We propose a methodology that can efficiently measure the Value-at-Risk (VaR) of large portfolios with time-varying volatility and correlations by bringing together the established historical simulation framework and recent contributions to the dynamic factor models literature. We find that the proposed methodology performs well relative to widely used VaR methodologies, and is a significant improvement from a computational point of view.

We use supervisory data to investigate the ex-ante credit risk taken by different types of lenders in the U.S. syndicated term loan market during the LSAPs period. We find that nonbank lenders, mutual funds and structured-finance vehicles, take higher risk when longer-term interest rates decrease. The results are stronger for mutual funds that charge higher fees. Banks accommodate other lenders’ investment choices by originating riskier loans and selling them off. These results are consistent with “search for yield” by nonbanks and with a risk-taking channel of monetary policy. Over the sample we study, lower longer-term interest rates appear to have only a minimal effect on loan spreads.