We document that even though the normal distribution provides a good approximation to GDP fluctuations, it severely underpredicts “macroeconomic tail risks,” that is, the frequency of large economic downturns. Using a multi-sector general equilibrium model, we show that the interplay of...

We document that even though the normal distribution provides a good approximation to GDP fluctuations, it severely underpredicts "macroeconomic tail risks," that is, the frequency of large economic downturns. Using a multi-sector general equilibrium model, we show that the interplay of...

We document that even though the normal distribution is a good approximation to the nature of aggregate fluctuations, it severely underpredicts the frequency of large economic downturns. We then provide a model that can explain these facts simultaneously. Our model shows that the propagation of...

We perform a large simulation study to examine the extent to which various generalized autoregressive conditional heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 stock market returns ranging from 1995-2014...

We propose a multivariate dynamic intensity peaks-over-threshold model to capture extreme events in a multivariate time series of returns. The random occurrence of extreme events exceeding a threshold is modeled by means of a multivariate dynamic intensity model allowing for feedback effects...