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

2021/07/22 by Shang, Han Lin, Kearney, Fearghal · 2 citations
#60G25 #62M20 #Applications (stat.AP) #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Economics and business #Statistical Finance (q-fin.ST)

paper · doi:10.48550/arxiv.2107.14026

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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