Financial Engineering and AI: Developing Predictive Models for Market Volatility

This paper examines financial engineering's use of AI to anticipate market volatility. To determine their efficacy, machine learning and deep learning are compared to ARCH and GARCH models. The study reviews secondary data and empirical experiments to assess AI-based model performance, strengths, and weaknesses. AI approaches outperform conventional methods in complex and turbulent markets because of their improved forecasting accuracy, adaptability, and capacity to capture non-linear market dynamics. AI models' interpretability, processing costs, and dependence on massive datasets restrict their acceptance. Policy implications underline the need for transparent, accountable, and ethical AI regulation in financial markets. The research also shows hybrid models that mix conventional and AI methods may improve volatility predictions while resolving interpretability issues. Overall, AI in financial modeling improves knowledge of market volatility and management.