We use supervisory data to investigate risk taking in the U.S. syndicated loan market at a time when longer-term interest rates are exceptionally low, and we study the ex-ante credit risk of loans acquired by different types of lenders, including banks and shadow banks. We find that insurance companies, pension funds, and, in particular, structured-finance vehicles take higher credit risk when investors expect interest rates to remain low. Banks originate riskier loans that they tend to divest shortly after origination, thus appearing to accommodate other lenders' investment choices. These results are consistent with a "search for yield" by certain types of shadow banks and, to the extent that Federal Reserve policies affected longer-term rates, the results are also consistent with the presence of a risk-taking channel of monetary policy. Finally, we find that longer-term interest rates have only a modest effect on loan spreads.

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We evaluate the short-horizon predictive ability of financial conditions indexes for stock returns and macroeconomic variables. We find reliable predictability only when the sample includes the 2008 financial crisis, and we argue that this result is driven by tailoring the indexes to the crisis and by nonsynchronous trading. In addition, we suggest a simple procedure for aggregating the various indexes into a single proxy for financial conditions, which can help to reduce the uncertainty faced by policymakers when monitoring financial conditions.

Trading portfolios at financial institutions are typically driven by a large number of financial variables. These variables are often correlated with each other and exhibit by time-varying volatilities. We propose a computationally efficient Value-at-Risk (VaR) methodology based on Dynamic Factor Models (DFM) that can be applied to portfolios with time-varying weights, and that, unlike the popular Historical Simulation (HS) and Filtered Historical Simulation (FHS) methodologies, can handle time-varying volatilities and correlations for a large set of financial variables. We test the DFM-VaR on three stock portfolios that cover the 2007-2009 financial crisis, and find that it reduces the number and average size of back-testing breaches relative to HS-VaR and FHS-VaR. DFM-VaR also outperforms HS-VaR when applied risk measurement of individual stocks that are exposed to systematic risk.

We evaluate the short horizon predictive ability of financial conditions indexes for stock returns and macroeconomic variables. We find reliable predictability only when the sample includes the 2008 financial crisis, and we argue that this result is driven by tailoring the indexes to the crisis and by non-synchronous trading. Financial conditions indexes are based on a variety of constituent variables and aggregation methods, and we discuss a simple procedure for consolidating the growing number of different indexes into a single proxy for financial conditions.