AI-Powered Predictive Analytics for Stock Market Volatility and Risk Management
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optimization, and risk management practices such as calculating Value-at-Risk (VaR). While traditional linear and parametric models like GARCH have served the financial sector for decades, they struggle to adapt to sudden market shocks driven by complex macroeconomic and geopolitical factors. This study investigates the effectiveness of Artificial Intelligence (AI) and machine learning techniques in predicting stock market volatility, focusing on their ability to map non-linear patterns across large financial datasets. Adopting a quantitative research design utilizing historical data from the S&P 500 Index and the CBOE Volatility Index (VIX), the study compares a traditional GARCH model against a Support Vector Machine (SVM) and a Long Short-Term Memory (LSTM) deep learning network, as well as a hybrid GARCH-LSTM model. The empirical findings demonstrate that the hybrid GARCH-LSTM model outperforms standalone models, achieving the lowest Root Mean Square Error (RMSE). Furthermore, while the traditional GARCH model tended to underestimate tail risk (breaching the targeted 5% VaR at 7.2%), the hybrid framework provided the most accurate safety buffers for financial institutions. However, the study notes that AI prediction accuracy decreases as volatility increases during extreme market events. Ultimately, the paper highlights the superiority of hybrid deep learning architectures in boosting financial system resilience and providing actionable dynamic hedging insights for portfolio managers.
Keywords
Artificial Intelligence, Deep Learning, GARCH, Hybrid Models, Long Short-Term Memory (LSTM), Risk Management, S&P 500, Value-at-Risk (VaR), VIX, Volatility Forecasting