Improved Data Generation for Enhanced Asset Allocation: A Synthetic
  Dataset Approach for the Fixed Income Universe
Szymon Kubiak Tillman Weyde Oleksandr Galkin Dan Philps Ram Gopal
We present a novel process for generating synthetic datasets tailored to
assess asset allocation methods and construct portfolios within the fixed
income universe. Our approach begins by enhancing the CorrGAN model to generate
synthetic correlation matrices. Subsequently, we propose an Encoder-Decoder
model that samples additional data conditioned on a given correlation matrix.
The resulting synthetic dataset facilitates in-depth analyses of asset
allocation methods across diverse asset universes. Additionally, we provide a
case study that exemplifies the use of the synthetic dataset to improve
portfolios constructed within a simulation-based asset allocation process.