The financial industry increasingly relies on large volumes of numerical data as financial products become more complex. As a result, analysts and financial engineers have turned to computational methods and numerical analysis to make sense of the financial data available to them to make investment decisions and manage financial risk.

In this hands-on course, you will be introduced to the models and theory necessary to develop your computational skills in the field of financial mathematics. Covering topics such as the Monte Carlo method, stochastic models, the binomial tree model, the theory of risk-neutral pricing, derivative pricing and the interpretation of random variables, you will learn how computational methods can be used to evaluate different financial scenarios.

During supervised programming sessions, which include an introduction to programming in Python, you will have the opportunity to implement the computational methods introduced to you using relevant examples. By the end of the course, you will be able to apply these methods to new numerical experiments based on real-world cases within the financial industry, including pricing derivatives, measuring risk, and designing an investment strategy.