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.

翻译：我们提出了一种新颖的合成数据集生成流程，专门用于评估资产配置方法并构建固定收益领域的投资组合。该方法首先对CorrGAN模型进行改进以生成合成相关矩阵，随后提出一种编码器-解码器模型，该模型在给定相关矩阵条件下采样额外数据。生成的合成数据集能够支持跨不同资产领域的资产配置方法深度分析。此外，我们通过案例研究展示如何利用该合成数据集在基于模拟的资产配置流程中优化所构建的投资组合。