Deep Generative Modeling for Financial Time Series with Application in VaR: A Comparative Review

ArXiv ID: 2401.10370 “View on arXiv”

Authors: Unknown

Abstract

In the financial services industry, forecasting the risk factor distribution conditional on the history and the current market environment is the key to market risk modeling in general and value at risk (VaR) model in particular. As one of the most widely adopted VaR models in commercial banks, Historical simulation (HS) uses the empirical distribution of daily returns in a historical window as the forecast distribution of risk factor returns in the next day. The objectives for financial time series generation are to generate synthetic data paths with good variety, and similar distribution and dynamics to the original historical data. In this paper, we apply multiple existing deep generative methods (e.g., CGAN, CWGAN, Diffusion, and Signature WGAN) for conditional time series generation, and propose and test two new methods for conditional multi-step time series generation, namely Encoder-Decoder CGAN and Conditional TimeVAE. Furthermore, we introduce a comprehensive framework with a set of KPIs to measure the quality of the generated time series for financial modeling. The KPIs cover distribution distance, autocorrelation and backtesting. All models (HS, parametric and neural networks) are tested on both historical USD yield curve data and additional data simulated from GARCH and CIR processes. The study shows that top performing models are HS, GARCH and CWGAN models. Future research directions in this area are also discussed.

Keywords: Generative AI, Historical Simulation (HS), Time Series Generation, Value at Risk (VaR), Conditional GANs, Fixed Income (Yield Curves)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures like GANs, WGANs, and diffusion models with mathematical formulations, but also includes comprehensive backtesting frameworks, comparative model performance on real yield curve data, and specific KPIs for financial modeling readiness.

  flowchart TD
    A["Research Goal: <br>Compare Deep Generative Models<br>for Financial Time Series in VaR"] --> B
    
    subgraph B ["Data & Inputs"]
        B1["Historical USD Yield Curve Data"]
        B2["Simulated GARCH & CIR Processes"]
    end

    B --> C["Methodology: Applied & Proposed Models"]
    
    subgraph C ["Models"]
        C1["Existing Models"]
        C2["Proposed New Methods"]
    end

    C1 --> C1a["CGAN, CWGAN"]
    C1 --> C1b["Diffusion, Signature WGAN"]
    C2 --> C2a["Encoder-Decoder CGAN"]
    C2 --> C2b["Conditional TimeVAE"]

    C --> D["Comprehensive Evaluation Framework"]
    D --> E["KPIs & Backtesting"]
    
    subgraph E ["Evaluation Metrics"]
        E1["Distribution Distance"]
        E2["Autocorrelation Structure"]
        E3["VaR Backtesting"]
    end

    E --> F["Key Findings/Outcomes"]
    
    F --> F1["Top Performers: <br>HS, GARCH, CWGAN"]
    F --> F2["Proposed Methods: <br>Encoder-Decoder CGAN & TimeVAE<br>Validated for Conditional Gen"]
    F --> F3["Future Directions: <br>Further Refinement of Generative Models"]