Forecasting Tail Risks

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Research , Preprint 01 Jan 2015 Italy English Publisher:Elsevier BVJournal:SSRN Electronic Journal (eissn: 1556-5068,

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Authors: De Nicolò, Gianni; LUCCHETTA, Marcella;

doi: 10.2139/ssrn.2685707 , 10.2139/ssrn.2593708 , 10.1002/jae.2509

handle: 10419/110795 , 10278/3678767

Forecasting Tail Risks

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Abstract

SummaryThis paper presents an early warning system as a set of multi‐period forecasts of indicators of tail real and financial risks obtained using a large database of monthly US data for the period 1972:1–2014:12. Pseudo‐real‐time forecasts are generated from: (a) sets of autoregressive and factor‐augmented vector autoregressions (VARs), and (b) sets of autoregressive and factor‐augmented quantile projections. Our key finding is that forecasts obtained with AR and factor‐augmented VAR forecasts significantly underestimate tail risks, while quantile projections deliver fairly accurate forecasts and reliable early warning signals for tail real and financial risks up to a 1‐year horizon. Copyright © 2016 John Wiley & Sons, Ltd.

Country

Italy

Related Organizations

Ca Foscari University of Venice
Italy
JOHNS HOPKINS UNIVERSITY
United States
International Monetary Fund
United States
Center for Economic Studies
Germany
Johns Hopkins University
United States

Keywords

ddc:330, quantile projections, C50, tail risks, density forecasts, factor models, quantile projections, factor models, G20, Economics and Econometrics; Social Sciences (miscellaneous), E30, tail risks, density forecasts, jel: jel:C50, jel: jel:G20, jel: jel:E30

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