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dc.contributor.authorApergis, Nicholas
dc.date.accessioned2020-12-09T10:56:28Z
dc.date.available2020-12-09T10:56:28Z
dc.date.issued2020-12-03
dc.identifier.citationApergis, N. (2020). 'Evaluating tail risks for the US economic policy uncertainty'. International Journal of Finance and Economics, pp. 1-40.en_US
dc.identifier.doi10.1002/ijfe.2354 PDFPDF
dc.identifier.urihttp://hdl.handle.net/10545/625455
dc.description.abstractThe goal of this paper is to employ a relatively new methodological approach to extract quantile-based economic policy uncertainty risk forecasts using the Quantile Autoregressive Distributed Lag Mixed-Frequency Data Sampling (QADL-MIDAS) regression model recommended by Ghysels and Iania (2018). This type of modelling delivers better quantile forecasts at various forecasting horizons. The forecasting results not only imply that the risk measure of economic policy uncertainty measure is linked to the future evolution of the index itself, but also it help constructing explicitly EPU risk measures, which are used to identify what drives such risk policy measures, especially across certain sub-sample periods associated with major global events, such as the collapse of the Lehman Brothers, the Trump’s election, and the trade-war tensions between the US and China. The findings offer a new empirical perspective to the existing economic policy uncertainty literature, documenting that special world events carry a strong informational content as being a primary key to understand the dynamics of the economic policy tails.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/epdf/10.1002/ijfe.2354en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectResearch Subject Categories::SOCIAL SCIENCESen_US
dc.subjecteconomic policyen_US
dc.subjectforecasting horizonsen_US
dc.titleEvaluating tail risks for the US economic policy uncertaintyen_US
dc.typeArticleen_US
dc.identifier.eissn1099-1158
dc.contributor.departmentUniversity of Derbyen_US
dc.identifier.journalInternational Journal of Finance and Economicsen_US
dcterms.dateAccepted2020-11-16
dc.author.detail786373en_US


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