Financial markets are central to the transmission of uncertainty shocks. This paper documents a new aspect of the interaction between the two by showing that uncertainty shocks have radically di¤erent macroeconomic implications depending on the state …nancial markets are in when they occur. Using monthly US data, we estimate a nonlinear VAR where economic uncertainty is proxied by the (unobserved) volatility of the structural shocks, and a regime change occurs whenever credit conditions cross a critical threshold. An exogenous increase in uncertainty has recessionary e¤ects in both good and bad credit regimes, but its impact on output is estimated to be …ve times larger when the economy is experiencing …nancial distress. Accounting for this nonlinearity, uncertainty accounts for about 1% of the peak fall in industrial production observed in

The global financial crisis of 2007-09 has illustrated the importance of including funding liquidity feedbacks in any model of systemic risk. This paper illustrates how we have incorporated such channels into a risk assessment model for systemic institutions (RAMSI), and it outlines the Bank of England's plans to use RAMSI to sharpen its assessment of institution-specific and systemwide All authors are with the Bank of England except Prasanna Gai, who is with the Australian National University, and Nada Mora, who is with the Federal Reserve Bank of Kansas City. The RAMSI project represents a major investment of Bank of England resources, and we are grateful to many people both inside and outside the Bank of England for their contributions. In particular, the National Bank of Austria has been very generous in providing guidance and significant analytical contributions. The analysis in this paper has benefited from encouragement and contributions from Viral Acharya, Niki

When do financial markets help in predicting economic activity? With incomplete markets, the link between financial and real economy is statedependent and financial indicators may turn out to be useful particularly in forecasting "tail" macroeconomic events. We examine this conjecture by studying Bayesian predictive distributions for output growth and inflation in the US between 1983 and 2012, comparing linear and nonlinear VAR models. We find that financial indicators significantly improve the accuracy of the distributions. Regime-switching models perform better than linear models thanks to their ability to capture changes in the transmission mechanism of financial shocks between good and bad times. Such models could have sent a credible advance warning ahead of the Great Recession. Furthermore, the discrepancies between models are themselves predictable, which allows the forecaster to formulate reasonable real-time guesses on which model is likely to be more accurate in the next future.JEL classification: C53, E32, E44, G01.

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