Course Materials

Welcome to the online course materials for three modules taught at the university level. These pages are designed to complement lectures and provide a reference you can return to throughout the semester.

Each module integrates theoretical foundations with practical Python implementations, so you will find both mathematical derivations and working code throughout.

Modules¶

📈 Financial Econometrics¶

Time series analysis, stylized facts of financial returns, volatility modeling (GARCH families), and spillover analysis. Python is used throughout for estimation and visualization.

📊 Statistics for Finance¶

Probability distributions, hypothesis testing, regression models, and diagnostics — all framed around financial applications. Emphasis on building intuition alongside formal methods.

💼 Valuation¶

Fundamental valuation approaches: discounted cash flow (DCF), relative valuation using multiples, and an introduction to option-based valuation. Real case studies are used to ground each method.

How to Use These Materials¶

The materials are organized so that each chapter can be read independently, though they build on one another within each module. Code cells are included in most chapters — you can download the notebooks from the GitHub repository and run them locally.

Prerequisites¶