Ruy M. Ribeiro

Teaching

Financial Econometrics

Measuring risk and return

Master'sInsperTaught in English

The econometric methods that produce the two inputs every portfolio needs: expected returns and risk. Sessions open with the expected-return toolkit: predictive regressions, the cross-section, the factor zoo, machine learning. They close with the risk toolkit: GARCH, realized volatility, large covariance matrices, tail risk. The two converge in portfolio construction under estimation error, with a live Python lab in every session.

Topics

  • Stylized facts of returns; linear time series and forecast evaluation
  • Predictive regressions, Stambaugh bias, out-of-sample discipline
  • The cross-section: portfolio sorts, Fama-MacBeth, GRS; factor zoo and multiple testing
  • Machine learning for expected returns: regularization, trees, neural nets, IPCA
  • GARCH, realized volatility and HAR, DCC correlations
  • Large covariance matrices: shrinkage and factor structure
  • Tail risk: VaR, Expected Shortfall, EVT, backtesting
  • Portfolio construction under estimation error; backtest statistics that hold up out of sample

Audience

Master's students (10×3h); graduate econometrics assumed, no prior finance required. PhD appendices serve research-bound students.

Materials

Dual-edition textbook in progress, 16 session decks, Jupyter labs on free open data (Ken French, Open Source Asset Pricing, JKP, Goyal-Welch, FRED).

Every session pairs a lecture with a live Python lab on free open data, and the course closes with a group capstone: a complete expected-return and risk pipeline, backtested with deflated Sharpe ratios, turnover, and costs included. A student-poll elective session covers high-frequency data, text and LLMs, event studies, or term-structure econometrics.

Book Preface and Table of Contents Show

Financial Econometrics: Measuring Risk and Return, master's edition (with PhD appendices). 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. The master's edition contains the full text, starred advanced sections, and a PhD appendix at the end of each chapter; starred boxes mark advanced sections that can be skipped without loss of continuity, and the PhD appendices collect proofs, asymptotic theory, and frontier references.

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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