Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2018 046

Forecasting the Term Structure of Option Implied Volatility: The Power of an Adaptive Method

Ying Chen
Qian Han
Linlin Niu

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 signi?cance 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

JEL Classification:
C32, C53