Textbook and Reading Materials

Required Textbook

Introductory Econometrics for Finance by Chris Brooks.

Supporting Material

The following are not required, but suggested for the serious student, especially if you are considering graduate school. I will present some material based on them.

Bayesian Econometrics by Gary Koop.

Bayesian Reasoning and Machine Learning by David Barber.

Machine Learning: a Probabilistic Perspective by Kevin P. Murphy.

Weekly Readings

Students will be assigned one or more papers to be read during the week prior to the lecture in which they will be discussed.

Additional References

The following are not required, but I may present material based on them. I list them for thoroughness and to provide a suggested reading list for the serious student.

Python

Python for Data Analysis by Wes McKinney

Numerical Python by Robert Johansson.

Econometrics and Statistics

Causal Inference in Statistics: A Primer by Pearl, Glymour, and Jewell.

The Econometrics of Financial Markets by Campbell, Lo, MacKinlay.

Analysis of Integrated and Cointegrated Time Series with R by Bernhard Pfaff.

Numerical Methods of Statistics by John F. Monahan.