How Interest Rate Caps Works?

An interest rate cap (or ceiling) is an agreement between the seller or provider of the cap and a borrower to limit the borrower’s floating interest rate to a specified level for a specified period of time. Viewed in this context, an interest rate cap is simply a series of call options on a floating interest rate index, usually 3 or 6 month Libor, which coincide with the rollover dates on the borrower’s floating liabilities.

Under a usual transaction, the purchaser of the cap, in return for an up-front fee or premium, is protected against rises in interest rates on its floating rate borrowings beyond a certain nominated upper limit.

If market rates exceed the ceiling or cap rate, then the provider of the cap will make payments to the buyer sufficient enough to bring its rate back to the ceiling level. When rates are below the ceiling, no payments are made and the borrower pays market rates. The buyer of the cap therefore enjoys a fixed rate when market rates are above the cap and a floating rate when interest rates are below the cap.

The payoff of a cap is given by the following formula:

(Index Level – Strike Price) x (# Days in Period / 360) x (Nominal Amount)

For example, suppose a cap has a strike of 6% based upon 3 month Libor, a notional amount of $10,000,000 and the number of days in the period was 90.

If at reset date (day 90) the 3 month Libor rate was at 7%, then the cap provider would pay:

(7% - 6%) x (90/360) x 10,000,000 = $250,000

If at reset date (day 90) the 3 month Libor rate was at 5%, then the cap provider would make no payments.

Because the buyer pays an up-front fee, no additional obligations exist from the buyer so there is no residual credit risk.

The following diagram depicts an interest rate cap.

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Data Science in Finance: 9-Book Bundle

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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

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