Theories of the Term Structure of Interest Rates

The shape of the yield curve has two major theories, one of which has three variations.

  • Market Segmentation Theory: Assumes that borrowers and lenders live in specific sections of the yield curve based on their need to match assets and liabilities. The theory goes further to assume that these participants do not leave their preferred maturity section. Thus, the yield curve shape is determined by supply and demand at different maturities.

    The Market Segmentation Theory could be used to explain any of the three yield curve shapes.

  • Expectations Theories (3): There are three variations of the Expectations Theory, one being "pure" and the other two "biased". All three variations share a common assumption that short term forward interest rates reflect market expectations of short term rates will be in the future.

  1. Pure Expectations Theory ("pure"): Only market expectations for future rates will consistently impact the yield curve shape. A positively shaped curve indicates that rates will increase in the future, a flat curve signals that rates are not expected to change, and an inverted yield curve points to interest rates falling in the future.

  2. Liquidity Preference Theory ("biased"): Assumes that investors prefer short term bonds to long term bonds because of the increased uncertainty associated with a longer time horizon. Therefore investors demand a liquidity premium for longer dated bonds. This theory has a natural bias toward a positively sloped yield curve.

  3. Preferred Habitat Theory ("biased"): Postulates that the shape of the yield curve reflects investor expectations of future interest rates, but rejects the notion of a liquidity preference because some investors prefer longer holding periods. The Preferred Habitat Theory relies heavily on the notion that investors will match assets and liabilities.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.