Paper: arXiv 2311.16004

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced generative neural networks (GANs, Encoder-Decoder architectures) and sophisticated data conditioning, indicating high mathematical complexity. It is highly data- and implementation-heavy, using real-world fixed income datasets, time-series transformations, and a detailed case study with portfolio performance evaluation, showing strong empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Improve Asset Allocation in Fixed Income"] --> B["Methodology: Synthetic Data Generation<br>(CorrGAN + Encoder-Decoder)"]
  B --> C["Input Data:<br>Real Historical Market Data"]
  C --> D["Step 1: Generate Synthetic Correlation Matrices<br>(Enhanced CorrGAN)"]
  D --> E["Step 2: Generate Synthetic Feature Data<br>(Encoder-Decoder conditioned on Correlations)"]
  E --> F["Outcome: Comprehensive Synthetic Dataset"]
  F --> G["Final Result: Enhanced Portfolio Construction<br>via Simulation-Based Allocation"]