Forecasting Tail Risks
Gianni De Nicolò and
Marcella Lucchetta ()
No 5286, CESifo Working Paper Series from CESifo Group Munich
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)
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Journal Article: Forecasting Tail Risks (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_5286
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