Machine Learning and AI in Derivatives Pricing and Risk Management: Enhancing Accuracy and Speed - Investigate the Application of ML Algorithms to Predict Market Volatility, Calibrate Complex Pricing Models, and Optimize Hedging Strategies

Nikhil Jarunde

Abstract: The increasing complexity and dynamism of financial markets have necessitated the adoption of advanced computational techniques in derivatives pricing and risk management. Machine learning (ML) and artificial intelligence (AI) methodologies offer promising avenues to enhance accuracy and speed in these domains. This paper investigates the application of ML algorithms to predict market volatility, a crucial determinant of derivative prices. Additionally, we explore how ML can be leveraged to calibrate complex pricing models that account for various market factors. Finally, we delve into the potential of ML - driven optimization techniques for refining hedging strategies, thereby mitigating the risks associated with derivative portfolios. Through a comprehensive review of recent research and case studies, this paper aims to highlight the transformative potential of ML and AI in revolutionizing derivatives pricing and risk management practices.

Keywords: Machine Learning, Derivatives Pricing, Risk Management, Volatility Prediction, Model Calibration, Hedging Strategy Optimization, Financial Technology (FinTech)

How to Cite?: Nikhil Jarunde, "Machine Learning and AI in Derivatives Pricing and Risk Management: Enhancing Accuracy and Speed - Investigate the Application of ML Algorithms to Predict Market Volatility, Calibrate Complex Pricing Models, and Optimize Hedging Strategies", Volume 10 Issue 8, August 2021, International Journal of Science and Research (IJSR), Pages: 1265-1273, https://www.ijsr.net/getabstract.php?paperid=SR24529191151, DOI: https://dx.doi.org/10.21275/SR24529191151