Reimagining Market Volatility: Integrating Deep Learning and Adaptive Strategy Design for Indian Stock Market

Capital market or financial market volatility is pervasive and complex. It is the pulse of the market that reflects balance between opportunity and uncertainty. In the fast-growing Indian stock market, accurately modelling and predicting this volatility is vital for achieving superior performance. Traditional econometric models like ARCH and GARCH have offered fundamental insights but fail to explain the nonlinear and regime shifting patterns of volatility. This paper introduces AI Volatility Adaption Cycle (AIVAC) i.e., a conceptual framework that integrates volatility theory, complexity economics and artificial intelligence to build adaptive trading strategies. The AIVAC blends econometric reasoning with neural and reinforcement learning to form a self-evolving intelligent framework. The proposed framework has five layers namely, data fusion and conceptualization, feature transformation, predictive intelligence and regime detection, adaptive strategy engine and feedback driven evolutionary learning. This will allow the users to automatically adjust their settings like risk levels and trading methods on real time basis. This paper contributes to framing a practically applicable adaptive intelligent framework fit for Indian market. The paper also discusses the implications for the stakeholders while emphasizing transparency, interpretability and ethical AI adoption.