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http://dx.doi.org/10.5351/KJAS.2016.29.7.1213

LIHAR model for forecasting realized volatilities featuring long-memory and asymmetry  

Shin, Jiwon (Department of Statistics, Ewha Womans University)
Shin, Dong Wan (Department of Statistics, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.29, no.7, 2016 , pp. 1213-1229 More about this Journal
Abstract
Cho and Shin (2016) recently demonstrated that an integrated HAR model has a forecast advantage over the HAR model of Corsi (2009). Recalling that realized volatilities of financial assets have asymmetries, we add a leverage term to the integrated HAR model, yielding the LIHAR model. Out-of-sample forecast comparisons show superiority of the LIHAR model over the HAR and IHAR models. The comparison was made for all the 20 realized volatilities in the Oxford-Man Realized Library focusing specially on the DJIA, the S&P 500, the Russell 2000, and the KOSPI. Analysis of the realized volatility data sets reveal apparent long-memory and asymmetry. The LIHAR model takes advantage of the long-memory and asymmetry and produces better forecasts than the HAR, IHAR, LHAR models.
Keywords
leverage; HAR model; asymmetry; long memory; nonstationarity; volatility forecasting;
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Times Cited By KSCI : 2  (Citation Analysis)
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