Using structural credit risk models to estimate default dependence requires estimates of correlations of changes in distance-to-default. We present a structural model that yields simple relations between asset value, distance-to-default, and CDS spreads, allowing the correlations to be estimated from CDS spreads. We generalize the model to include a randomly varying default boundary; in this version the distance-to-default dynamics also depend on the movement of the default boundary. The CDS spread correlations we estimate exceed equity correlations, consistent with a randomly varying default boundary. We also present evidence that variations in funding liquidity affect the correlations, consistent with recent models.

We analyze the supply-side disruptions associated with COVID-19. We find that sectors in which a higher fraction of the workforce is not able to work remotely experienced greater declines in employment and expected revenue growth, worse stock market performance, and higher likelihood of default. The stock market overweights low-exposure industries. Thus, our findings cast light on the disconnect between stock market indices and aggregate outcomes. We combine these ex ante heterogeneous industry exposures with daily financial market data to create a stock return portfolio that tracks news about the supply-side disruptions resulting from the pandemic.

We document persistent superior trading performance among a subset of individual investors. Investors classified in the top performance decile in the first half of our sample subsequently earn risk-adjusted returns of about 6% per year. These returns are not confined to stocks in which the investors are likely to have inside information, nor are they driven by illiquid stocks. Our results suggest that skilled individual investors exploit market inefficiencies (or perhaps conditional risk premiums) to earn abnormal profits, above and beyond any profits available from well-known strategies based on size, value, momentum, or earnings announcements. (JEL G11, G14, G40, G51) Received: October 11, 2020 Editorial decision: January 4, 2021 Editor: Jeffrey Pontiff

We model, in a market-based democracy, different constituencies that disagree regarding the likelihood of economic disasters. Costly public policy initiatives to reduce or eliminate disasters are assessed relative to private alternatives presented by financial markets. Demand for such public policies falls as much as 40% with disagreement, and crowding out by private insurance drives most of the reduction. As support for disaster-reducing policy jumps in periods of disasters, costly policies may be adopted only after disasters occur. In some scenarios constituencies may even demand policies oriented at increasing disaster risk if these policies introduce speculative opportunities. Received September 25, 2017; Editorial decision September 3, 2018 by Editor: Thierry Foucault

We investigate an asset pricing model with preferences cycling between high risk aversion and low EIS in fall/winter and the reverse in spring/summer. Calibrating to consumption data and allowing plausible preference parameter values, we produce returns that match observed equity and Treasury returns across the seasons: risky returns are higher and risk-free returns are lower or stable in fall/winter, and they reverse in spring/summer. Further, risky returns vary more than risk-free returns. A novel finding is that both EIS and risk aversion must vary seasonally to match observed returns. Further, the degree of necessary seasonal change in EIS is small.

We identify the causal effects of short-selling bans on stock prices using regression discontinuity (RD). We exploit three threshold-based rules that determine a stock’s short-selling eligibility on the Hong Kong Stock Exchange. Short-selling bans have a large effect on short-selling volume at all thresholds. Despite this, bans do not affect stock prices. Stock returns, volatility, and crash risk are not different for banned versus unrestricted stocks when appropriate counterfactual stocks are used to measure a ban’s effects. Our findings suggest that short-selling bans are not as costly as previously argued, but are ineffective at reducing volatility or buttressing prices. Received September 13, 2017; editorial decision April 29, 2018 by Editor Jeffrey Pontiff.