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Bootstrap forecast intervals for asymmetric volatilities via EGARCH model

Title
Bootstrap forecast intervals for asymmetric volatilities via EGARCH model
Authors
Maeng H.Shin D.W.
Ewha Authors
신동완
SCOPUS Author ID
신동완scopus
Issue Date
2017
Journal Title
Communications in Statistics - Theory and Methods
ISSN
0361-0926JCR Link
Citation
Communications in Statistics - Theory and Methods vol. 46, no. 3, pp. 1144 - 1157
Keywords
Asymmetric volatilitybootstrappingEGARCH modelvolatility forecasting
Publisher
Taylor and Francis Inc.
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Bootstrap forecast intervals are developed for volatilities having asymmetric features, which are accounted for by fitting EGARCH models. A Monte-Carlo simulation compares the proposed forecast intervals with those based on GARCH fittings which ignore asymmetry. The comparison reveals substantial advantage of addressing asymmetry through EGARCH fitting over ignoring it as the conventional GARCH forecast. The EGARCH forecast intervals have empirical coverage probabilities closer to the nominal level and/or have shorter average lengths than the GARCH forecast intervals. The finding is also supported by real dataset analysis of Dow–Jones index and financial times stock exchange (FTSE) 100 index. © 2017 Taylor & Francis Group, LLC.
DOI
10.1080/03610926.2015.1014105
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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