Lecture notes and slides from courses I have taught in asset pricing, derivatives, investments, financial technology, and stochastic foundations, to undergraduates, graduate students, and MBAs at WashU Olin , Miami Herbert and ESSEC Business School . My first course website grew out of QuantEcon, the open lectures started by Tom Sargent and John Stachurski: seeing economics taught with their Python code made me want to teach finance the same way. Tom was my macroeconomics professor when I began my PhD in finance at NYU. Much of the material predates AI tools, and I have since revised and expanded it with AI assistance, as the AI assistance disclosure explains. Materials are open for anyone to use. Everything renders in HTML for cross-device consistency; I am still adding content from past courses such as introduction to finance, international finance, and trading and markets.
Recently Added
New
From Papers to Portfolios
Notebooks
Python notebooks that take ideas from recent empirical asset pricing research and turn them into portfolios, with all the code, data and theory shown and realistic constraints imposed. The first notebooks estimate minimum-variance portfolios of U.S. stocks from CRSP data and study when the sample covariance matrix can be trusted. I will add to them over time.
New
Numerical Applications
Notebooks
Python notebooks that implement ideas from finance end to end: the Critical Line Algorithm for constrained frontiers, the exact value function for recursive utility, and calibration of the Heston model to option prices. Several of them are companions to the Asset Pricing notes, and I will add to them over time.
Course Materials
Updated Oct 19
Asset Pricing
Notes Slides
Foundations of asset pricing theory, stochastic discount factors in discrete and continuous time, continuous-time asset pricing, and commodity pricing. I am still adding to and refining this section.
Used in Topics in Quantitative Finance (FIN 5018) at WashU.
Updated Oct 18
Investments
Notes Slides
Portfolio theory, fixed income, and market microstructure for use across investment courses. I taught the Investments (FIN 320) course at the University of Miami to undergraduates for many years.
Used in Investments (FIN 4410) and Investment Theory (FIN 5320) at WashU.
Updated Oct 15
Derivatives
Notes Slides
Forwards, futures, options, binomial pricing, and Black-Scholes applications. I started working on these notes when teaching similar courses at the University of Miami for undergraduate, graduate, and MBA students.
Used in Options and Futures (FIN 5240), Derivative Securities (FIN 5241), and Options, Futures and Derivative Securities (FIN 4510) at WashU.
Updated Oct 9
Stochastic Foundations for Finance
Notes
Probability, random walks, Brownian motion, stochastic calculus, and continuous-time asset pricing.
Used in Stochastic Foundations for Finance (FIN 5380) at WashU, required for students in the Quantitative Finance and Wealth and Asset Management master’s programs.
Data Analysis for Investments
Notebooks
Python notebooks covering financial data acquisition, return computation, factor models, portfolio optimization, and time-series analysis.
Used in Data Analysis for Investments (FIN 6533) at WashU, which I typically teach online during Summer sessions for graduate students in finance.
Financial Technology
Notebooks
Python notebooks covering optimization, data science, option pricing, blockchain, and stablecoins. Some of these materials were developed when I taught a similar course at the University of Miami for graduate students.
Used in Financial Technology: Methods and Practice (FIN 4506/5506) at WashU, an elective for both undergraduate and graduate students.