Implied volatility directional forecasting: a machine learning approach

Spyridon D. Vrontos, John Galakis and Ioannis D. Vrontos

Quantitative Finance, 2021, vol. 21, issue 10, 1687-1706

Abstract: 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.

Date: 2021
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DOI: 10.1080/14697688.2021.1905869

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