Does Macro-Financial Information Matter for Growth at Risk Forecasting?

Finance master project by Lapo Bini and Daniel Mueck ’21

Charts show growth at risk predictions

Editor’s note: This post is part of a series showcasing Barcelona School of Economics master projects. The project is a required component of all BSE Master’s programs.


In order to analyse whether financial conditions are relevant downside risk predictors for the 5% Growth at Risk conditional quantile, we propose a Dynamic Factor- GARCH Model, comparing it to the two most relevant approaches in the literature. We conduct an out-of sample forecasting analysis on the whole sample, as well as focusing on a period of increased European integration after the 2000s. Always, including the national financial conditions index, term structure and housing prices for 17 European countries and the United States, as down side risk predictors. We find evidence of significant predicting power of financial conditions, which, if exploited correctly, becomes more relevant in times of extraordinary financial distress. 


We propose a Dynamic Factor-GARCH model which computes the conditional distribution of the GDP growth rates non-parametrically, exploiting the dimensions of a panel of national financial conditions and compare it to the models of Adrian, Boyarchenko, and Giannone (2016 )and Brownlees and Souza (2021) out-of-sample.

Contrasting to our in-sample results, the out-of sample results exhibit a higher degree of heterogeneity across countries. While our model performs at least as good or better as the AR(1)-GARCH(1,1) specification of Brownlees and Souza (2021) in the long run, it produces unsatisfactory results for the one-step forecast horizon.

However, by focusing our out-of-sample analysis on a smaller sample around the period of the Great Recession, we not only outperform the other two models analysed, but also obtain strong indication of increased importance and predictive power of financial conditions.

We provide evidence that by correctly modelling financial conditions, they not only exhibit predictive ability for GDP downside risk, but also improve in-sample GaR predictions. Further, we show that they are relevant out-of-sample predictors in the long run. Finally, when focusing on periods of extraordinary financial distress, like the Great Recession, financial conditions become even more relevant. However, the right model needs to be applied in order to exploit that predictive power.

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About the BSE Master’s Program in Finance

What can the risk neutral moments tell us about future returns?

Editor’s note: This post is part of a series showcasing Barcelona GSE master projects by students in the Class of 2015. The project is a required component of every master program.

Juan Imbet, Nuria Mata

Master’s Program:

Paper Abstract:

We test if the first four moments of the risk neutral distribution implicit in options’ prices predict market returns. We estimate the risk
neutral distribution of the S&P 500 over different frequencies using a non parametric polynomial fitting, and test if the first four moments of the distribution predict returns of the S&P 500. Our results suggest that there is no evidence on this predictability power.

Presentation Slides: