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Implied Volatility Term Structure and Exchange Rate Predictability

Author

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  • José Renato Haas Ornelas
  • Roberto Baltieri Mauad

Abstract

This paper provides empirical evidence of the predictive power of the currency implied volatility term structure (IVTS) on exchange rate behavior from both cross-section 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 this means high future returns. In order to analyze predictability from a cross-section perspective, we build 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) those two with the highest slopes. The proposed strategy performs better than common currency strategies - carry trade, risk reversal and volatility risk premium - based on the Sharpe ratio, considering only currency returns, which supports the exchange rate predictability of the IVTS from a cross-section perspective.

Suggested Citation

  • José Renato Haas Ornelas & Roberto Baltieri Mauad, 2019. "Implied Volatility Term Structure and Exchange Rate Predictability," Working Papers Series 492, Central Bank of Brazil, Research Department.
    • Handle: RePEc:bcb:wpaper:492
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    Cited by:

    1. Matthew Greenwood-Nimmo & Daan Steenkamp & Rossouw van Jaarsveld, 2022. "CaninformationonthedistributionofZARreturnsbeusedtoimproveSARBsZARforecasts," Working Papers 11035, South African Reserve Bank.
    2. Yang, Lu, 2025. "Economic policy uncertainty and foreign exchange market implied volatility: A complex partial wavelet coherence approach," Journal of International Money and Finance, Elsevier, vol. 156(C).
    3. 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).
    4. Adam Clements & Yin Liao & Yusui Tang, 2022. "Moving beyond Volatility Index (VIX): HARnessing the term structure of implied volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 86-99, January.
    5. 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.
    6. Sangyuan Wang & Keran Li & Yaling Liu & Yijun Chen & Xianbo Tang, 2024. "VIX constant maturity futures trading strategy: A walk-forward machine learning study," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-22, April.
    7. 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.
    8. 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.
    9. 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.

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