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On improved volatility modelling by fitting skewness in ARCH models
Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
Univ Aegean, Greece.
Mendel Univ Brno, Czech Republic.
Shumen Univ, Bulgaria.
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2019 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532Article in journal (Refereed) Epub ahead of print
Abstract [en]

We study ARCH/GARCH effects under possible deviation from normality. Since skewness is the principal cause for deviations from normality in many practical applications, e.g. finance, we study in particular skewness. We propose robust tests for normality both for NoVaS and modified NoVaS transformed and original data. Such an approach is not applicable for EGARCH, but applicable for GARCH-GJR models. A novel test procedure is proposed for the skewness in autoregressive conditional volatility models. The power of the tests is investigated with various underlying models. Applications with financial data show the applicability and the capabilities of the proposed testing procedure.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2019.
Keywords [en]
Robust test for normality, ARCH, GARCH model, NoVaS, skewness, kurtosis
National Category
Probability Theory and Statistics
Research subject
Economy
Identifiers
URN: urn:nbn:se:lnu:diva-89708DOI: 10.1080/02664763.2019.1671323ISI: 000488186400001OAI: oai:DiVA.org:lnu-89708DiVA, id: diva2:1362125
Available from: 2019-10-18 Created: 2019-10-18 Last updated: 2019-10-18

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Mantalos, Panagiotis

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  • apa
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
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