Volatility Forecasting in Global Financial Markets Using TimeMixer
This work addresses volatility prediction for financial risk management, but it is incremental as it applies an existing method to new data without major innovations.
The study tackled volatility forecasting in global financial markets by applying the TimeMixer model, finding it performs exceptionally well for short-term predictions but accuracy diminishes for longer-term forecasts, especially in highly volatile markets.
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.