Quantitative Methods in Finance

TL;DR

This comprehensive set of lecture notes introduces graduate students to quantitative methods in finance, emphasizing Python implementation, bridging theory and practice through examples, and covering core topics like probability, simulation, and time-series analysis.

Contribution

It offers a unified, practical toolkit for finance students and researchers, integrating theory with hands-on coding and emphasizing reproducibility and clarity.

Findings

Develops a Python-based framework for quantitative finance methods.

Provides practical examples linking theory to empirical analysis.

Emphasizes reproducibility and numerical stability in computations.

Abstract

These lecture notes provide a comprehensive introduction to Quantitative Methods in Finance (QMF), designed for graduate students in finance and economics with heterogeneous programming backgrounds. The material develops a unified toolkit combining probability theory, statistics, numerical methods, and empirical modeling, with a strong emphasis on implementation in Python. Core topics include random variables and distributions, moments and dependence, simulation and Monte Carlo methods, numerical optimization, root-finding, and time-series models commonly used in finance and macro-finance. Particular attention is paid to translating theoretical concepts into reproducible code, emphasizing vectorization, numerical stability, and interpretation of outputs. The notes progressively bridge theory and practice through worked examples and exercises covering asset pricing intuition, risk measurement, forecasting, and empirical analysis. By focusing on clarity, minimal prerequisites, and hands-on computation, these lecture notes aim to serve both as a pedagogical entry point for non-programmers and as a practical reference for applied researchers seeking transparent and replicable quantitative methods in finance.