Generative model for financial time series trained with MMD using a signature kernel

Abstract: Generating synthetic financial time series data that accurately reflects real-world market dynamics holds tremendous potential for various applications, including portfolio optimization, risk management, and large scale machine learning. We present an approach for training generative models for financial time series using the maximum mean discrepancy (MMD) with a signature kernel. Our method leverages the expressive power of the signature transform to capture the complex dependencies and temporal structures inherent in financial data. We employ a moving average model to model the variance of the noise input, enhancing the model's ability to reproduce stylized facts such as volatility clustering. Through empirical experiments on S&P 500 index data, we demonstrate that our model effectively captures key characteristics of financial time series and outperforms a comparable GAN-based approach. In addition, we explore the application of the synthetic data generated to train a reinforcement learning agent for portfolio management, achieving promising results. Finally, we propose a method to add robustness to the generative model by tweaking the noise input so that the generated sequences can be adjusted to different market environments with minimal data.