Volatility Forecasting in Global Financial Markets Using TimeMixer

Alex Li

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arXiv · 2410.09062

Volatility Forecasting in Global Financial Markets Using TimeMixer

Alex Li

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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.

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q-fin.ST cs.AI cs.LG

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Alex Li. 2024-09-27. Volatility Forecasting in Global Financial Markets Using TimeMixer. https://arxiv.org/abs/2410.09062

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