We address to what extent a central bank can de-risk its balance sheet by unconventional monetary policy operations. To that end, we propose a novel risk measurement framework to empirically study the time variation in central bank portfolio credit risks associated with such operations. The...

We propose a dynamic semi-parametric framework to study time variation in tail parameters. The framework builds on the Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a conditional framework to allow for time-variation...

We propose an empirical framework to assess joint and conditional probabilities of credit events from CDS prices observed in the market. Our model is based on a dynamic skewed-t distribution that captures many salient features of CDS data, including skewed and heavy-tailed changes in the price...

We address the question to what extent a central bank can de-risk its balance sheet by unconventional monetary policy operations. To this end, we propose a novel risk measurement framework to empirically study the time-variation in central bank portfolio credit risks associated with such...

We develop a theoretical model in which farmers' adoption decisions are based on their degree of risk aversion and on the information about the quality of a new technology received from other farmers. To test the predictions of this model, we run a field experiment in Bangladesh. We show that...

We use text analysis and a novel dataset to measure the sentiment component of central bank communications in 23 countries over the 2002-2017 period. Our analysis yields three key results. First, using directed networks, we show that comovement in sentiment across central banks is not reducible...