Transformer-Based Time Series Forecasting for Stock Market Volatility Prediction

Abstract

Forecasting stock market volatility is a persistent challenge in financial systems because volatility is latent, heteroscedastic, and highly sensitive to structural breaks in market microstructure, information flow, and participant behavior. Traditional econometric approaches have provided interpretable baselines, but they often struggle with nonlinear dependencies across heterogeneous time scales and high-dimensional conditioning variables. Recent advances in deep sequence modeling, particularly transformer-based architectures, offer an alternative mechanism for learning long-range temporal dependencies and contextual interactions without the sequential constraints of recurrent models. This paper examines transformer-based time series forecasting as a system-level instrument for volatility prediction. It moves beyond isolated model accuracy to analyze architectural trade-offs, data infrastructure constraints, training and validation dynamics, deployment governance, robustness under distributional shift, fairness implications, and broader policy considerations. The discussion emphasizes that effective volatility forecasting is not solely a statistical modeling problem but an integrated socio-technical system challenge involving data provenance, computational sustainability, latency requirements, regulatory accountability, and risk management. The paper situates transformer-based predictors within the existing volatility modeling literature, compares their structural properties with conventional and recurrent approaches, and develops a framework for responsible deployment in financial institutions. The analysis suggests that while transformer models can improve representation learning for volatility dynamics, their practical value depends on careful governance, interpretability mechanisms, stress testing, and alignment with institutional risk appetite and regulatory expectations.