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

Han Lin Shang and Fearghal Kearney

International Journal of Forecasting, 2022, vol. 38, issue 3, 1025-1049

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. 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.

Keywords: Augmented common factor method; Functional principal component analysis; Long-run covariance; Stochastic processes; Univariate time-series forecasting (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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Working Paper: Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:3:p:1025-1049

DOI: 10.1016/j.ijforecast.2021.07.011

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