用TimeMixer预测全球金融资产波动率，短时效果好，长时有局限。
Volatility Forecasting in Global Financial Markets Using TimeMixer
- 采用多尺度混合机制，同时捕捉短期与长期时间模式。
- 短期波动率预测表现优异，长期预测精度明显下降。
- 适合高频风险管控，不适用于长期投资决策参考。
由于金融市场（包括股票、指数ETF、外汇及加密货币）时间序列具有内在复杂性和非线性动态，预测其波动率仍具挑战性。本文应用前沿时间序列预测模型TimeMixer，对全球金融资产的波动率进行预测。TimeMixer通过多尺度混合方法，在不同时间尺度上分析数据，有效捕捉短时与长时依赖关系。实证结果表明，TimeMixer在短期波动率预测中表现卓越，但在高度波动市场中，长期预测准确性显著下降。该发现凸显了其在捕捉短期波动方面的优势，使其在金融风险管理等需要精确短期预测的场景中极具应用价值。然而，其在长期预测中的局限性也提示未来改进方向。
原文摘要 · Abstract (English)
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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