Excerpt from course description

Financial Econometrics


Financial econometrics can be understood as the application of statistical techniques to data using the statistical programming language R (which is widely used in the financial industry) to answer questions in finance. Therefore, financial econometrics can be used to test theories in finance. As such, it supports financial decision-making.

Course content

Introduction and mathematical foundations

  • Steps involved in formulating an econometric model
  • Functions
  • Matrices

Statistical foundations and dealing with data

  • Probability and probability distributions
  • Descriptive statistics
  • Types of data and data aggregation
  • Simple returns vs. log returns

Review: The capital asset pricing model (CAPM)

  • The assumptions underlying the CAPM
  • Security market line
  • Capital market line
  • Sharpe ratio, Treynor ratio, Jensen's alpha

The classical linear regression model (CLRM)

  • Simple regression
  • The assumptions underlying the CLRM
  • Properties of the OLS estimator
  • Standard errors
  • Statistical inference
  • t-statistic
  • p-value

Further development and analysis of the CLRM

  • From univariate to multivariate regression
  • Parameter estimation and standard errors in the multivariate regression framework
  • Testing multiple hypotheses: the F-test
  • R² and adjusted R²

CLRM assumptions and the diagnostic tests

  • Assumption 1: Errors have zero mean
  • Assumption 2: Errors have constant variance
  • Assumption 3: Errors are linearly independent of each other
  • Assumption 4: Errors are linearly independent of x-variables
  • Assumption 5: Errors are normally distributed
  • Multicollinearity
  • Omitted variable bias
  • Parameter stability tests


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