Interest Rate Derivatives - Caps and Floors

  • Interest rate caps and floors are option like contracts, which are customized and negotiated by two parties.

  • Caps and floors are based on interest rates and have multiple settlement dates (a single data cap is a "caplet" and a single date floor is a "floorlet").

  • Like other options, the buyer will pay a premium to purchase the option, so the buyer faces credit risk.

  • Caps are also called ceilings because the buyer is protected from interest rates rising above the strike rate.

  • The payment to the option holder when rates rise above the strike rate is the difference between the market rate and the strike rate, multiplied by the notional, and divided by the number of settlements per year.

  • Floors set a minimum interest rate payment because if interest rates fall below the strike rate the floor holder is protected; payments are calculated the same as caps.

  • Floors are commonly employed by floating rate bond holders to protect their rates from falling below a certain level.

Cap Payment = Max[0; Notional × (Index rate - Cap strike rate) × (Days in settlement period / 360)]

Floor Payment = Max[0; Notional × (Floor strike rate - Index rate) × (Days in settlement period/360)]

Cap and Floor Payoffs and Interest Rate Collars

  • An interest rate collar can be created by buying a cap and selling a floor.
  • This creates an interest rate range and the collar holder is protected from rates above the cap strike rate, but has forgone the benefits of interest rates falling below the floor rate sold.
  • When the cost of the floor sold equals the cost of the cap purchased, it is called a "zero cost collar".

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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.