Machine Learning Approaches for Predicting Stock Market Volatility in Emerging Markets
Abstract
Predicting stock market volatility in emerging markets represents a critical challenge for global financial stability, portfolio management, and systemic risk mitigation. Traditional econometric models often fail to capture the highly nonlinear, non-stationary, and regime-shifting characteristics inherent in these developing financial ecosystems. This paper provides a comprehensive, system-level investigation into the deployment of machine learning architectures for volatility forecasting in emerging economies. We explore the architectural trade-offs, infrastructure requirements, and structural vulnerabilities associated with transitioning from classical statistical frameworks to advanced computational paradigms, including deep recurrent networks, ensemble methods, and hybrid neural-econometric systems. Beyond algorithmic performance, this study emphasizes the socio-technical dimensions of algorithmic deployment, analyzing the critical infrastructure needed to handle asynchronous data streams, fragmented regulatory reporting, and low-liquidity regimes. We examine the structural trade-offs between model interpretability and predictive capacity, highlighting how opaque deep learning systems can inadvertently amplify systemic risks during periods of market stress. Furthermore, the paper addresses governance challenges, data fairness, and the policy implications of widespread algorithmic trading in environments characterized by weak institutional safeguards and high susceptibility to capital flight. By evaluating these systems through an interdisciplinary lens that unites computer science, financial economics, and public policy, we outline a robust, sustainable framework for integrating machine learning into the regulatory and operational fabrics of emerging financial markets.
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