inference that feature varying level of trade-off between estimation precision and computational speed. Using monthly data for … measures, resulting from the new model, can be used to implement joint risk scenario analysis …

We analyze output growth risk with respect to financial conditions across U.S. manufacturing industries. Using a multi …-level quantile regression approach, we find strong heterogeneity in growth risk, particularly between the more vulnerable durable …

decompositions, derives a network risk model for a portfolio of assets. As a normalized measure of the sum of variance contributions … deriving the network risk model, the portfolio covariance matrix is decomposed to obtain the network-driven component of the … both the variance and covariance decompositions. In a third step, using quantile regressions, the proposed network risk …

This paper proposes a Skewed Stochastic Volatility (SSV) model to model time varying, asymmetric forecast distributions … to estimate Growth at Risk as introduced in Adrian, Boyarchenko, and Giannone's (2019) seminal paper "Vulnerable Growth … volatility and asymmetric measurement densities. Estimating the model based on US data yields conditional forecast densities that …

We investigate the extent to which various structural risks exacerbate the materialization of cyclical risk. We use a … role in explaining the severity of cyclical and credit risk materialization during financial cycle contractions. Among …

rate risk shock increases by 63 percent and the contribution of interest rate risk shocks to business cycle volatility more …Jesús Fernández-Villaverde, Pablo A. Guerrón-Quintana, Juan F. Rubio-Ramírez and Martín Uribe (2011) find that risk … than doubles. Hence, risk matters more in the recalibrated model. However, the recalibrated model does worse in capturing …

In aftermath of the Financial Crisis, some risk management practitioners advocate wider adoption of Bayesian inference … to replace Value-at-Risk (VaR) models for minimizing risk failures (Borison & Hamm, 2010). They claim reliance of …-Bayesian and [increasingly] Bayesian – continues to be a key methodological foundation of risk management and regulation related …

Financial risk managers routinely use non-linear time series models to predict the downside risk of the capital under … prediction and the evaluation of downside risk. Emphasis is given to the two key financial downside risk measures: Value-at-Risk …