We provide empirical evidence for the existence, magnitude, and economic cost of stigma associated with banks borrowing from the Federal Reserve's Discount Window (DW) during the 2007–2008 financial crisis. We find that banks were willing to pay a premium of around 44 basis points (bps) across funding sources (126bps after the bankruptcy of Lehman Brothers) to avoid borrowing from the DW. DW stigma is economically relevant as it increased some banks' borrowing cost by 32bps of their pre-tax return on assets (ROA) during the crisis. The implications of our results for the provision of liquidity by central banks are discussed.

We estimate unsmoothed private equity net asset values (NAVs) at weekly frequency for individual funds. Using simulations and large samples of buyout and venture funds, we show that our method yields superior estimates of NAVs relative to simple approaches based on extrapolation of reported NAVs. The market beta of an average buyout (venture) fund is around 1.0 (1.4), and the total risk is 33% (40%) per year. The risk-return profile of the funds varies significantly over time and across funds. Risk-taking and reporting quality appear to persist by manager.

We revisit the relation between stock market volatility and macroeconomic activity using a new class of component models that distinguish short-run from long-run movements. We formulate models with the long-term component driven by inflation and industrial production growth that are in terms of pseudo out-of-sample prediction for horizons of one quarter at par or outperform more traditional time series volatility models at longer horizons. Hence, imputing economic fundamentals into volatility models pays off in terms of long-horizon forecasting. We also find that macroeconomic fundamentals play a significant role even at short horizons.

We evaluate the performance of two popular systemic risk measures, CoVaR and SRISK, during eight financial panics in the era before FDIC insurance. Bank stock price and balance sheet data were not readily available for this period. We rectify this shortcoming by constructing a novel dataset for the New York banking system before 1933. Our evaluation exercise focuses on two challenges: ranking systemically important financial institutions (SIFIs) and financial crisis prediction. We find that CoVaR and SRISK meet the SIFI ranking challenge. That is, they help identify systemic institutions in periods of distress beyond what is explained by standard risk measures up to six months before panics. In contrast, aggregate CoVaR and SRISK are only somewhat effective at predicting financial crises.