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Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces
Han Lin Shang
Fearghal Kearney
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.
Han Lin Shang & Fearghal Kearney, 2021. "Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces," Papers 2107.14026, arXiv.org.
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NEP fields
This paper has been announced in the following NEP Reports:
NEP-ETS-2021-08-16 (Econometric Time Series)
NEP-FOR-2021-08-16 (Forecasting)
NEP-ISF-2021-08-16 (Islamic Finance)
NEP-RMG-2021-08-16 (Risk Management)
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