focus on pricing basket options and, time permitting, on some other type of derivatives. Results of different ML approaches will be benchmarked against the market, classical financial mathematics approaches, and similar published studies. Python will be used as a main programming language. After completing the project the students will have a good understanding of financial data, MC simulations, derivatives, risk sensitivities, valuation, as well as ML and its application to derivatives pricing.
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