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http://dx.doi.org/10.5351/KJAS.2017.30.3.323

An outlier-adaptive forecast method for realized volatilities  

Shin, Ji Won (Department of Statistics, Ewha Womans University)
Shin, Dong Wan (Department of Statistics, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.30, no.3, 2017 , pp. 323-334 More about this Journal
Abstract
We note that the dynamics of realized volatilities (RVs) are near the boundary between stationarity and non-stationarity because RVs have persistent long-memory and are often subject to fairly large outlying values. To forecast realized volatility, we consider a new method that adaptively use models with and without unit root according to the abnormality of observed RV: heterogeneous autoregressive (HAR) model and the Integrated HAR (IHAR) model. The resulting method is called the IHAR-O-HAR method. In an out-of-sample forecast comparison for the realized volatility datasets of the 3 major indexes of the S&P 500, the NASDAQ, and the Nikkei 225, the new IHAR-O-HAR method is shown superior to the existing HAR and IHAR method.
Keywords
realized volatility; HAR model; additive outlier; volatility forecasting;
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