Uncovered and Covered Interest Rate Parity Relationship

Uncovered interest rate parity assumes that the nominal risk free rates of two economies determine the expected future spot exchange rate, when applied to the current spot exchange rate.

E(S 1 X/Y) = S 0 X/Y (1+ r f X)/( 1+ r f Y)

Covered interest rate parity concludes that the market’s forward exchange rate contract should always be tied to the periodic nominal risk free interest rate difference between the two countries.

F X/Y = S X/Y (1+ r f X)/( 1+ r f Y)

If the analyst feels the forward rate has significantly deviated from its value predicted by the relationship between risk free rates, then an arbitrage currency trade opportunity may be present.

If the forward contract is overvalued, then the trader will purchase the currency in the spot market and sell short the forward.  If the forward contract is undervalued, then trader will buy the forward contract and sell short the spot currency.

Analysts should not be fooled into believing that a higher nominal interest rate implies a higher real return, as currency exchange rates offset this difference.

Forward contracts can be used to hedge the uncertainty surrounding the future value of a transaction denominated in a foreign currency.  For example, a US company set to receive a £1 million payment in three months could sell short a forward contract for British pounds of equal value to lock in a fixed exchange rate.  In that time, the pound could appreciate or depreciate against the US dollar, but that volatility has been eliminated through the forward contract.

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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.