Notebooks

These are the notebooks for FIN 4506 (Spring 2026). Each notebook combines economic ideas with Python implementation.

For shorter versions of these materials, see Notebook Summaries.

Notebook	Description
Introduction to Jupyter Notebooks	Quick introduction to Python notebook workflow, data download with yfinance, basic return calculations, plotting, and a first regression.
Optimization in Python	Numerical optimization tools in Python with examples relevant to finance applications.
A Market Index for Cryptocurrencies	Builds a crypto market index, estimates coin betas to the index, and compares crypto market behavior with SPY.
Minimum Variance Portfolio	Constructs and analyzes minimum-variance portfolios, including covariance estimation and risk-return implications.
Analyzing Mutual Fund Performance	Evaluates mutual fund performance with factor-style regressions and performance metrics.
Option Pricing with Neural Networks	Trains neural networks to approximate option prices and compares model predictions with observed values.
Volatility Surface Modeling with Neural Networks	Uses SPX option data to model implied volatility surfaces with machine learning and visualize fit quality.
Blockchain Foundations	Covers bytes, hashing, block structure, PoW intuition, mining pools, and security through a state-process perspective.
Proof of Work Example	Implements a minimal Proof of Work blockchain: hashing, mining, block validation, tamper checks, and chain integrity.