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 helps us systematically examine the model’s out-of-sample performance, which reveals the tradeoffs between structural and reduced-form approaches for ..