改进数据生成以增强资产配置：面向固定投资领域的合成数据集方法
Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe
AI总结：
本文提出一种生成合成数据集的新流程，通过增强CorrGAN模型生成相关矩阵并结合Encoder-Decoder模型进行条件采样，以评估固定投资领域的资产配置方法并改进投资组合构建。
摘要：
我们提出了一种生成合成数据集的新流程，专为评估资产配置方法及在固定投资领域构建投资组合而定制。该方法首先增强CorrGAN模型以生成合成相关矩阵。随后，我们提出一种Encoder-Decoder模型，基于给定的相关矩阵进行条件采样以生成额外数据。所得合成数据集有助于对跨多种资产领域的资产配置方法进行深入分析。此外，我们提供了一个案例研究，展示了如何利用该合成数据集来改进基于模拟的资产配置过程中构建的投资组合。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
