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An integrated heteroscedastic autoregressive model for forecasting realized volatilities

Title
An integrated heteroscedastic autoregressive model for forecasting realized volatilities
Authors
Cho, SoojinShin, Dong Wan
Ewha Authors
신동완
SCOPUS Author ID
신동완scopus
Issue Date
2016
Journal Title
JOURNAL OF THE KOREAN STATISTICAL SOCIETY
ISSN
1226-3192JCR Link1876-4231JCR Link
Citation
vol. 45, no. 3, pp. 371 - 380
Keywords
Conditional heteroscedasticityFractional integrationHAR modelHigh frequency dataLong-memoryVolatility forecasting
Publisher
KOREAN STATISTICAL SOC
Indexed
SCIE; SCOPUS; KCI WOS scopus
Abstract
A new strategy for forecasting realized volatility (RV) is proposed for the heteroscedastic autoregressive (HAR) model of Corsi (2009). The strategy is constraining the sum of the HAR coefficients to one, resulting in an integrated model, called IHAR model. The IHAR model is motivated by stationarity of estimated HAR model, downward biases of estimated HAR coefficients, and over-rejection of ADF test for long-memory processes. Considerable out of -sample forecast improvements of the IHAR model over the HAR model are demonstrated for RVs of 4 financial assets: the US S&P 500 index, the US NASDAQindex, the Japan yen/US dollar exchange rate, and the EU euro/US dollar exchange rate. Forecast improvement is also verified in a Monte, Carlo experiment and in an empirical comparison for an extended data set. The forecast improvement is shown to be a consequence of the fact that the IHAR model takes better advantage of the long memory of RV and the conditional heteroscedasticity of RV than the HAR model. (C) 2016 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
DOI
10.1016/j.jkss.2015.12.004
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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