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Volatility Forecasting in Global Financial Markets Using TimeMixer

Alex Li

TL;DR

While TimeMixer performs exceptionally well in short-term volatility forecasting, its accuracy diminishes for longer-term predictions, particularly in highly volatile markets, highlighting the model's limitations in long-term forecasting.

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.

Volatility Forecasting in Global Financial Markets Using TimeMixer

TL;DR

While TimeMixer performs exceptionally well in short-term volatility forecasting, its accuracy diminishes for longer-term predictions, particularly in highly volatile markets, highlighting the model's limitations in long-term forecasting.

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.

Paper Structure

This paper contains 28 sections, 7 equations, 2 figures, 8 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Traditional Statistical Models for Volatility Forecasting
  4. Machine Learning and Deep Learning Approaches
  5. Multiscale Approaches for Time Series Decomposition
  6. TimeMixer: A Novel Multiscale-Mixing Approach
  7. Application of TimeMixer in Financial Markets
  8. MULTISCALE MIXING ARCHITECTURE
  9. Past-Decomposable-Mixing (PDM)
  10. Future-Multipredictor-Mixing (FMM)
  11. EXPERIMENTS
  12. Experiment Overview
  13. Stock Dataset
  14. Index ETF Dataset
  15. Forex Dataset
  16. ...and 13 more sections

Figures (2)

  • Figure 1: Overall architecture of TimeMixer, which consists of (a) Multiscale Time Series, (b) Past Decomposable Mixing and (c) Future Multipredictor Mixing for past observations and future predictions respectively.
  • Figure 2: The temporal linear layer in seasonal mixing (a), trend mixing (b) and future prediction (c).