Using Python to Teach Computational Finance

Understanding Delta-Hedging with the Probo Package

Tyler Brough

Beginners Business Business Cases Education Jupyter

See in schedule

In this demo-driven session, we will introduce the Probo package for teaching Python programming and concepts from computational finance to beginning programmers in the domain of finance. We'll show how Python is the perfect tool for teaching computational thinking to develop deeper quantitative reasoning. Jupyter notebooks, together with Python packages such as NumPy and Pandas, provide the ideal learning environment.

We will start by introducing the Probo package for derivative pricing and hedging. We will demo the pricing of European and American options via the famous Black-Scholes option pricing model. Other examples include Monte Carlo simulation and binomial trees. Using Probo, the answers to derivative pricing problems are right at the students' fingertips. Students can operationalize their understanding by going directly from the mathematics of derivative pricing theories to their implementation in clean and simple code.

We will end with a demonstration using Probo to teach the concept of dynamic hedging. Dynamic hedging is perhaps _the_ crucial concept in modern financial derivatives theory. It is also one of the most difficult concepts to grasp. We'll show how developing deeper intuition is possible with computational thinking via Monte Carlo simulation of delta-hedging. By leveraging the power and simplicity of Python and Jupyter notebooks, the Probo package provides the ideal learning platform for students of computational finance.

Type: Talk (45 mins); Python level: Beginner; Domain level: Beginner

Tyler Brough

Utah State University

Dr. Tyler J. Brough is an Associate Professor of Finance in the Jon M. Huntsman School of Business at Utah State University. Dr. Brough earned a PhD in Finance at the University of Arizona in 2010, an MS in Finance at the University of Illinois Urbana-Champaign in 2004, and a BS in Economics at Brigham Young University in 2000. He teaches Business Statistics to undergraduates, and Derivatives Markets, Computational Methods, and Financial Econometrics to graduate students in the Master of Science in Financial Economics and Master of Data Analytics programs. His research interests are in empirical market microstructure, applied econometrics, and computational methods.