Realized volatility forecasting using hybrid neural networks : an application for The Istanbul Stock Exchange

Abstract

Volatility forecasting in the financial markets is important in the areas of risk management and asset pricing, among others. In this study, BIST 100’s 1-day, 5-day, and 10-day-ahead return volatilities are examined. Two types of hybrid models are utilized to improve individual GARCH-family models’ predictions. For the first hybrid model, a group of GARCH- family models is constructed to produce volatility estimates which were then fed into neural network to increase the predictive power. The second hybrid model received GARCH- family models’ specifications instead of volatility estimates as inputs for ANN to conduct the learning process. Hybrid neural networks were also fed a set of exogenous, endogenous, and dummy variables. One of the main conclusions is that both hybrid models increased the forecasting precision of individual GARCH-family models while the second hybrid model provided better volatility forecasts for all error measures used in this study. Equal forecast accuracy test also showed that the hybrid models’ out-of-sample predictions were significantly better than GARCH-family methods. All model performances deteriorated as forecast horizon was extended, although the steepest decline happened for hybrid models rather than the GARCH-family. Lastly, as the complexity of the neural network architecture was increased, the loss measures for the out-of- sample forecasts improved except on the last case where the network overfitted using the highest number of neurons per hidden layer among the searched hyperparameter grid.