A growing empirical literature on how to measure uncertainty has emerged following the 2007-2008 financial crisis. This paper review the different methods measuring uncertainty. From a principal component analysis (PCA) including the various measures of uncertainty provided by this growing empirical literature, a monthly global measure of uncertainty for the United States on the period 1990-2015 has been developed and the factors explaining fluctuations in uncertainty have been determined. The US global measure from the PCA has similarities with a composite index from a dynamic factor model. The same methodology is used using euro area data. We find many similarities between US uncertainty peaks and the uncertainty peaks of the euro area. The second factor provides a switch between two natures of uncertainty: macroeconomic and financial. Finally, we extend our analysis adding a measure related to the pandemic risk to take into account the current COVID-19 pandemic.
|No. of pages:||93|