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.
Highlights
This study investigates whether the direction of U.S implied volatility, volatility have significant reper- index (VIX) index, can be forecast
This study investigates whether the direction of U.S implied volatility, VIX index, can be forecast
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
Summary
The modeling approaches and machine learning techniques used to forecast VIX index related directional patterns are outlined . Generalized linear models (GLM) can be used to model the dependence of the binary directional variable Yt, given a set of N (lagged) covariates or predictor variables, Xj,t−l, j = 1, . ), which is a linear function of the predictor variables, the autoregressive components, and the unknown parameters. Yt−p) denotes the augmented vector of predictors that contain the lagged predictor variables and the autoregressive components, h being the maximum number of lags used in the model, and θ = (β , φ ) the total parameter vector with β =. Maximum likelihood estimates of the Logit regression model parameters, θ, can be obtained by assuming that each.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
