Assessing Macroeconomic Tail Risk

Abstract

What drives macroeconomic tail risk? To answer this question, we borrow a definition of macroeconomic risk from Adrian et al. (2019) by studying (left-tail) percentiles of the forecast distribution of GDP growth. We use local projections (Jordà, 2005) to assess how this measure of risk moves in response to economic shocks to the level of technology, monetary policy, and financial conditions. Furthermore, by studying various percentiles jointly, we study how the overall economic outlook-as characterized by the entire forecast distribution of GDP growth-shifts in response to shocks. We find that contractionary shocks disproportionately increase downside risk, independently of what shock we look at.

Multimodality in Macro-Financial Dynamics

“…Across data and methodologies, many papers have confirmed the stability of upside growth risk and the variability of its downside risk, often referred to as "vulnerable growth." These results have been validated with quantile regression(Adrian, Grinberg, Liang, and Malik, 2018), at higher frequencies(Ferrara, Mogliani, and Sahuc, 2019), conditionally on shocks(Loria, Matthes, and Zhang, 2018), with VAR with stochastic volatility affecting the conditional mean(Carriero, Clark, and Marcellino, 2018a;Caldara, Scotti, and Zhong, 2019), with Markov switching models(Doz, Ferrara, and Pionnier, 2019), in labor markets(Kiley, 2018), and in housing markets(Valckx, Deghi, Katagiri, Khadarina, and Shahid, 2019).…”

Forecasting Macroeconomic Risk in Real Time: Great and Covid-19 Recessions

“…βi,h τ , correspond to the quantiles τ of the predictive distribution of y i t+h conditional on x i t+1|Ωv . This non-linear approach acknowledges the skewed and fat-tailed nature of GDP growth, which is documented in, e.g., Fagiolo et al (2008), Williams et al (2017, Atalay et al (2018), Bloom et al (2018), Adrian et al (2019), Salgado et al (2019), Loria et al (2019), Carriero et al (2020 and Plagborg-Møller et al (2020). Moreover, the use of the conditional quantile function facilitates the evaluation of macroeconomic risk reflected by the lower percentiles of the quantile function of GDP growth: a fall in the lower percentiles of the conditional distribution of GDP growth indicates an increase in macroeconomic risk.…”

“…Moreover, the use of the conditional quantile function facilitates the evaluation of macroeconomic risk reflected by the lower percentiles of the quantile function of GDP growth: a fall in the lower percentiles of the conditional distribution of GDP growth indicates an increase in macroeconomic risk. Finally, the predictive power of the conditioning variables most likely exhibits substantial heterogeneity across percentiles of GDP growth distribution, which may be particularly true for financial variables in predicting the left tail of the distribution, as illustrated by, e.g., Giglio et al (2016), , Adrian et al (2019), Beutel et al (2019), Loria et al (2019), Chavleishvili and Manganelli (2019), Figueres and Jarociński (2020),…”

Modeling and Forecasting Macroeconomic Downside Risk

“…Loria et al (2019) show that contractionary shocks to technology, monetary policy, and financial conditions disproportionately increase downside risk to economic growth.…”