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Title: Modeling and Forecasting Macroeconomic Downside Risk

Author(s): Andrea De Polis, Davide Delle Monache and Ivan Petrella

Publication Date: July 2020

Keyword(s): Business cycle, Downside risk, financial conditions, score driven models and Skewness

Programme Area(s): Monetary Economics and Fluctuations

Abstract: We investigate the relation between downside risk to the economy and the financial markets within a fully parametric model. We characterize the complete predictive distribution of GDP growth employing a Skew-t distribution with time-varying location, scale, and shape, for which we model both secular trends and cyclical changes. Episodes of downside risk are characterized by increasing negative asymmetry, which emerges as a clear feature of the data. Negatively skewed predictive distributions arise ahead and during recessions, and tend to be anticipated by tightening of financial conditions. Indicators of excess leverage and household credit outstanding are found to be significant drivers of downside risk. Moreover, the Great Recession marks a significant shift in the unconditional distribution of GDP growth, which has featured a distinct negative skewness since then. The model delivers competitive out-of-sample (point and density) forecasts, improving upon standard benchmarks, especially due to financial conditions providing a strong signal of increasing downside risk.

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Bibliographic Reference

De Polis, A, Delle Monache, D and Petrella, I. 2020. 'Modeling and Forecasting Macroeconomic Downside Risk'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=15109