Forecasting Tail Risks

Nicoló, Gianni De; Lucchetta, Marcella

doi:10.1002/jae.2509

Cited by 51 publications

(22 citation statements)

References 46 publications

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“…For GDP growth, the BVAR-SV model captures some asymmetries around the Great Recession. 27 At the one-step horizon, the QR-based estimates actually display some upside asymmetry in longrise in the early or mid-1990s; this is not as evident in the BVAR-SV estimates. In addition, over the out-of-sample period, the differences in volatilities of shortfall versus long-rise are more modest than in the in-sample period.…”

Section: Predictive Distributionsmentioning

confidence: 94%

“…Building on a longer tradition in finance of assessing tail risks in asset prices and returns, a rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and has relied on quantile regression methods to estimate tail risks, as developed in Adrian, Boyarchenko, and Giannone (2019a), Adrian, et al (2018), De Nicolo and Lucchetta (2017), and Giglio, Kelly, and Pruitt (2016) and extended to vector autoregressive models in Chavleishvili and Manganelli (2019). This work has emphasized the link of tail risks to output stemming from poor financial conditions.…”

Section: Introductionmentioning

confidence: 99%

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Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

Carriero

Clark

Marcellino

2020

Working Paper (Federal Reserve Bank of Cleveland)

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A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and it has relied on quantile regression methods to estimate tail risks. Although much of this work discusses asymmetries in conditional predictive distributions, the analysis often focuses on evidence of downside risk varying more than upside risk. We note that this pattern in risk estimates over time could obtain with conditional distributions that are symmetric but subject to simultaneous shifts in conditional means (down) and variances (up). Building on that insight, we examine the ability of Bayesian VARs with stochastic volatility to capture tail risks in macroeconomic forecast distributions and outcomes. We consider both a conventional stochastic volatility specification and a specification with a common factor in volatility that enters the VAR's conditional mean. Even though the one-step-ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, the model estimates yield more time variation in downside risk as compared to upside risk. Results from the model that includes a volatility factor in the conditional mean and thereby allows for asymmetries in conditional distributions are very similar. Our paper also extends the recent literature by formally evaluating the accuracy of tail risk forecasts and assessing the performance of Bayesian quantile regression, as well as our Bayesian VARs, in this context. Overall, the BVAR models perform comparably to quantile regression for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis.

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Section: Predictive Distributionsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

Carriero

Clark

Marcellino

2020

Working Paper (Federal Reserve Bank of Cleveland)

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“…Determining both the threshold and the confidence is up to the investor, even before choosing the investment. Indeed, in banking analysis, the downside risk is particularly important, since "tail risk" is considered an important component in financial market analysis, as underlined by De Nicolò and Lucchetta (2017). The economic cycle may matter.…”

Section: Firms' Appraisalmentioning

confidence: 99%

Catch the Heterogeneity: The New Bank Tailored Integrated Rating

2018

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“…Two recent events, the financial crisis and the COVID-19 pandemic, have increased the interest in tail risks in macroeconomic outcomes. A fast-growing literature has focused on the risks of significant declines in GDP, with quantile regression as the main method to estimate tail risks (see, e.g., Adrian, Boyarchenko, and Giannone (2019); Adrian, et al (2018); Cook and Doh (2019); De Nicolò and Lucchetta (2017); Ferrara, Mogliani, and Sahuc (2019); Giglio, Kelly, and Pruitt (2016); González-Rivera, Maldonado, and Ruiz (2019); Delle Monache, De Polis, and Petrella (2020); Plagborg-Møller, et al (2020); Reichlin, Ricco, and Hasenzagl (2020); and Mitchell, Poon, and Mazzi (forthcoming)). For output growth, forecasting tail risks has some precedent in the literature on forecasting recessions or just periods of negative growth (see, e.g., Aastveit, Ravazzolo, and van Dijk (2018)).…”

Section: Introductionmentioning

confidence: 99%

Tail Forecasting with Multivariate Bayesian Additive Regression Trees

Clark

Huber

Koop

et al. 2021

Working Paper (Federal Reserve Bank of Cleveland)

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We develop novel multivariate time series models using Bayesian additive regression trees that posit nonlinear relationships among macroeconomic variables, their lags, and possibly the lags of the errors. The variance of the errors can be stable, driven by stochastic volatility (SV), or follow a novel nonparametric specification. Estimation is carried out using scalable Markov chain Monte Carlo estimation algorithms for each specification. We evaluate the real-time density and tail forecasting performance of the various models for a set of US macroeconomic and financial indicators. Our results suggest that using nonparametric models generally leads to improved forecast accuracy. In particular, when interest centers on the tails of the posterior predictive, flexible models improve upon standard VAR models with SV. Another key finding is that if we allow for nonlinearities in the conditional mean, allowing for heteroskedasticity becomes less important. A scenario analysis reveals highly nonlinear relations between the predictive distribution and financial conditions.

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Forecasting Tail Risks

Cited by 51 publications

References 46 publications

Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

Catch the Heterogeneity: The New Bank Tailored Integrated Rating

Tail Forecasting with Multivariate Bayesian Additive Regression Trees

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