Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 27 Nov 2023.
Builds on
10 of 22 references.
References · 10
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2022Generative Adversarial Networkscited by 5,603
- 2017Improved Training of Wasserstein GANscited by 2,223
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
- 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 284
- 2017Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resizecited by 37
- 2019CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networkscited by 15
- 2021Scenario generation for market risk models using generative neural networkscited by 7
- 2021Deep Hedging, Generative Adversarial Networks, and Beyondcited by 1
- 2021cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptopecited by 1
Built on by
Not cited yet.