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

Author

Listed:
  • Helena Chuliá

    (Riskcenter, Institut de Recerca en Economia Aplicada (IREA), Departament d’Econometria, Estadística i Economia Aplicada, Universitat de Barcelona (UB).)

  • Ignacio Garrón

    (Departament d’Econometria, Estadística i Economia Aplicada, Universitat de Barcelona (UB).)

  • Jorge M. Uribe

    (Faculty of Economics and Business Studies, Open University of Catalonia.)

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.

Suggested Citation

  • Helena Chuliá & Ignacio Garrón & Jorge M. Uribe, 2022. ""Daily Growth at Risk: financial or real drivers? The answer is not always the same"," IREA Working Papers 202208, University of Barcelona, Research Institute of Applied Economics, revised Jun 2022.
    • Handle: RePEc:ira:wpaper:202208
    as

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    References listed on IDEAS

    as
    1. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    2. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Vulnerable growth; Quantiles; Machine learning; Forecasting; Value at risk. JEL classification: E27; E44; E66.;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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