Forecasting realized volatility with neural networks - Stránský, Lukáš (2025)
This thesis evaluates the forecasting performance of traditional and neural network models for realized volatility across five major currency pairs. Linear HAR-family models are compared with nonlinear neural network architectures, feedforward neural networks (FNNs) and long short-term memory (LSTM) networks using both standard and relative RMSE as performance metrics. The models incorporate a rich set of features, including realized quarticity, jump variation, and semivariances aggregated over multiple time horizons. Results show that neural networks consistently achieved the best in-sample performance and outperformed linear models in 3 out of 5 currencies on the test set. The LSTM with a 22-day lookback was most effective for high-volatility currencies like AUD/USD, while the FNN with comprehensive inputs performed best for more stable series like EUR/USD. However, OLS models remained highly competitive, particularly for USD/JPY and USD/CHF. These findings highlight the importance of aligning model complexity with data characteristics, showing that while neural networks excel in high-volatility settings, well-specified linear models remain competitive in more stable environments.