Measuring Currency Exposure

  • Currency exposures measure, in the investor's domestic currency, an asset return's sensitivity to returns on the ith/LC exchange rates.
  • Currency exposure risk must be captured this way when the ICAPM is used to determine the domestic currency returns for a domestic investor purchasing a foreign asset.
  • For example, if a Canadian investor wants to determine the required Canadian dollar (CAD) return for the common stock shares of U.S. auto maker Ford (NYSE: F) using ICAPM, then he/she needs to know how sensitive the Canadian dollar returns on the Ford shares might be to changes against the value of the US dollar (USD) against all world currencies, including the CAD.
  • Currency exposures are calculated by regressing the foreign risky asset's return as measured in the investor's domestic currency against the percentage change in the value of the foreign asset's local currency against currencies 1 through k, for the ith currency.

Correlations Between Asset Returns and Exchange Rate Movements

  • Zero Correlation: A foreign risky asset's price will have no systemic reaction to a change in the Investor Domestic Currency/Asset's Local Currency exchange rate.

  • Positive Correlation: A foreign risky asset's local currency price will change in the same direction as any change in the DC/LC exchange rate.

  • Negative Correlation: A foreign risky asset's local currency price will change in the opposite direction as any change in the DC/LC exchange rate.

  • Importance of Real Exchange Rates:

  • Only changes in the real exchange rates will produce real changes in asset returns.

  • When changes in nominal exchange rates only reflect inflation rate differences between countries, reported nominal asset returns will only reflect inflation rate difference between the local country of the foreign asset and the investor's domestic country.

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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.