Supervised Deep Neural Networks (DNNs) for Pricing/Calibration of Vanilla/Exotic Options Under Various Different Processes

2019/02/15 by Ali Hirsa, Hirsa, Ali, Tugce Karatas +3 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · #Capital Investment and Risk Analysis #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Pricing of Securities (q-fin.PR) #Stochastic processes and financial applications #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.1902.05810

openalex publication_date 2019/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We apply supervised deep neural networks (DNNs) for pricing and calibration of both vanilla and exotic options under both diffusion and pure jump processes with and without stochastic volatility. We train our neural network models under different number of layers, neurons per layer, and various different activation functions in order to find which combinations work better empirically. For training, we consider various different loss functions and optimization routines. We demonstrate that deep neural networks exponentially expedite option pricing compared to commonly used option pricing methods which consequently make calibration and parameter estimation super fast.

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