Modeling moneyness volatility in measuring exchange rate volatility

Modeling moneyness volatility in measuring exchange rate volatility

Purpose – The purpose of this paper is to introduce a model to measure foreign exchange (FX) rate volatility accurately. The FX rate volatility forecasting is a crucial endeavour in financial markets and has gained the attention of researchers and practitioners over the last several decades. The implied volatility (IV) measure is widely believed to be the best measure of exchange rate volatility. Despite its widespread usage, the IV approach suffers from an obvious chicken-egg problem: obtaining an unbiased IV requires the options to be priced correctly and calculating option prices accurately requires an unbiased IV.

Design/methodology/approach – The authors contribute to the literature by developing a new model for FX rate volatility – the 'moneyness volatility (MV)'. This approach is based on measuring the variability of forward-looking 'moneyness' rather than use of options price. To assess volatility forecasting performance of MV against IV, the in-sample and out-of-sample tests are involved using the F-test, Granger-Newbold test and Diebold-Mariano framework.

Findings – The MV model outperforms the IV in FX rate volatility forecasting ability in both in-sample and out-of-sample tests. The F-test, Granger-Newbold test and Diebold-Mariano test results consistently reveal that MV outperforms IV in estimating as well as forecasting exchange rate volatility for six major currency options. Furthermore, in Mincer-Zarnowitz regressions, MV outperforms IV and time-series models in predicting future volatility.

Originality/value – The authors' pioneering approach in modeling exchange rate volatility has far-reaching implications for academicians, professional traders, and financial risk analysts and managers.

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