Assessing aggregate macroeconomic risk and the impact for the business and financial cycles

The thesis aims to introduce a measure to assess aggregate macroeconomic risk in the US economy. Existing literature has consistently highlighted that macroeconomic fluctuations exhibit asymmetries, particularly emphasizing that recessions tend to be more severe and pronounced than expansions. However, most studies primarily focus on measuring the expected asymmetry of individual macroeconomic variables. Instead, my work aims at developing a measure that summarizes aggregate risk at the macroeconomic level. First, a data-driven risk measure was calculated using a quantile factor model framework, which allows to analyze the comovements in a large set of macro and financial indicators at different quantiles. Second, a VAR analysis was conducted to evaluate the impact of changing risks on the dynamics of business and financial cycles. The main finding is that the risk measure I developed is effective in capturing fluctuations in both macroeconomic and financial variables, highlighting the importance of using a model that accounts for multiple dependent variables and captures their interrelationships and dynamics across all quantiles. The approach proposed in the thesis is more effective in capturing time-varying aggregate risk and its effects on the business and financial cycles compared to approaches that look at evolving risks for series taken in isolation.