Research Methodology for Finance

Introduksjon

Please note that this is a preliminary course description. The final version will be published in June 2026.

This course is of great importance for your master thesis for three reasons. First, it introduces you to econometric concepts that are used in empirical finance. As such, it helps you to understand the methodology employed in published articles. Second, this course equips you with the skills to test empirical predictions based on theories from finance or economics. Third, the library session familiarizes you with information search strategies.

The course kicks off with a brief revision of regression analysis and diagnostic tests, before delving into panel regressions. Subsequently, we shift our focus to univariate and multivariate time series models. In the section about cointegration, we explore unit roots, stationarity tests, and error correction models. Lastly, GARCH models are employed to capture volatility clustering.

For every topic, there will be R codes showcasing the practical implementation of each econometric technique or concept. Usually, each R code is accompanied by a video that walks you line-by-line through the code. In the context of the mid-term exam, you need to demonstrate your coding skills by applying econometric techniques to real-world data.

Kursets innhold

Regression analysis

• Classical linear regression model (CLRM)
• CLRM assumptions and the diagnostic tests
• Limited dependent variable models
• Panel regressions

Simulation methods

• Monte Carlo simulations
• Bootstrapping

Time series modeling

• Univariate time series models
  • Moving average (MA) processes
  • Autoregressive (AR) processes
  • ARMA processes
  • Box-Jenkins methodology
  • Forecasting
• Multivariate time series analysis
  • Vector autoregressive (VAR) models
  • Granger causality tests
  • Impulse responses and variance decompositions

Cointegration and volatility modeling

• Cointegration: Modelling long-run financial behavior
  • Stationarity and unit root testing
  • Cointegration
  • Error correction models
  • Testing for cointegration
• Modeling volatility: GARCH models
  • Autoregressive conditionally heteroscedastic (ARCH) models
  • Generalized ARCH (GARCH) models
  • Maximum likelihood estimation

Information search strategies

• Search strategies
• Literature review articles
• Evaluation of sources