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

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  • Han Lin Shang
  • Fearghal Kearney

Abstract

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.

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  • Han Lin Shang & Fearghal Kearney, 2021. "Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces," Papers 2107.14026, arXiv.org.
    • Handle: RePEc:arx:papers:2107.14026
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    Cited by:

    1. Vedant Choudhary & Sebastian Jaimungal & Maxime Bergeron, 2023. "FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs," Papers 2303.00859, arXiv.org, revised Dec 2023.
    2. Yang, Yang & Shang, Han Lin & Raymer, James, 2024. "Forecasting Australian fertility by age, region, and birthplace," International Journal of Forecasting, Elsevier, vol. 40(2), pages 532-548.
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    4. Shang, Han Lin & Haberman, Steven & Xu, Ruofan, 2022. "Multi-population modelling and forecasting life-table death counts," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 239-253.
    5. Shaokang Wang & Han Lin Shang & Leonie Tickle & Han Li, 2024. "Forecasting Age- and Sex-Specific Survival Functions: Application to Annuity Pricing," Risks, MDPI, vol. 12(7), pages 1-15, July.

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