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Forecasting Tail Risks
Author(s) -
De Nicolò Gianni,
Lucchetta Marcella
Publication year - 2016
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2509
Subject(s) - quantile , autoregressive model , econometrics , warning system , data set , vector autoregression , factor analysis , economics , computer science , artificial intelligence , telecommunications
Summary This 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.