Volatility Forecasting in Global Financial Markets Using TimeMixer

TL;DR

This paper applies the TimeMixer model to forecast volatility across various global financial markets, demonstrating its effectiveness in short-term predictions but noting limitations in long-term accuracy, especially in volatile conditions.

Contribution

It introduces the application of the TimeMixer model to global financial volatility forecasting, highlighting its strengths and limitations in different temporal horizons.

Findings

TimeMixer excels in short-term volatility prediction.

Model's accuracy decreases for long-term forecasts.

Effective for risk management in volatile markets.

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.