Supervised Deep Neural Networks (DNNs) for Pricing/Calibration of Vanilla/Exotic Options Under Various Different Processes
This work addresses computational inefficiencies in financial modeling for practitioners, though it is incremental as it applies existing DNN methods to a known bottleneck.
The paper tackled the problem of slow option pricing and calibration by applying supervised deep neural networks (DNNs) to price and calibrate vanilla and exotic options under various processes, resulting in exponential speed-up compared to common methods.
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.