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APPLICATION OF MACHINE LEARNING TO DERIVATIVE PRICING
PROJECT DETAILS
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.
POTENTIAL EMPLOYMENT OPPORTUNITIES
Front Office Quants
Risk Management Quants
Data Management Office Quant
Data Vendor Quant
Data Scientist in a Financial Institution
PROJECT DURATION & COST
Program Duration: 10 months
Upfront Cost: $0
Upon Securing Employment: $9,999
MENTORS
Dr. Dmitry Vyushin is a Director in Non-Trading Book Risk Modelling Team of Market Risk Department at the Bank of Montreal. He is currently developing a new Enterprise VaR system that covers Trading, Treasury, Insurance, and Pension portfolios. He also holds a PhD in Physics from the University of Toronto.
Dr. Markiyan Sloboda finished his PhD in Computer Science from the University of Guelph. Currently, Dr. Sloboda is working as a Vice President in BMO Capital Markets, responsible for developing equity vanilla pricing models and supporting the volatility trading desk in their everyday activities. During his tenure at TD and BMO, Dr. Sloboda was also teaching for 3 years at the Ted Rodgers Business School, Ryerson University.