Please use this identifier to cite or link to this item:
Title: Daily Growth at Risk: financial or real drivers? The answer is not always the same
Author: Chuliá Soler, Helena
Garrón, Ignacio
Uribe Gil, Jorge Mario
Keywords: Risc (Economia)
Valor (Economia)
Aprenentatge automàtic
Variables aleatòries
Value (Economics)
Machine learning
Random variables
Issue Date: 2022
Publisher: Universitat de Barcelona. Facultat d'Economia i Empresa
Series/Report no: [WP E-IR22/08]
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.
Note: Reproducció del document publicat a:
It is part of: IREA – Working Papers, 2022, IR22/08
Appears in Collections:Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))

Files in This Item:
File Description SizeFormat 
IR22_008_Chulia+Garron+Uribe.pdf6.26 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons