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.

提出了一种生成针对资产配置方法和构建固定收益领域投资组合的合成数据集的新方法，该方法通过改进 CorrGAN 模型生成合成相关矩阵，并提出了一种编码器 - 解码器模型，在给定相关矩阵的条件下采样附加数据，从而促进对不同资产中的资产配置方法进行深入分析，并提供一个案例研究，说明了利用合成数据集改进基于模拟的资产配置过程中构建的投资组合的应用。