Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces

Hanlin Shang*, Fearghal Kearney

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper presents static and dynamic versions of univariate, multivariate, and multilevel functional time-series methods to forecast implied volatility surfaces in foreign exchange markets. We find that dynamic functional principal component analysis generally improves out-of-sample forecast accuracy. More specifically, the dynamic univariate functional time-series method shows the greatest improvement. Our models lead to multiple instances of statistically significant improvements in forecast accuracy for daily EUR-USD, EUR-GBP, and EUR-JPY implied volatility surfaces across various maturities, when benchmarked against established methods. A stylised trading strategy is also employed to demonstrate the potential economic benefits of our proposed approach.
Original languageEnglish
JournalInternational Journal of Forecasting
Publication statusAccepted/In press - 23 Jul 2021


  • Augmented common factor method
  • Functional principal component analysis
  • Long-run covariance
  • Stochastic processes
  • Univariate time-series forecasting


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