FIN538 Financial Econometrics

• Topics

  The goal of the course is to cover econometrics methods widely used in asset pricing and corporate finance with a particular emphasis on intuition and empirical applications.

  Topics covered:

  • Introduction to R
  • Basic Econometrics
    • Ordinary Least Squares
    • Maximum Likelihood
    • Generalized Method of Moments
  • Cross-section of stock returns (CAPM, multifactor models, Arbitrage Pricing Theory (APT))
    • Black, Jensen and Scholes (1972) approach
    • Fama MacBeth (1973) approach
    • Principal Component Analysis
  • Time series of stock returns (Efficient Market Hypothesis, volatility and correlation modelling, high-frequency data)
    • Event studies
    • Vector-autoregressions
    • ARCH/GARCH/DCC models
    • High-frequency time-series
  • Risk management
    • Value-at-Risk
    • Expected Shortfall
    • Extreme Value Theory
  • Causality
    • Panel data
    • Instrumental variables
    • Natural experiments

• Learning outcome

  After completion of the course, the student's will be able to:

  Knowledge

  • Assess the common empirical research methods in finance
  • Understand methods for testing asset pricing models
  • Understand methods used corporate finance, time series analysis and choice theory.

  Skills

  • Implement the common empirical methods in finance using R.
  • Replicate analysis in state-of-the art empirical finance papers.

  Competence

  • Will be able to conduct, interpret and critically deal with empirical studies in finance.
  • Has the tools and knowledge necessary to define, design and deliver the results of empirical investigations as done in academic articles.

• Teaching

  The course will consist of the combination of lectures and class student presentations.

• Recommended prerequisites

  Advanced master-level courses such as asset pricing, investment, corporate finance, and econometrics.

• Credit reduction due to overlap

  The course cannot be combined with ECS503 Advanced Econometrics.

• Assessment

  Class participation (20%)

  Individual presentation(s) of academic paper(s) (20%)

  Four individual assignments (60%)

• Grading Scale

  Grading scale A - F.

• Computer tools

  R/ RStudio

• Literature

  No textbook. The course is based on lecture slides and academic papers.