SBBTS: A Unified Schrödinger-Bass Framework for Synthetic Financial Time Series
This addresses the challenge of realistic time series generation for financial machine learning, offering a practical solution for data augmentation, though it is incremental as it builds on prior Schrödinger-Bass methods.
The paper tackled the problem of generating synthetic financial time series that jointly model drift and stochastic volatility, introducing the SBBTS framework, which improved downstream forecasting on S&P 500 data with higher classification accuracy and Sharpe ratio compared to real-data-only training.
We study the problem of generating synthetic time series that reproduce both marginal distributions and temporal dynamics, a central challenge in financial machine learning. Existing approaches typically fail to jointly model drift and stochastic volatility, as diffusion-based methods fix the volatility while martingale transport models ignore drift. We introduce the Schrödinger-Bass Bridge for Time Series (SBBTS), a unified framework that extends the Schrödinger-Bass formulation to multi-step time series. The method constructs a diffusion process that jointly calibrates drift and volatility and admits a tractable decomposition into conditional transport problems, enabling efficient learning. Numerical experiments on the Heston model demonstrate that SBBTS accurately recovers stochastic volatility and correlation parameters that prior SchrödingerBridge methods fail to capture. Applied to S&P 500 data, SBBTS-generated synthetic time series consistently improve downstream forecasting performance when used for data augmentation, yielding higher classification accuracy and Sharpe ratio compared to real-data-only training. These results show that SBBTS provides a practical and effective framework for realistic time series generation and data augmentation in financial applications.