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Implied volatility term structure and exchange rate predictability

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  • Ornelas, José Renato Haas
  • Mauad, Roberto Baltieri

Abstract

This paper provides empirical evidence of the predictive power of the currency implied volatility term structure (IVTS) for the behavior of the exchange rate from both cross-sectional and time series perspectives. Intriguingly, the direction of the prediction is not the same for developed and emerging markets. For developed markets, a high slope means low future returns, while for emerging markets it means high future returns. We analyze predictability from a cross-sectional perspective by building portfolios based on the slope of the term structure, and thus present a new currency trading strategy. For developed (emerging) currencies, we buy (sell) the two currencies with the lowest slopes and sell (buy) the two with the highest slopes. The proposed strategy performs better than common currency strategies – carry trade, risk reversal, and volatility risk premium (VRP) – based on the Sharpe ratio, considering only currency returns, which supports the exchange rate predictability of the IVTS from a cross-sectional perspective.

Suggested Citation

  • Ornelas, José Renato Haas & Mauad, Roberto Baltieri, 2019. "Implied volatility term structure and exchange rate predictability," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1800-1813.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:4:p:1800-1813
    DOI: 10.1016/j.ijforecast.2019.03.016
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    1. Haas Ornelas, José Renato, 2019. "Expected currency returns and volatility risk premia," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 206-234.
    2. Yu, Xing & Li, Yanyan & Gong, Xue & Zhang, Nan, 2022. "Evaluating the performance of futures hedging using factors-driven realized volatility," International Review of Financial Analysis, Elsevier, vol. 84(C).
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    4. Kwas, Marek & Beckmann, Joscha & Rubaszek, Michał, 2024. "Are consensus FX forecasts valuable for investors?," International Journal of Forecasting, Elsevier, vol. 40(1), pages 268-284.
    5. Matthew Greenwood-Nimmo & Daan Steenkamp & Rossouw van Jaarsveld, 2022. "CaninformationonthedistributionofZARreturnsbeusedtoimproveSARBsZARforecasts," Working Papers 11035, South African Reserve Bank.
    6. Zuzana Rowland & George Lazaroiu & Ivana Podhorská, 2020. "Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate," Risks, MDPI, vol. 9(1), pages 1-21, December.
    7. Sobhesh Kumar Agarwalla & Jayanth R. Varma & Vineet Virmani, 2021. "Rational repricing of risk during COVID‐19: Evidence from Indian single stock options market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1498-1519, October.

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