The aggregate default probability is significantly priced in equities because of its close relation with uncertainty. Ceteris paribus, the aggregate default probability positively predicts stock market returns, and loadings on its changes correlate negatively with the cross-section of expected stock returns. These findings are consistent with multifactor models in which aggregate uncertainty is a determinant of the conditional equity premium and innovations in aggregate uncertainty are a systematic risk factor. By contrast, credit spreads have negligible explanatory power for expected stock returns. Contrary to the conventional wisdom, credit spreads are poor proxies for aggregate credit risk.

Using options-implied variance, a forward-looking measure of conditional variance, we revisit the debate on the idiosyncratic risk-return relation. In both cross-sectional (for individual stocks) and time-series (for the market index) regressions, we find a negative relation between options-implied variance and future stock returns. Consistent with Miller’s (1977) divergence of opinion hypothesis, the negative relation gets stronger (1) for stocks with more stringent short-sale constraints or (2) when shorting stocks becomes more difficult. Moreover, the negative correlation of realized idiosyncratic variance or analyst forecast dispersion with future stock returns mainly reflects their close correlation with our conditional idiosyncratic variance measure.

Abstract We propose a consumption-based model to explain puzzling unstable (i.e., sometimes positive and sometimes negative) relations between stock market variance with both stock market risk premia and prices. In the model, market risk premia depend positively (negatively) on fear (euphoria) variance. Market prices, which decrease with discount rates, correlate negatively (positively) with fear (euphoria) variance. Because it is the sum of fear and euphoria variances, the market variance may correlate positively or negatively with expected returns and prices, depending on the relative importance of the two variances. Our empirical results support the model’s key assumptions and many novel implications.

Abstract A spurious positive relation between exponential generalized autoregressive conditional heteroskedasticity (EGARCH) estimates of expected month t idiosyncratic volatility and month t stock returns arises when the month t return is included in estimation of model parameters. We illustrate via simulations that this look-ahead bias is problematic for empirically observed degrees of stock return skewness and typical monthly return time series lengths. Moreover, the empirical idiosyncratic risk-return relation becomes negligible when expected month t idiosyncratic volatility is estimated using returns only up to month t − 1.

We investigate the relative importance of various bankruptcy predictors commonly used in the existing literature by applying a variable selection technique, the least absolute shrinkage and selection operator (LASSO), to a comprehensive bankruptcy database. Over the 1980–2009 period, LASSO admits the majority of Campbell et al. (2008) predictive variables into the bankruptcy forecast model. Interestingly, by contrast with recent studies, some financial ratios constructed from only accounting data also contain significant incremental information about future default risk, and their importance relative to that of market-based variables in bankruptcy forecasts increases with prediction horizons. Moreover, LASSO-selected variables have superior out-of-sample predictive power and outperform (1) those advocated by Campbell et al. (2008) and (2) the distance to default from Merton’s (1974) structural model.

We model conditional market beta and alpha as flexible functions of state variables identified via a formal variable-selection procedure. In the post-1963 sample, the beta of the value premium comoves strongly with unemployment, inflation, and the price–earnings ratio in a countercyclical manner. We also uncover a novel nonlinear dependence of alpha on business conditions: It falls sharply and even becomes negative during severe economic downturns but is positive and flat otherwise. The conditional capital asset pricing model (CAPM) performs better than the unconditional CAPM, but this does not fully explain the value premium. Our findings are consistent with a conditional CAPM with rare disasters.

Abstract We uncover a positive stock market risk-return tradeoff after controlling for the covariance of market returns with the value premium. Fama and French (1996) conjecture that the value premium proxies for investment opportunities; therefore, by ignoring it, early specifications suffer from an omitted variable problem that causes a downward bias in the risk-return tradeoff estimation. We also document a positive relation between the value premium and its conditional variance, and the estimated conditional value premium is strongly countercyclical. The latter evidence supports the view that value is riskier than growth in bad times, when the price of risk is high.

There is mixed evidence of a positive relationship between the stock market risk and return. We reexamine this critical implication of asset pricing theory using fresh data from China's stock market, which is largely segmented from the rest of the global financial market. Using formal variable selection methods and a comprehensive set of predictor variables, we identify conditional market variance, scaled market prices, and inflation as crucial determinants of equity premiums. The estimated simple risk-return relationship exhibits downward omitted variable bias, which underlines the importance of considering multiple factors to explain the variation in equity premiums. We cannot wholly attribute the three-factor conditional equity premium model to data mining, as Guo, Sanni, and Yu (2022) select the same model for the U.S. stock market. These findings challenge existing asset pricing models and provide valuable guidance for future theoretical research.

We investigate the international transmission of inflation among G-7 countries using data-determined vector autoregression analysis, as advocated by Swanson and Granger [Swanson, N., Granger, C., 1997. Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions. Journal of the American Statistical Association 92, 357–367]. Over the period 1973–2003, we find that unexpected changes in US inflation have large effects on inflation in other countries, although they are not always the dominant international factor. Similarly, shocks to some other countries also have a statistically and economically significant influence on US inflation. Moreover, our evidence indicates that US inflation has become less vulnerable to foreign shocks since the early 1990s, mainly because of the diminished influence from Germany and France.