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Forecasting Tail Risks

Author

Listed:
  • Gianni De Nicolò
  • Marcella Lucchetta

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.

Suggested Citation

  • Gianni De Nicolò & Marcella Lucchetta, 2015. "Forecasting Tail Risks," CESifo Working Paper Series 5286, CESifo.
    • Handle: RePEc:ces:ceswps:_5286
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    References listed on IDEAS

    as
    1. Tilmann Gneiting & Roopesh Ranjan, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 411-422, July.
    2. Gneiting, Tilmann & Ranjan, Roopesh, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 411-422.
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    Keywords

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    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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