Forecasting Implied Volatility in Foreign Exchange Markets: A Functional Times Series Approach

The European Journal of Finance, Forthcoming

31 Pages Posted: 2 Apr 2015 Last revised: 21 Dec 2016

See all articles by Fearghal Kearney

Fearghal Kearney

Queen's Management School

Mark Cummins

Dublin City University Business School

Finbarr Murphy

University of Limerick - Kemmy Business School

Date Written: February 23, 2016

Abstract

We utilise functional time series (FTS) techniques to characterise and forecast implied volatility in foreign exchange markets. In particular, we examine the daily implied volatility curves of FX options, namely; EUR-USD, EUR-GBP, and EUR-JPY. Based on existing techniques in the literature, the FTS model is shown to produce both realistic and plausible implied volatility shapes that closely match empirical data during the volatile 2006-2013 period. Furthermore, the FTS model significantly outperforms implied volatility forecasts produced by traditionally employed parametric models. The evaluation is performed under both an in-sample and out-of-sample testing framework with our findings shown to be robust across various currencies, moneyness segments, contract maturities, forecasting horizons, and out-of-sample window lengths. The economic signicance of the results is highlighted through the implementation of a simple trading strategy.

Suggested Citation

Kearney, Fearghal Joseph and Cummins, Mark and Murphy, Finbarr, Forecasting Implied Volatility in Foreign Exchange Markets: A Functional Times Series Approach (February 23, 2016). The European Journal of Finance, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2587787 or http://dx.doi.org/10.2139/ssrn.2587787

Fearghal Joseph Kearney (Contact Author)

Queen's Management School ( email )

Riddel Hall
Belfast, Northern Ireland BT9 5EE
Northern Ireland

Mark Cummins

Dublin City University Business School ( email )

Dublin 9
Ireland

Finbarr Murphy

University of Limerick - Kemmy Business School ( email )

Limerick
Ireland

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