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Forecasting Tail Risks

Gianni De Nicolò and Marcella Lucchetta ()

No 5286, CESifo Working Paper Series from CESifo Group Munich

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 (search for similar items in EconPapers)
JEL-codes: C50 E30 G20 (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

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Journal Article: Forecasting Tail Risks (2017) Downloads
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