Foster and Hart (2009) introduce an objective measure of the riskiness of an asset that implies a bound on how much of one’s wealth is ‘safe’ to invest in the asset while (a.s.) guaranteeing no-bankruptcy. In this study, we translate the Foster–Hart measure from static and abstract gambles to dynamic and applied finance using nonparametric estimation of risk-neutral densities from S&P 500 call and put option prices covering 2003–2013. The dynamics of the resulting ‘option-implied Foster–Hart bound’ are assessed in light of other well-known option-implied risk measures including value at risk, expected shortfall and risk-neutral volatility, as well as high moments of the densities and several industry measures. Rigorous variable selection reveals that the new measure is a significant predictor of (large) ahead-return downturns. Furthermore, it grasps more characteristics of the risk-neutral probability distributions in terms of moments than other measures and exhibits predictive consistency. The robustness of the risk-neutral density estimation is analyzed via Monte Carlo methods.

Under the Fundamental Review of the Trading Book, capital charges are based on the coherent Expected Shortfall (ES) risk measure, which is sensitive to tail risk. We argue that backtesting of the forecasting models used to derive ES can be based on a multinomial test of Value-at-Risk (VaR) exceptions at several levels. Using simulation experiments with heavy-tailed distributions and GARCH volatility models, we design a statistical procedure to show that at least four VaR levels are required to obtain tests for misspecified trading book models that are more powerful than single-level (or even two-level) binomial exception tests. A traffic-light system for model approval is proposed and illustrated with three real-data examples spanning the 2008 financial crisis.

We investigate the nascent but fast-growing Chinese bond market and credit rating industry. We find Chinese bond ratings are informative and significantly correlated with bond offering yields. In addition, the Chinese bond investors distinguish ratings from different credit rating agencies (CRAs), demanding lower yields on bonds rated by global-partnered CRAs. However, the empirical results suggest that the rating scales used by Chinese CRAs are not comparable to those of international CRAs. Furthermore, Chinese CRAs have very broad rating scales and pool bonds with significantly different default risks into a single rating category, resulting in over 90% of bonds in only three rating categories.

This study examines the role of investor sentiment in the pricing dynamics between the spot and futures markets. The empirical evidence suggests that investor sentiment has a positive impact on price volatility and the bid–ask spread on both the spot and futures markets, which induces higher arbitrage risk and trading costs during high sentiment periods. Consequently, during high sentiment periods, informed traders become less willing to leverage their information advantages on the futures market, which diminishes the futures markets’ leading informational role and contributions to price discovery. Our findings provide support for the theory of limits to arbitrage.

We show evidence that consistent with category-learning behavior, investors allocate more attention to macroeconomic news than to firm-specific news, such as earnings announcements. Despite the distracting effect of macroeconomic news on investor attention, we find that earnings announcements with concurrent macroeconomic news announcements actually have significantly stronger immediate market response and weaker post-earnings announcement drift. We hypothesize that the combined total attention to macroeconomic news and earnings announcements helps investors understand both the systematic and firm-specific components of earnings surprises. Consistent with the hypothesis, our results show that the macroeconomic news effect is mainly driven by firms with high exposure to macroeconomic news. Moreover, we show that the effect is stronger when macroeconomic news contains more information and for firms with greater information uncertainty. Finally, we provide evidence that macroeconomic news helps reduce stock return uncertainty and enhance stock price efficiency.

Using a sample of U.S. firms during the 1989–2015 period, we study whether the efficiency with which managers generate revenue is sensitive to monitoring by institutional shareholders. We find that institutional ownership is positively related to managerial efficiency. Our identification relies on a discontinuity in ownership around the Russell 1000/2000 Index threshold and suggests that the positive effect of institutional ownership on managerial efficiency is causal. Furthermore, we document that monitoring by institutions helps improve managerial efficiency, and that an exogenous increase in institutional ownership leads to higher pay-for-performance sensitivity. Finally, we find consistent results after excluding from our sample forced CEO turnovers, suggesting that institutional shareholders force incumbent managers to exert greater effort rather than influence the replacement of less efficient CEOs. Taken together, our findings highlight the important role played by institutional shareholders in getting the most out of corporate executives.