Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe

TL;DR

This paper introduces a new synthetic data generation process for the fixed income universe, enhancing asset allocation analysis through improved correlation matrix synthesis and data sampling techniques.

Contribution

It presents an enhanced CorrGAN model for generating synthetic correlation matrices and an Encoder-Decoder model for conditioned data sampling, enabling better asset allocation analysis.

Findings

Synthetic datasets improve asset allocation analysis.

Enhanced models generate more realistic correlation matrices.

Case study demonstrates portfolio improvement.

Abstract

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.