Volatility Forecasting in Global Financial Markets Using TimeMixer

ArXiv ID: 2410.09062 “View on arXiv”

Authors: Unknown

Abstract

Predicting volatility in financial markets, including stocks, index ETFs, foreign exchange, and cryptocurrencies, remains a challenging task due to the inherent complexity and non-linear dynamics of these time series. In this study, I apply TimeMixer, a state-of-the-art time series forecasting model, to predict the volatility of global financial assets. TimeMixer utilizes a multiscale-mixing approach that effectively captures both short-term and long-term temporal patterns by analyzing data across different scales. My empirical results reveal that while TimeMixer performs exceptionally well in short-term volatility forecasting, its accuracy diminishes for longer-term predictions, particularly in highly volatile markets. These findings highlight TimeMixer’s strength in capturing short-term volatility, making it highly suitable for practical applications in financial risk management, where precise short-term forecasts are critical. However, the model’s limitations in long-term forecasting point to potential areas for further refinement.

Keywords: TimeMixer, volatility forecasting, time series forecasting, multiscale-mixing, financial risk management, Multi-asset (stocks, index ETFs, foreign exchange, cryptocurrencies)

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts including multiscale decomposition and specific neural network blocks (PDM/FMM), indicative of high complexity. It is backed by empirical results across multiple asset classes (stocks, ETFs, FX, crypto) and discusses performance metrics, showing significant data and implementation efforts.

  flowchart TD
    A["Research Goal: Predict Volatility<br>in Global Financial Markets"] --> B["Data Collection<br>Multi-asset Time Series"]
    B --> C["Methodology: Apply TimeMixer<br>Multiscale-Mixing Approach"]
    C --> D["Computational Process<br>Capture Short & Long-Term Patterns"]
    D --> E{"Key Findings / Outcomes"}
    E --> F["Strong Performance in<br>Short-Term Forecasting"]
    E --> G["Limitations in<br>Long-Term Forecasting"]
    F --> H["Application: Financial<br>Risk Management"]