Modeling Financial Options Using Data Science and Computational Methods

Publication Date

Spring 5-5-2026

College

College of Sciences & Mathematics

Department

Math and Computer Science, Department of

Student Level

Undergraduate

Faculty Mentor

Dr. Davis

Presentation Type

Article

Summary

Abstract

Financial instruments such as stock options are often valued using mathematical models. These models help investors manage risk, evaluate opportunities, and make more informed decisions under uncertainty. This project explores how computational data science methods can be used to model and analyze option pricing with real market data.

Using publicly available financial data, the project implements two widely used pricing methods: the discrete-time binomial tree model and the continuous-time Black-Scholes model. The binomial model simulates possible future stock price movements over time, while the Black-Scholes model provides a theoretical benchmark for comparison. By combining real-world data with these approaches, the project examines how well numerical methods approximate established financial theory.

The analysis focuses on three key areas: model accuracy, convergence behavior, and the impact of volatility estimation. Results show that as the number of simulated time steps increases, the binomial model converges toward the Black-Scholes price, demonstrating the strength of numerical approximation in financial modeling.

More broadly, this project shows how data science can serve as a bridge between theoretical mathematics and real-world financial decision-making. It also highlights the importance of reproducibility, model validation, and data-driven analysis in modern quantitative finance. In addition, the project explores how these pricing models can be integrated into a real-time dashboard, allowing users to visualize market data and model outputs remotely through a secure private network.

This document is currently not available here.

Share

COinS