Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics

TL;DR

This paper introduces a diffusion model-based method for generating synthetic financial market data that closely mimics real data, offering improved accuracy and efficiency over traditional Monte Carlo approaches.

Contribution

The paper presents a novel diffusion model approach for synthetic financial data generation, outperforming Monte Carlo methods in accuracy and computational efficiency.

Findings

Synthetic data passes key statistical tests for market data.

Covariance matrices from synthetic data have lower condition numbers.

The method is validated on large equity datasets.

Abstract

We propose a highly efficient and accurate methodology for generating synthetic financial market data using a diffusion model approach. The synthetic data produced by our methodology align closely with observed market data in several key aspects: (i) they pass the two-sample Cramer - von Mises test for portfolios of assets, and (ii) Q - Q plots demonstrate consistency across quantiles, including in the tails, between observed and generated market data. Moreover, the covariance matrices derived from a large set of synthetic market data exhibit significantly lower condition numbers compared to the estimated covariance matrices of the observed data. This property makes them suitable for use as regularized versions of the latter. For model training, we develop an efficient and fast algorithm based on numerical integration rather than Monte Carlo simulations. The methodology is tested on a large set of equity data.