Combining dimensionality reduction methods with neural networks for realized volatility forecasting

The application of artificial neural networks to finance has recently received a great deal of attention from both investors and researchers, particularly as a forecasting tool. However, when dealing with a large number of predictors, these methods may overfit the data and provide poor out-of-sample forecasts. Our paper addresses this issue by employing two different approaches to predict realized volatility. On the one hand, we use a two-step procedure where several dimensionality reduction methods, such as Bayesian Model Averaging (BMA), Principal Component Analysis (PCA), and Least Absolute Shrinkage and Selection Operator (Lasso), are employed in the initial step to reduce dimensionality. The reduced samples are then combined with artificial neutral networks. On the other hand, we implement two single-step regularized neural networks that can shrink the input weights to zero and effectively handle high-dimensional data. Our findings on the volatility of different stock asset prices indicate that the reduced models outperform the compared models without regularization in terms of predictive accuracy.