Staff Working Paper No. 1,084
By Fernando Eguren-Martin, Sevim Kösem, Guido Maia and Andrej Sokol
We propose a novel approach to extract factors from large data sets that maximise covariation with the quantiles of a target distribution of interest. From the data underlying the Chicago Fed’s National Financial Conditions Index, we build targeted financial conditions indices for the quantiles of future US GDP growth. We show that our indices yield considerably better out-of-sample density forecasts than competing models, as well as insights on the importance of individual financial series for different quantiles. Notably, leverage indicators appear to co-move more with the median of the predictive distribution, while credit and risk indicators are more informative about downside risks.
Targeted financial conditions indices and growth-at-risk
