Generating drawdown-realistic financial price paths using path signatures

TL;DR

This paper introduces a non-parametric Monte Carlo method combining variational autoencoders and path signatures to generate financial price paths with realistic drawdowns, aiding in risk management and strategy testing.

Contribution

It proposes a novel approach that integrates drawdown reconstruction loss with path signatures in a generative model for more realistic financial simulations.

Findings

Successfully generates drawdown-realistic paths for diverse portfolios

Proves the regularity and approximation consistency of the drawdown function

Demonstrates effectiveness on empirical and fractional Brownian data

Abstract

A novel generative machine learning approach for the simulation of sequences of financial price data with drawdowns quantifiably close to empirical data is introduced. Applications such as pricing drawdown insurance options or developing portfolio drawdown control strategies call for a host of drawdown-realistic paths. Historical scenarios may be insufficient to effectively train and backtest the strategy, while standard parametric Monte Carlo does not adequately preserve drawdowns. We advocate a non-parametric Monte Carlo approach combining a variational autoencoder generative model with a drawdown reconstruction loss function. To overcome issues of numerical complexity and non-differentiability, we approximate drawdown as a linear function of the moments of the path, known in the literature as path signatures. We prove the required regularity of drawdown function and consistency of the approximation. Furthermore, we obtain close numerical approximations using linear regression for fractional Brownian and empirical data. We argue that linear combinations of the moments of a path yield a mathematically non-trivial smoothing of the drawdown function, which gives one leeway to simulate drawdown-realistic price paths by including drawdown evaluation metrics in the learning objective. We conclude with numerical experiments on mixed equity, bond, real estate and commodity portfolios and obtain a host of drawdown-realistic paths.