Financial Econometrics (FINA 4397, FINA 7354)

The goal of this class is to provide students with econometric tools and techniques to analyse and interpret financial data. Students will learn how to organize and work with financial data (cross-section, time series, and panel data) as well as analyzing finacial data sets using appropriate econometric techniques. The class also develops student’s ability to estimate various models and perform various tests using R.

Reference book
Required: Introductory Econometrics for Finance, 4th edition, by Chris Brooks. Cambridge University, 2019. The 4th Edition of the textbook is relatively new and revised to include R as the programming language. You can use the 3rd Edition of the textbook (2014) instead.
Note: The review parts of statistics and linear algebra (Part 1 in my lecture notes) will not be covered in class. I will cover parts as we move along the class.
R Note: The R programs are text files (.txt). R will run them better if you changed them to R format; in this case, just change the type from ".txt" to ".R". Not difficult. For security reasons, the server will not download "R files," that is why I have to put them in the server as .txt files.

Lecture Notes and R Programs:

Part 1: Intro to Statistics and Linear Algebra (Not covered in class)

Lecture 1 - Part 2: Review of Statistical Concepts, Returns and Data

Lecture 2 - Part 2: Intro to R and Hypothesis Testing -- Download R Programs for Lec 2

Lecture 3 - Part 2: Hypothesis Testing, Confidence Intervals (C.I.)

Lecture 4 - Part 2: C.I. & Bootstrap and Value-at-Risk (VaR) Application -- R Download Programs for Lec 4

Lecture 5 - Part 3: Regression: VaR, Introduction to Least Squares & Linear Algebra -- Download R Programs for Lec 5

Lecture 6 - Part 3: Regression: Review of Linear Algebra and LS with Linear Algebra -- Download R Programs for Lec 6

Lecture 7 - Part 3: Regression: Properties and One Parameter Hypothesis Testing -- Download R Programs for Lec 7

Lecture 8 - Part 3: Regression: CAPM, Fama-French Factors, & Goodness of Fit -- Download R Programs for Lec 8

Lecture 9 - Part 3: Regression: Maximum Likelihood Estimation and Data Problems -- Download R Programs for Lec 9

Lecture 10 - Part 4-5: Regression: Bootstrap and Testing Multiple Parameters in the CLM -- Download R Programs for Lec 10

Lecture 11 - Part 5-6: Regression: Testing Multiple Parameters in the CLM (Nested and Non-nested Tests) -- Download R Programs for Lec 11