SBBTS creates a diffusion process that jointly models drift and stochastic volatility in financial time series via a tractable decomposition into conditional transport problems, recovering parameters missed by prior Schrödinger bridge methods and improving downstream ML performance on S&P 500 data.
Synthetic data for portfolios: A throw of the dice will never abolish chance
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A factor-conditioned Diffusion Transformer learns cross-sectional next-day return distributions and generates samples for daily mean-variance and mean-CVaR portfolio optimization that outperforms benchmarks on Chinese A-share data.
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SBBTS: A Unified Schr\"odinger-Bass Framework for Synthetic Financial Time Series
SBBTS creates a diffusion process that jointly models drift and stochastic volatility in financial time series via a tractable decomposition into conditional transport problems, recovering parameters missed by prior Schrödinger bridge methods and improving downstream ML performance on S&P 500 data.
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Factor-Based Conditional Diffusion Model for Contextual Portfolio Optimization
A factor-conditioned Diffusion Transformer learns cross-sectional next-day return distributions and generates samples for daily mean-variance and mean-CVaR portfolio optimization that outperforms benchmarks on Chinese A-share data.