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Abstract

This study provides a new approach for implied volatility indices forecasting.
We assess whether non-parametric techniques provide better predictions of implied volatility compared to standard forecasting models, such as AFRIMA and HAR.
A combination of Singular Spectrum Analysis SSA and Holt-Winters HW model is applied on eight implied volatility indices for the period from February, 2001 to July, 2013.
The findings confirm that the SSA-HW provides statistically superior one trading day and ten trading days ahead implied volatility forecasts world widely.
Model-averaged forecasts suggest that the forecasting accuracy is further enhanced, for the ten-days ahead, when the SSA-HW is combined with an ARI1,1 model.
Additionally, the trading game reveals that the SSA-HW and the ARI-SSA-HW are able to generate significant average positive net daily returns in the out-of-sample period.
The results are important for option pricing, portfolio management, value-at-risk and economic policy.



Item Type: MPRA Paper -

Original Title: Forecasting implied volatility indices worldwide: A new approach-

Language: English-

Keywords: Implied Volatility, Volatility Forecasting, Singular Spectrum Analysis, ARFIMA, HAR, Holt-Winters, Model Confidence Set, Combined Forecasts.-

Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: GeneralC - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion ProcessesC - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and SelectionC - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation MethodsG - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets-





Autor: Degiannakis, Stavros

Fuente: https://mpra.ub.uni-muenchen.de/72084/



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