This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting...

A decision maker tests whether the gradient of the loss function evaluated at a judgmental decision is zero. If the test does not reject, the action is the judgmental decision. If the test rejects, the action sets the gradient equal to the boundary of the rejection region. This statistical...

Bayesian decisions are observationally identical to decisions with judgment. Decisions with judgment test whether a judgmental decision is optimal and, in case of rejection, move to the closest boundary of the confidence interval, for a given confidence level. The resulting decisions condition...

To measure contagion empirically, we propose using a Bayesian time-varying coefficient model estimated with Markov ChainMonte Carlo methods. The proposed measure works in the joint presence of heteroskedasticity and omitted variables and does not require knowledge of the timing of the crisis. It...

We investigate the risk of holding credit default swaps (CDS) in the trading book and compare the Value at Risk (VaR) of a CDS position to the VaR for investing in the respective firm's equity using a sample of CDS stock price pairs for 86 actively traded firms over the period from March 2003 to...

We introduce a structural quantile vector autoregressive (VAR) model. Unlike standard VAR which models only the average interaction of the endogenous variables, quantile VAR models their interaction at any quantile. We show how to estimate and forecast multivariate quantiles within a recursive...

Liquidity has its systemic aspect that is frequently neglected in research and risk management applications. We build a model that focuses on systemic aspects of liquidity and its links with solvency conditions accounting for pertinent interactions between market participants in an agent-based...

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...