Modeling and predicting the CBOE market volatility index
AbstractThis paper performs a thorough statistical examination of the time-series properties of the market volatility index (VIX) from the Chicago Board Options Exchange (CBOE). The motivation lies on the widespread consensus that the VIX is a barometer to the overall market sentiment as to what concerns risk appetite. To assess the statistical behavior of the time series, we run a series of preliminary analyses whose results suggest there is some long-range dependence in the VIX index. This is consistent with the strong empirical evidence in the literature supporting long memory in both options-implied and realized volatilities. We thus resort to linear and nonlinear heterogeneous autoregressive (HAR) processes, including smooth transition and threshold HAR-type models, as well as to smooth transition autoregressive trees (START) for modeling and forecasting purposes. The in-sample results for the HAR-type indicate that they cope with the long-range dependence in the VIX time series as well as the more popular ARFIMA model. In addition, the highly nonlinear START specification also does a god job in controlling for the long memory. The out-of-sample analysis evince that the linear ARMA and ARFIMA models perform very well in the short run and very poorly in the long-run, whereas the START model entails by far the best results for the longer horizon despite of failing at shorter horizons. In contrast, the HAR-type models entail reasonable relative performances in most horizons. Finally, we also show how a simple forecast combination brings about great improvements in terms of predictive ability for most horizons.
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Date of creation: Aug 2007
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heterogeneous autoregression; implied volatility; smooth transition; VIX.;
Find related papers by JEL classification:
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-08-27 (All new papers)
- NEP-CFN-2007-08-27 (Corporate Finance)
- NEP-FOR-2007-08-27 (Forecasting)
- NEP-MAC-2007-08-27 (Macroeconomics)
- NEP-RMG-2007-08-27 (Risk Management)
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- Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2011. "Conditional jumps in volatility and their economic determinants," "Marco Fanno" Working Papers 0138, Dipartimento di Scienze Economiche "Marco Fanno".
- Hammoudeh, Shawkat & Liu, Tengdong & Chang, Chia-Lin & McAleer, Michael, 2013.
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- Hammoudeh, S.M. & Liu, T. & Chang, C-L. & McAleer, M.J., 2011. "Risk Spillovers in Oil-Related CDS, Stock and Credit Markets," Econometric Institute Report EI 2011-15, Erasmus University Rotterdam, Econometric Institute.
- Shawkat Hammoudeh & Tengdong Liu & Chia-Lin Chang & Michael McAleer, 2011. "Risk Spillovers in Oil-Related CDS, Stock and Credit Markets," KIER Working Papers 772, Kyoto University, Institute of Economic Research.
- Shawkat Hammoudeh & Tengdong Liu & Chia-Lin Chang & Michael McAleer, 2011. "Risk Spillovers in Oil-Related CDS, Stock and Credit Markets," Documentos del Instituto Complutense de AnÃ¡lisis EconÃ³mico 2011-12, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
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