Deep Structural Estimation: With an Application to Option Pricing

TL;DR

This paper introduces a deep neural network-based framework for structural economic model estimation, significantly improving efficiency and enabling detailed analysis, with an application comparing option pricing models and a non-parametric approach.

Contribution

It presents a novel deep learning methodology for structural estimation that overcomes dimensionality issues and accelerates computations, demonstrated through an option pricing application.

Findings

Bates model outperforms in out-of-sample pricing

Random forest excels at short horizons and illiquid days

Structural models outperform in delta hedging

Abstract

We propose a novel structural estimation framework in which we train a surrogate of an economic model with deep neural networks. Our methodology alleviates the curse of dimensionality and speeds up the evaluation and parameter estimation by orders of magnitudes, which significantly enhances one's ability to conduct analyses that require frequent parameter re-estimation. As an empirical application, we compare two popular option pricing models (the Heston and the Bates model with double-exponential jumps) against a non-parametric random forest model. We document that: a) the Bates model produces better out-of-sample pricing on average, but both structural models fail to outperform random forest for large areas of the volatility surface; b) random forest is more competitive at short horizons (e.g., 1-day), for short-dated options (with less than 7 days to maturity), and on days with poor liquidity; c) both structural models outperform random forest in out-of-sample delta hedging; d) the Heston model's relative performance has deteriorated significantly after the 2008 financial crisis.