The course introduces students to econometric methods applied in fiancial economics. In particular, this course focuses on the estimation and inferential methods used in empirical analysis of modern finance theory. Due to the applied emphasis, classes will cover MATLAB and Stata programming to implement the tools discussed in the lectures as well.

Course Materials

• Syllabus
• Lecture notes:
  • Chapter 0: Introduction
  • Chapter 1: Primer on financial econometrics
  • Chapter 2: Stationary time series
    • Chapter 2a: Stationarity and linear process
    • Chapter 2b: Autoregressive model
    • Chapter 2c: AR model in practice
    • Chapter 2d: MA and ARMA model
  • Chapter 3: Nontationary time series

Supplementary Materials

• Moments of the Uniform Distribution
• Stationarity of an MA(1) Process
• Mean, Variance and Autocovariance of a AR(1) Process
• AR(p) Process
• AR in Practice
• ARMA
• Summary

Assignment

Please type your answers with a word processing software (e.g., Word or LaTeX) and save it as a PDF file. Please send me your answers through email.

• Assignment 1
  • Solution
• Assignment 2
  • Solution

Course Outline

• The Characteristics of Financial Market Data
  • Asset returns and compounding
  • Review of statistical distributions
  • Value-at-risk and expected shortfall
• Stationary time series
  • Efficient market hypothesis
  • Stationarity and ergodicity
  • ARMA model
  • Estimation, inference and model selection
  • Forecasting
• Nonstationary time series
  • Deterministic trends and unit root
  • Unit root tests
  • ARIMA model
  • Price bubbles

Grading

• Class participation: 10%
• Assignments: 20%
• Exam: 70%