Why did the shadow banking sectors in the US and the euro area expand in the decade before the financial crisis and what are the implications for systemic risk and macro-prudential policy? This paper examines these issues with a model of the financial sector where the size of the shadow banking...

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...

Interconnectedness is an inherent feature of the modern financial system. While it con-tributes to efficiency of financial services, it also creates structural vulnerabilities: pernicious shock transmission and amplification impacting banks’ capitalization. This has recently been seen during...

This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey forecasts by optimally exploiting their properties. To do that, it compares the forecasting performance of optimal pooling and tilting techniques, including...

Severe financial turbulences are driven by high impact and low probability events that are the characteristic hallmarks of systemic financial stress. These unlikely adverse events arise from the extreme tail of a probability distribution and are therefore very poorly captured by traditional...

Any empirical analysis of the credit channel faces a key identification challenge: changes in credit supply and demand are difficult to disentangle. To address this issue, we use the detailed answers from the US and the confidential and unique Euro area bank lending surveys. Embedding this...

This paper proposes a new and robust methodology to obtain conditional density forecasts, based on information not contained in an initial econometric model. The methodology allows to condition on expected marginal densities for a selection of variables in the model, rather than just on future...

I propose a new model, conditional quantile regression (CQR), that generates density forecasts consistent with a specific view of the future evolution of some variables. This addresses a shortcoming of existing quantile regression-based models, for example the at-risk framework popularised by...