Forecasting implied volatilities of currency options with machine learning techniques and econometrics models

Abstract

Developing an effective modeling framework to minimize foreign exchange (FX) risk is of vital importance for hedgers and traders in FX markets. In this study, we compare the ability of long short-term memory (LSTM) models to that of random forest and several time series models for forecasting EURUSD implied volatility across the volatility surface. As our literature study argues, there are only a few published papers on this subject. We find that the LSTM model is the best model for shorter option maturities, while the AR-GARCH model is superior when the maturities increase. We observe that the LSTM model is able to capture immense and immediate changes in implied volatility, which is important for hedging against significant shifts in FX rates.