Generating drawdown-realistic financial price paths using path signatures
This addresses the need for realistic financial simulations for risk management and strategy backtesting, though it is incremental as it builds on existing generative models and path signature techniques.
The paper tackled the problem of generating financial price paths with realistic drawdowns for applications like pricing drawdown insurance, by introducing a generative machine learning approach that combines a variational autoencoder with a drawdown reconstruction loss using path signatures. They proved the method's regularity and consistency, and demonstrated close numerical approximations on fractional Brownian and empirical data, producing drawdown-realistic paths for mixed portfolios.
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.