arXiv 19 Jan 2026 · Econometrics
arXiv:2601.12896 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Marno Verbeek (2017) A Guide to Modern Econometrics | 1.000 | 22 | 8 | 100% |
| 2 | Alexander, Carol (2008) Market Risk Analysis, Volume II, Practical Financial Econometrics | 1.000 | 5 | 3 | 100% |
| 3 | Nelson, Daniel B (1991) Conditional Heteroskedasticity in Asset Returns: A New Approach | 0.874 | 7 | 2 | 100% |
| 4 | Blundell, Richard and Duncan, Alan (1998) Kernel Regression in Empirical Microeconomics | 0.874 | 5 | 2 | 100% |
| 5 | Kousky, Carolyn and Cooke, Roger (2012) Explaining the Failure to Insure Catastrophic Risks | 0.874 | 5 | 2 | 100% |
| 6 | James G. MacKinnon (2010) Critical Values for Cointegration Tests | 0.874 | 5 | 2 | 100% |
| 7 | Rolski, Tomasz and Schmidli, Hanspeter and Schmidt, Volker and Teuge… (1999) Stochastic processes for insurance and finance | 0.874 | 5 | 2 | 100% |
| 8 | Roth, Jonathan and Sant’Anna, Pedro HC and Bilinski, Alyssa and Poe,… (2023) What’s trending in difference-in-differences? A synthesis of the recent econometrics literature | 0.874 | 5 | 2 | 100% |
| 9 | Jondeau, Eric and Poon, Ser-Huang and Rockinger, Michael (2007) Financial Modeling Under Non-Gaussian Distributions | 0.811 | 4 | 2 | 100% |
| 10 | Adrian, Tobias and Boyarchenko, Nina and Giannone, Domenico (2019) Vulnerable Growth | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 350 scored citations.