Ruy M. Ribeiro

Teaching

Financial Econometrics

Measuring risk and return (undergraduate)

UndergraduateInsperTaught in English

The undergraduate edition of the course: the same two toolkits, expected returns and risk, calibrated for advanced undergraduates over 13 two-hour sessions. It keeps the live Python lab in every session and the portfolio-construction capstone, with gentler pacing on the econometric theory.

Topics

  • Stylized facts of returns; linear time series and forecast evaluation
  • Predictive regressions and out-of-sample discipline
  • The cross-section: portfolio sorts, Fama-MacBeth; the factor zoo
  • Machine learning for expected returns: regularization, trees, neural nets
  • GARCH, realized volatility, dynamic correlations
  • Covariance matrices: shrinkage and factor structure
  • Tail risk: VaR, Expected Shortfall, backtesting
  • Portfolio construction under estimation error

Audience

Advanced undergraduates comfortable with regression and basic programming; no prior finance coursework required.

Materials

Undergraduate edition of the dual-edition textbook in progress, session decks, Jupyter labs on free open data.

The undergraduate and master’s editions compile from the same textbook source, so the two courses share their structure while differing in depth: starred advanced sections and PhD appendices stay in the graduate edition, and the undergraduate text adds gentler passages where the theory gets heavy. Every session still ends in a Python lab on free open data.

Book Preface and Table of Contents Show

Financial Econometrics: Measuring Risk and Return, undergraduate edition. Draft in progress.

From the preface. This book teaches the econometric methods that produce the two inputs every portfolio needs: expected returns (μ) and risk (Σ). It exists in two editions compiled from a single source; this is the undergraduate edition. Where the underlying theory requires graduate-level tools, we state results and build intuition; the master's edition contains the formal treatment.

Contents

Part I. Foundations

  1. Asset Returns, Stylized Facts, and the Portfolio Problem
  2. The Econometric Toolkit in Finance Language

Part II. Expected Returns

  1. Time-Series Predictability of Returns
  2. The Cross-Section I: Factor Models, Sorts, and Fama-MacBeth
  3. The Cross-Section II: The Factor Zoo, Multiple Testing, and Shrinkage
  4. Machine Learning for Expected Returns I: Regularization and Trees
  5. Machine Learning for Expected Returns II: Neural Networks, Latent Factors, and Costs

Part III. Risk

  1. Volatility I: The GARCH Family
  2. Volatility II: Realized Measures and Dynamic Correlations
  3. Large Covariance Matrices for Portfolios
  4. Risk Measures and Tails

Part IV. Portfolio Construction

  1. From Estimates to Portfolios

Part V. Electives

  1. High-Frequency Econometrics and Market Microstructure
  2. Text, LLMs, and Alternative Data
  3. Event Studies
  4. Bond and Term-Structure Econometrics

Appendices: Mathematics and Statistics Refresher · Python and Data Guide · Notation

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