Implied volatility directional forecasting: a machine learning approach
研究了能否预测美国隐含波动率（VIX指数）的方向，比较了标准计量模型与多种机器学习技术的预测效果，发现机器学习在统计和经济评估上均优于主流方法。
This study investigates whether the direction of U.S. implied volatility, VIX index, can be forecasted. 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.