Testing for an omitted multiplicative long-term component in GARCH models

• Verfasst von: Conrad, Christian [VerfasserIn]
• Verfasst von: Schienle, Melanie [VerfasserIn]
• Titel: Testing for an omitted multiplicative long-term component in GARCH models
• Verf.angabe: Christian Conrad, Melanie Schienle
• Jahr: 2020
• Umfang: 14 S.
• Fussnoten: Published online: 07 Sep 2018 ; Gesehen am 17.04.2019
• Titel Quelle: Enthalten in: Journal of business & economic statistics
• Ort Quelle: Abingdon : Taylor & Francis, 1983
• Jahr Quelle: 2020
• Band/Heft Quelle: 38(2020), 2, Seite 229-242
• ISSN Quelle: 1537-2707
• DOI: doi:10.1080/07350015.2018.1482759
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: GARCH-MIDAS
• Sach-SW: LM test
• Sach-SW: Long-term volatility
• Sach-SW: Mixed-frequency data
• Sach-SW: Volatility component models
• Sach-SW: GARCH-MIDAS
• Sach-SW: LM test
• Sach-SW: Long-term volatility
• Sach-SW: Mixed-frequency data
• Sach-SW: Volatility component models
• Form-SW: Aufsatz in Zeitschrift
• K10plus-PPN: 1663391777

Abstract

We consider the problem of testing for an omitted multiplicative long-term component in GARCH-type models. Under the alternative, there is a two-component model with a short-term GARCH component that fluctuates around a smoothly time-varying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange multiplier statistic for testing the null hypothesis that the variable has no explanatory power. We derive the asymptotic theory for our test statistic and investigate its finite sample properties by Monte Carlo simulation. Our test also covers the mixed-frequency case in which the returns are observed at a higher frequency than the explanatory variable. The usefulness of our procedure is illustrated by empirical applications to S&P 500 return data. Supplementary materials for this article are available online.