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

Author

Listed:
  • Szymon Kubiak
  • Tillman Weyde
  • Oleksandr Galkin
  • Dan Philps
  • Ram Gopal

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.

Suggested Citation

  • Szymon Kubiak & Tillman Weyde & Oleksandr Galkin & Dan Philps & Ram Gopal, 2023. "Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe," Papers 2311.16004, arXiv.org.
    • Handle: RePEc:arx:papers:2311.16004
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    References listed on IDEAS

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    1. Adriano Koshiyama & Nick Firoozye & Philip Treleaven, 2021. "Generative adversarial networks for financial trading strategies fine-tuning and combination," Quantitative Finance, Taylor & Francis Journals, vol. 21(5), pages 797-813, May.
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    4. Solveig Flaig & Gero Junike, 2022. "Scenario Generation for Market Risk Models Using Generative Neural Networks," Risks, MDPI, vol. 10(11), pages 1-28, October.
    5. Kwanda Sydwell Ngwenduna & Rendani Mbuvha, 2021. "Alleviating Class Imbalance in Actuarial Applications Using Generative Adversarial Networks," Risks, MDPI, vol. 9(3), pages 1-33, March.
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