• Graduate Programs
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Summer School
      • Applied Public Policy Evaluation
      • Deep Learning
      • Development Economics
      • Economics of Blockchain and Digital Currencies
      • Economics of Climate Change
      • The Economics of Crime
      • Foundations of Machine Learning with Applications in Python
      • From Preference to Choice: The Economic Theory of Decision-Making
      • Inequalities in Health and Healthcare
      • Marketing Research with Purpose
      • Markets with Frictions
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Business Data Science Summer School Program
    • Tinbergen Institute Lectures
    • 2026 Tinbergen Institute Opening Conference
    • Annual Tinbergen Institute Conference
  • News
  • Summer School
    • Applied Public Policy Evaluation
    • Deep Learning
    • Development Economics
    • Economics of Blockchain and Digital Currencies
    • Economics of Climate Change
    • The Economics of Crime
    • Foundations of Machine Learning with Applications in Python
    • From Preference to Choice: The Economic Theory of Decision-Making
    • Inequalities in Health and Healthcare
    • Marketing Research with Purpose
    • Markets with Frictions
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
    • Tuition Fees and Payment
  • Alumni

Ellen, S.T. and Zwinkels, R.C.J. (2010). Oil price dynamics: A behavioral finance approach with heterogeneous agents Energy Economics, 32(6):1427--1434.


  • Journal
    Energy Economics

In this paper, we develop and test a heterogeneous agent model for the oil market. The demand for oil is divided in a speculative component and a real component. Speculators are boundedly rational in forming price expectations. Expectations are formed by one of two boundedly rational rules of thumb: fundamentalist and chartist. While fundamentalists trade on mean-reversion, chartists follow the trend in prices. Speculators then choose between these rules based on past profitability. Estimation results on Brent and WTI oil reveal that both groups are active in the oil market, and that speculators often switch between the groups. The model outperforms both the random walk and VAR models in out-of-sample forecasting.