Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe
This work addresses data scarcity for researchers and practitioners in fixed income asset allocation, though it is incremental as it builds on existing models like CorrGAN.
The authors tackled the problem of limited data for evaluating asset allocation methods in fixed income by developing a synthetic dataset generation process, which involves enhancing CorrGAN for correlation matrices and using an Encoder-Decoder model to sample data, and demonstrated its application in a case study to improve portfolio construction.
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.