At-risk measures and financial stability

Abstract

Financial stability is aimed at preventing and mitigating systemic risk, which is largely associated to the tail risk of macrofinancial variables. In this context, policy makers need to consider not only the most likely (central tendency) future path of macrofinancial variables, but also the distribution of all possible outcomes about that path, and focus on the downside risk. Against this background, the so-called at-risk methods provide a useful framework for the assessment of financial stability by the recognition of non-linear effects on the distribution of macrofinancial variables. We describe the use of quantile regressions for this purpose and illustrate two empirical applications related to the house prices and the GDP, from which useful insights for policymakers are derived.