Forecasting Tail Risks

47 Pages Posted: 14 Apr 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: March 31, 2015

Abstract

Reliable early warning signals are essential for timely implementation of macroeconomic and macro-prudential policies. This paper presents an early warning system as a set of multi-period forecasts of indicators of tail real and financial (systemic) risks. Forecasts are obtained from: (a) autoregressive and factor-augmented VARs with linear GARCH volatility (FAVARs), and (b) auto-regressive and factor-augmented Quantile Projections (QPs). We use a large database of monthly U.S. data for the period 1972:1-2014:12 to forecasts our tail risk indicators with each model in pseudo-real time. Our key finding is that forecasts obtained with autoregressive and FAVAR models significantly underestimate tail risks, while forecasts obtained with autoregressive and factor-augmented QPs deliver superior and fairly 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 (March 31, 2015). CESifo Working Paper Series No. 5286. Available at SSRN: https://ssrn.com/abstract=2593708

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

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
104
Abstract Views
511
rank
112,397
PlumX Metrics