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Implied Volatility Directional Forecasting: A Machine Learning Approach

Vrontos, Spyridon and Galakis, John and Vrontos, Ioannis (2021) 'Implied Volatility Directional Forecasting: A Machine Learning Approach.' Quantitative Finance, 2021 (10). pp. 1687-1706. ISSN 1469-7688

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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.

Item Type: Article
Uncontrolled Keywords: Forecasting; Implied Volatility; Binary Logit; Machine Learning; Penalized Likelihood models; Investment Strategies
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 16 Mar 2021 14:35
Last Modified: 06 Jan 2022 14:22

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