This study investigates whether the direction of U.S. implied volatility, VIX index, can be forecast. Multiple forecasts are generated based on standard econometric models, but, more importantly, on several machine learning techniques. Their statistical significance is assessed by a plethora of performance evaluation measures, while real-time investment strategies are devised to appraise the investment implications of the underlying modeling approaches. The main conclusion of the analysis is that the implementation of machine learning techniques in implied volatility forecasting can be more effective compared to mainstream econometric models and model selection techniques, as they are superior both in a statistical and an economic evaluation setting.

本研究调查是否可以预测美国隐含波动率 VIX 指数的方向。多种预测是基于标准的计量经济模型生成的，但更重要的是，基于多种机器学习技术。它们的统计显着性是通过大量绩效评估措施来评估的，而实时投资策略旨在评估潜在建模方法的投资影响。分析的主要结论是，与主流计量经济学模型和模型选择技术相比，在隐含波动率预测中实施机器学习技术可以更有效，因为它们在统计和经济评估环境中均表现出色。