We study business uncertainty in high- versus low-volatility environments by surveying over 31,000 managers across 41 … their mean absolute deviations. Analogously, we measure realized volatility using absolute forecast errors. We establish two … new facts. (1) Subjective uncertainty and realized volatility both decline with GDP per capita. (2) Managers underestimate …

(systemic) risks. Forecasts are obtained from: (a) autoregressive and factor-augmented VARs with linear GARCH volatility (FAVARs … the period 1972:1-2014:12 to forecasts our tail risk indicators with each model in pseudo-real time. Our key finding is …

We examine how the tail risk of currency returns over the past 20 years were impacted by central bank (monetary and … effects last for up to 1 month, and are proportionally higher for joint QE actions. This cross-border source of tail risk is …

We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high … EV models, especially in the developed markets. -- value at risk ; expected shortfall ; hybrid historical simulation … ; extreme value theory ; bootstrapping …

In this study, we compare the out-of-sample forecasting performance of several modern Value-at- Risk (VaR) estimators … derived from extreme value theory (EVT). Specifically, in a multi-asset study covering 30 years of stock, bond, commodity and …

exert a large and persistent effect on the volatility of stock returns of acquirers and that this response is crucially … - engender a positive response in acquirers' volatility. Our results suggest that acquisitions affect uncertainty because they …

In accordance with Basel Capital Accords, the Capital Requirements (CR) for market risk exposure of banks is a … nonlinear function of Value-at-Risk (VaR). Importantly, the CR is calculated based on a bank’s actual portfolio, i.e. the … Conditional Heteroscedasticity (GARCH) volatility model. The optimization was performed by employing a Nondominated Sorting …