Deep Surrogates for Finance: With an Application to Option Pricing

Abstract

We introduce ``deep surrogates'' -- high-precision approximations of structural models based on deep neural networks, which speed up model evaluation and estimation by orders of magnitude and allow for various compute-intensive applications that were previously infeasible. As an application, we build a deep surrogate for a high-dimensional workhorse option pricing model. The surrogate enables us to re-estimate the model at high frequency to construct an option-implied tail risk measure, which is highly predictive of future market crashes. It also enables us to systematically examine the model's out-of-sample performances, which reveals the tradeoffs between structural and reduced-form approaches for option pricing. Moreover, we construct a measure for the degree of parameter instability and connect it to option market illiquidity in the data. Finally, we use the surrogate to construct conditional distributions of option returns, which is useful for risk management and provides a new way to test the model.

Keywords: surrogate, deep neural network, tail risk index, parameter instability, illiquidity, distribution of option return, VaR

JEL Classification: C45, C52, C58, G12, G13