Agentic AI for Financial Volatility Regime Detection: A Hybrid Deep Learning and Statistical Framework

ABSTRACT The work presented develops an autonomous system that identifies shifts in market instability through a synergistic integration of agentic artificial intelligence with statistical and deep learning techniques. The system operates through a multi‐agent architecture that streams real‐time market data, detects changes in volatility and produces corresponding trade position recommendations. Its analytical engine applies statistical methods such as principal component analysis and clustering together with deep learning models that use attention‐based recurrent neural networks. Using performance data from 2000 to 2023, the system achieved an accuracy of 87 percent in classification and generated average yearly risk‐adjusted returns that exceeded benchmark portfolio strategies by 2.3 percent. These outcomes highlight the growing capabilities of agentic artificial intelligence in the financial sector and demonstrate its adaptive predictive strength in enhancing clarity and supporting informed decision‐making.