Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230757
Authors: 
Chen, Ying
Han, Qian
Niu, Linlin
Year of Publication: 
2018
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2018-046
Abstract: 
We model the term structure of implied volatility (TSIV) with an adaptive approach to improve predictability, which treats dynamic time series models of globally time- varying but locally constant parameters and uses a data-driven procedure to ?nd the local optimal interval. We choose two speci?cations of the adaptive models: a simple local AR (LAR) model for a univariate implied volatility series and an adaptive dynamic Nelson-Siegel (ADNS) model of three factors, each based on an LAR, to model the cross- section of the TSIV simultaneously with parsimony. Both LAR and ADNS models uniformly outperform more than a dozen alternative models with significance across maturities for 1-20 day forecast horizons. Measured by RMSE and MAE, the forecast errors of the random walk model can be reduced by between 20% and 60% for the 5 to 20 days ahead forecast. In terms of prediction accuracy of future directional changes, the adaptive models achieve an accuracy range of 60%-90%, which strictly dominates the range of 30%-59% of the alternative models.
Subjects: 
Term structure of implied volatility
local parametric models
forecasting
JEL: 
C32
C53
Document Type: 
Working Paper

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