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.
Term structure of implied volatility local parametric models forecasting