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Daily Growth at Risk: financial or real drivers? The answer is not always the same

Helena Chuliá (), Ignacio Garrón () and Jorge Uribe ()
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Ignacio Garrón: Departament d’Econometria, Estadística i Economia Aplicada, Universitat de Barcelona (UB).

No 202208, IREA Working Papers from University of Barcelona, Research Institute of Applied Economics

Abstract: We estimate Growth-at-Risk (GaR) statistics for the US economy using daily regressors. We show that the relative importance, in terms of forecasting power, of financial and real variables is time varying. Indeed, the optimal forecasting weights of these types of variables were clearly different during the Global Financial Crisis and the recent Covid-19 crisis, which reflects the dissimilar nature of the two crises. We introduce the LASSO and the Elastic Net into the family of mixed data sampling models used to estimate GaR and show that these methods outperform past candidates explored in the literature. The role of the VXO and ADS indicators was found to be very relevant, especially in out-of-sample exercises and during crisis episodes. Overall, our results show that daily information for both real and financial variables is key for producing accurate point and tail risk nowcasts and forecasts of economic activity.

Keywords: Vulnerable growth; Quantiles; Machine learning; Forecasting; Value at risk. JEL classification: E27; E44; E66. (search for similar items in EconPapers)
Pages: 53 pages
Date: 2022-06, Revised 2022-06
New Economics Papers: this item is included in nep-big, nep-fdg and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:ira:wpaper:202208

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