Deep Learning for VIX Volatility Forecasting: A Comparative Study of LSTM, BiLSTM, and CNN-LSTM Architectures
TL;DR
The CNN-LSTM model consistently outperforms both unidirectional and bidirectional LSTMs, and consistently outperforms both unidirectional and bidirectional LSTMs during periods of market turbulence, with CNN-LSTM demonstrating the strongest resilience.
Abstract
This study evaluates the predictive performance of three deep learning architectures: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and a hybrid Convolutional Neural Network-LSTM (CNN-LSTM), for forecasting the CBOE Volatility Index (VIX). Using daily VIX data from January 2000 to June 2024, we benchmark these models using mean absolute error (MAE), mean squared error (MSE), and the coefficient of determination (R2). The CNN-LSTM model consistently outperforms both unidirectional and bidirectional LSTMs, achieving the lowest MAE (1.758), MSE (9.033), and highest R2 (0.872). Contrary to expectations, the BiLSTM performs worst among the three, with an R2 of 0.670, and this suggests that bidirectional information flow may introduce noise rather than improve accuracy for volatility forecasting. The results also indicate that deep learning models maintain predictive stability during periods of market turbulence, with CNN-LSTM demonstrating the strongest resilience. These findings have practical implications for real-time risk monitoring systems in volatile financial markets.