The Role of VIX In Neural Network–Based Short-Horizon Volatility Forecasting: Evidence from Global Equity Markets

Abstract

Volatility forecasts play an important role in financial markets. They are used for setting position limits, determining margin requirements, and guiding short-term trading decisions. Financial volatility is known to exhibit persistence and regime dependence, which makes short-horizon forecasting challenging. In addition to historical volatility measures, implied volatility indicators such as the Volatility Index (VIX) are often used as forward-looking proxies for market risk expectations. However, it is not clear whether the predictive usefulness of the VIX is consistent across different neural network architectures. This study investigates the role of the VIX in one-day-ahead realized volatility forecasting and examines whether its contribution varies across alternative neural network models. The empirical analysis focuses on major global equity markets, including the S&P 500, DAX, FTSE 100, Nikkei 225, and Hang Seng Index. Using market data from these indices, the study evaluates the forecasting performance of artificial neural networks (ANN), long short-term memory networks (LSTM), and gated recurrent unit (GRU) models. All models are estimated within a rolling out-of-sample framework and are tested with and without the VIX variable. Forecast accuracy is evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Quasi-Likelihood (QLIKE) loss functions, and Diebold–Mariano tests. The results show that memory-based models outperform feedforward ANN models, while the contribution of the VIX varies across models and markets.