Model Risk in Financial Derivatives and The Transformative Impact of Deep Learning: A Systematic Review

This paper presents a comprehensive examination of model risk within the derivatives pricing context, specifically focusing on autocallable products. It identifies potential sources of model risk, encompassing insufficient model assumptions, calibration challenges, data availability limitations, and overfitting concerns. The paper explores diverse approaches for model risk mitigation in derivatives pricing, including robust model selection, rigorous model validation, risk sensitivity analysis, and model diversification. Moreover, it investigates the utilization of advanced machine learning techniques to alleviate model risk and discusses alternatives to tree methods, such as manifold learning algorithms and topological data analysis, for gaining deeper insights into intricate datasets and pricing relationships. The paper concludes with an analysis of autocallable notes, emphasizing the critical importance of accurately capturing correlations between underlying assets and their corresponding volatilities.