Applying Deep Learning to Calibrate Stochastic Volatility Models ...

• Main Authors: Sridi, Abir, Bilokon, Paul
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2023
• Subjects: Computational Finance q-fin.CP; Artificial Intelligence cs.AI; Mathematical Finance q-fin.MF; Pricing of Securities q-fin.PR; Risk Management q-fin.RM; FOS Economics and business; FOS Computer and information sciences; DML
• Online Access: https://dx.doi.org/10.48550/arxiv.2309.07843; https://arxiv.org/abs/2309.07843

Stochastic volatility models, where the volatility is a stochastic process, can capture most of the essential stylized facts of implied volatility surfaces and give more realistic dynamics of the volatility smile/skew. However, they come with the significant issue that they take too long to calibrate. Alternative calibration methods based on Deep Learning (DL) techniques have been recently used to build fast and accurate solutions to the calibration problem. Huge and Savine developed a Differential Machine Learning (DML) approach, where Machine Learning models are trained on samples of not only features and labels but also differentials of labels to features. The present work aims to apply the DML technique to price vanilla European options (i.e. the calibration instruments), more specifically, puts when the underlying asset follows a Heston model and then calibrate the model on the trained network. DML allows for fast training and accurate pricing. The trained neural network dramatically reduces Heston ...