Forecasting Tail Risks

Journal of Applied Econometrics, Forthcoming

43 Pages Posted: 4 Nov 2015

See all articles by Gianni De Nicolo

Gianni De Nicolo

Johns Hopkins University - Carey Business School; CESifo (Center for Economic Studies and Ifo Institute)

Marcella Lucchetta

Ca Foscari University of Venice

Multiple version iconThere are 2 versions of this paper

Date Written: November 3, 2015

Abstract

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 U.S. data for the period 1972:1-2014:12 Pseudo-real time forecasts are generated from: (a) sets of autoregressive and factor-augmented VARs, and (b) sets of auto-regressive 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 one-year horizon.

Keywords: tail risks, density forecasts, factor models, quantile projections

JEL Classification: C500, E300, G200

Suggested Citation

De Nicolo, Gianni and Lucchetta, Marcella, Forecasting Tail Risks (November 3, 2015). Journal of Applied Econometrics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2685707 or http://dx.doi.org/10.2139/ssrn.2685707

Gianni De Nicolo (Contact Author)

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States
(410) 234-4507 (Phone)

CESifo (Center for Economic Studies and Ifo Institute) ( email )

Poschinger Str. 5
Munich, DE-81679
Germany

Marcella Lucchetta

Ca Foscari University of Venice ( email )

Venice
Italy

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