Dynamic Volatility Forecasting in Financial Markets Using CNN Long-Short Term Memory Networks (LSTM)

Volatility is a critical factor in market risk which is influenced by various economic events and market conditions. In this study, we propose a novel deep learning model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for dynamic volatility forecasting in financial markets. The model employs the feature extraction capability of CNNs with the sequential learning ability of LSTMs to capture temporal dependencies in market data. Financial time-series data were collected and robustness testing was performed under varying market conditions, including high-volatility events such as financial crises and low-volatility periods. The model was trained over 100 epochs, and its performance was assessed using metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Directional Accuracy (DA). The CNN-LSTM architecture demonstrated strong performance, with MAPE of 0.4206, RMSE of 11.4084, and perfect directional accuracy (100%). Grad-CAM was employed for interpreting CNN layers and to analyzed attention mechanisms to extract temporal insights. The robustness of the model was validated across different market conditions ensuring its reliability. Furthermore, we explored the practical implications of the model, particularly in risk management, portfolio optimization, and market regulation. The study highlights the importance of this model for institutional investors, traders, and policymakers who seek to make informed decisions in volatile markets.