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

Ying Chen, Qian Han and Linlin Niu

No 2018-046, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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.

Keywords: Term structure of implied volatility; local parametric models; forecasting (search for similar items in EconPapers)
JEL-codes: C32 C53 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)

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Journal Article: Forecasting the term structure of option implied volatility: The power of an adaptive method (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2018046

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