CoFinDiff: Controllable Financial Diffusion Model for Time Series Generation

ArXiv ID: 2503.04164 “View on arXiv”

Authors: Unknown

Abstract

The generation of synthetic financial data is a critical technology in the financial domain, addressing challenges posed by limited data availability. Traditionally, statistical models have been employed to generate synthetic data. However, these models fail to capture the stylized facts commonly observed in financial data, limiting their practical applicability. Recently, machine learning models have been introduced to address the limitations of statistical models; however, controlling synthetic data generation remains challenging. We propose CoFinDiff (Controllable Financial Diffusion model), a synthetic financial data generation model based on conditional diffusion models that accept conditions about the synthetic time series. By incorporating conditions derived from price data into the conditional diffusion model via cross-attention, CoFinDiff learns the relationships between the conditions and the data, generating synthetic data that align with arbitrary conditions. Experimental results demonstrate that: (i) synthetic data generated by CoFinDiff capture stylized facts; (ii) the generated data accurately meet specified conditions for trends and volatility; (iii) the diversity of the generated data surpasses that of the baseline models; and (iv) models trained on CoFinDiff-generated data achieve improved performance in deep hedging task.

Keywords: Conditional Diffusion Models, Synthetic Financial Data, Stylized Facts, Cross-Attention, Deep Hedging, Derivatives / General Financial Assets

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics, including diffusion models, cross-attention mechanisms, and Haar wavelet transformations, to tackle a complex generative modeling problem. Empirical rigor is high, featuring extensive statistical tests (kurtosis, Hill index, autocorrelation) on stylized facts, controllability experiments, diversity metrics, and a downstream deep hedging task with improved performance.

  flowchart TD
    A["Research Goal<br>Controllable Financial Data Generation"] --> B["Methodology: CoFinDiff<br>Conditional Diffusion Model"]
    B --> C["Key Technique: Cross-Attention<br>Integrates Price Conditions"]
    C --> D{"Computational Process<br>Learn Relations: Conditions ↔ Data"}
    D --> E["Generate Synthetic Time Series<br>Aligning with Arbitrary Conditions"]
    E --> F["Key Findings & Outcomes"]
    
    F --> F1["Captures Stylized Facts"]
    F --> F2["Accurate Trend & Volatility Control"]
    F --> F3["Superior Data Diversity"]
    F --> F4["Improves Deep Hedging Performance"]