Recent studies conflict sharply about the stock returns of financially distressed firms. Both the basic empirical pattern and interpretation have been challenged. This study addresses both critiques. Analyzing about 4.3 million firm-months observations in 38 countries from January 1992 to June 2013, we find a strong, negative link between credit risk and subsequent equity returns, concentrated among low-capitalization stocks in developed countries in North America and Europe. Comparisons between countries reveal: 1) no relation to creditor rights, inconsistent with theories based on shareholders expropriation, but 2) a strong, positive relation to individualism, a proxy for investor overconfidence. Additional analysis using within-country proxies for investor overconfidence further supports an overconfidence-based explanation.

This study investigates the effects of ownership change on the performance and exposure to risk of 60 Indonesian commercial banks over the period 2005–2012. We find that state-owned banks tend to be less profitable and more exposed to risk than private and foreign banks. Domestic investors tend to select the best performers for acquisition. Domestic acquisition is generally associated with a decrease in the efficiency of the acquired banks. Non-regional foreign acquisition is associated with a reduction in risk exposure. Acquisition by regional foreign investors is associated with performance gains.

Forecasting the risk of extreme losses is an important issue in the management of financial risk. There has been a great deal of research examining how option implied volatilities (IV) can be used to forecast asset return volatility. However, the role of IV in the context of predicting extreme risk has received relatively little attention. The potential benefit of IV in forecasting extreme risk is considered within a range of models beginning with the traditional GARCH based approach, along with a number of novel point process models. Univariate models where IV is included as an exogenous variable are considered along with a novel bivariate approach where extreme movements in IV are treated as another point process. It is found that in the context of forecasting Value-at-Risk, the bivariate models produce the most accurate forecasts across a wide range of scenarios.

This paper provides an alternative approach to penalized regression for model selection in the context of high‐dimensional linear regressions where the number of covariates is large, often much larger than the number of available observations. We consider the statistical significance of individual covariates one at a time, while taking full account of the multiple testing nature of the inferential problem involved. We refer to the proposed method as One Covariate at a Time Multiple Testing (OCMT) procedure, and use ideas from the multiple testing literature to control the probability of selecting the approximating model, the false positive rate, and the false discovery rate. OCMT is easy to interpret, relates to classical statistical analysis, is valid under general assumptions, is faster to compute, and performs well in small samples. The usefulness of OCMT is also illustrated by an empirical application to forecasting U.S. output growth and inflation.

We investigate determinants of driving speed in large U.S. cities. We first estimate city-level supply functions for travel in an econometric framework where the supply and demand for travel are explicit. These estimations allow us to calculate an index of driving speed and to rank cities by driving speed. Our data suggest that a congestion tax of about 3.5 cents per kilometer yields welfare gains of about $30 billion per year, that centralized cities are slower, that cities with ring roads are faster, and that the provision of automobile travel in cities is subject to decreasing returns to scale.

Basu and Bundick, 2017 showed an intertemporal preference volatility shock has meaningful effects on real activity in a New Keynesian model with Epstein and Zin, 1991 preferences. We show that when the distributional weights on current and future utility in the Epstein–Zin time aggregator do not sum to 1, there is an asymptote in the responses to such a shock with unit intertemporal elasticity of substitution. In the Basu–Bundick model, the intertemporal elasticity of substitution is set near unity and the preference shock only hits current utility, so the sum of the weights differs from 1. We show that when we restrict the weights to sum to 1, the asymptote disappears and preference volatility shocks no longer have large effects. We examine several different calibrations and preferences as potential resolutions with varying degrees of success.

We propose a novel method to estimate loss aversion together with risk aversion and subjective probability weighting in a reference-dependent utility. Using multiple asset allocations in the 31 OECD pension funds, we find that our estimates of loss aversion and subjective probability weights are similar to those reported by Wang et al. (2017) and Rieger et al. (2011), respectively, despite the differences in the estimation methods. However, loss aversion increases with wealth and only Hofstede's Individualism is positively related to loss aversion. Countries with high individualism or masculinity prefer high risk and high return assets to bonds, whereas countries that dislike uncertainty prefer bonds to risky assets.

We investigate whether US government spending multipliers are higher during periods of economic slack or when interest rates are near the zero lower bound. Using new quarterly historical US data covering multiple large wars and deep recessions, we estimate multipliers that are below unity irrespective of the amount of slack in the economy. These results are robust to two leading identification schemes, two different estimation methodologies, and many alternative specifications. In contrast, the results are more mixed for the zero lower bound state, with a few specifications implying multipliers as high as 1.5.

Suppose that, when evaluating two alternatives x and y by means of a parametric utility function, low values of the parameter indicate a preference for x and high values indicate a preference for y. We say that a stochastic choice model is monotone whenever the probability of choosing x is decreasing in the preference parameter. We show that the standard use of random utility models in the context of risk and time preferences may sharply violate this monotonicity property and argue that their use in preference estimation may be problematic. They may pose identification problems and could yield biased estimations. We then establish that the alternative random parameter models are always monotone.

Policy uncertainty reduces individual welfare when individuals have limited opportunities to mitigate or insure against the resulting consumption fluctuations. We field an original survey to measure the degree of perceived policy uncertainty in Social Security benefits and to estimate the impact of this uncertainty on individual welfare. Our central estimates show that on average individuals are willing to forgo 6 percent of the benefits they are supposed to get under current law to remove the policy uncertainty associated with their future Social Security benefits. This translates to a risk premium from policy uncertainty equal to 10 percent of expected benefits.