Deep surrogates for finance: With an application to option pricing
Researchers introduce deep surrogates, high-precision neural networks that quickly approximate complex financial models, especially for option pricing. Their method enables rapid analysis at scales previously impossible. Notably, the surrogate model creates an option-implied tail risk measure that strongly predicts market crashes, a surprising insight for risk management. The study also links model instability to illiquidity in option markets. While the approach boosts speed and predictive power, questions remain about overfitting and transparency in neural network surrogates.
What it examines
This paper introduces deep surrogates—fast, accurate neural network models that approximate complex financial models. The authors apply these surrogates to option pricing, enabling high-frequency estimation and new risk measures. The study aims to make advanced financial modeling faster and more practical for real-world applications.
What it concludes
Deep surrogates allow for rapid risk measurement, improved model testing, and analysis of market instability. These tools can help predict market crashes, manage risk, and study market liquidity. The approach opens new research directions but may need further validation across different financial markets and conditions.
Evidence objects
Researchers introduce 'deep surrogates,' neural networks that rapidly approximate complex financial models, revolutionizing option pricing analysis with unprecedented speed and scale for high-frequency, large-scale financial computations.
key_findings bullet 1 · key_findings · validation V0
A standout finding: deep surrogates enable swift re-estimation of option-implied tail risk, which is highly predictive of future market crashesoffering a powerful, surprising tool for risk management and forecasting.
key_findings bullet 2 · key_findings · validation V0
The study links model parameter instability to option market illiquidity, providing fresh insights into market microstructure, but notes concerns about neural network surrogates' robustness, overfitting, and interpretability remain unresolved.
key_findings bullet 3 · key_findings · validation V0
This paper presents 'deep surrogates,' a novel deep neural network methodology for high-precision approximation of structural financial models, revolutionizing derivative modeling and option pricing. Its originality lies in enabling high-frequency re-estimation, constructing option-implied tail risk measures, and connecting to market illiquidity, offering groundbreaking tools for quantitative finance and risk management.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
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 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.
Source row: 565 · abstract type: unknown