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When using high-frequency data, the conditional capital asset pricing model (CAPM) can explain asset-pricing anomalies. Using conditional betas based on daily data, the model works reasonably well for a recent sample period. However, it fails to explain the size anomaly as well as three out of six of the anomaly component excess returns. Using high-frequency betas, the conditional CAPM is able to explain the size, value, and momentum anomalies. We further show that high-frequency betas provide more accurate predictions of future betas than those based on daily data. This result holds for both the time-series and the cross-sectional dimensions. This paper was accepted by Karl Diether, finance.

PurposeThis paper empirically investigates the effect of the coronavirus pandemic (COVID-19) on the Indian financial market and firm betas, perhaps the first paper to do so. The results will be helpful for investors tracking betas during future the coronavirus waves.Design/methodology/approachA conditional capital asset pricing model (CAPM) and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model is used to estimate time-varying daily betas of the 50 largest Indian stocks spread across 16 industries over five years (Nov 2017 to May 2021), including the two waves of COVID-19 in India.FindingsThe results show that the betas increased during the COVID wave-1 (2020) but not during COVID wave-2 (2021). Moreover, the increase is more pronounced for consumer goods, infrastructure, insurance and information technology, unlike energy (oil and gas, power and mining) industries. Further, there are positive abnormal residual returns during the COVID waves. The results will be helpful for investors tracking betas during future COVID-19 waves.Originality/valueThis is perhaps the first paper to study the firm betas in light of the COVID-19 pandemic.

This paper derives and tests an intertemporal capital asset pricing model (ICAPM) based on a conditional version of the Campbell-Vuolteenaho two-beta ICAPM (bad beta, good beta (BBGB)). The novel factor is a scaled cash-flow factor that results from the interaction between cash-flow news and a lagged state variable (market dividend yield or consumer price index inflation). The cross-sectional tests over 10 portfolios sorted on size, 10 portfolios sorted on book-to-market, and 10 portfolios sorted on momentum show that the scaled ICAPM explains relatively well the dispersion in excess returns on the 30 portfolios. The results for an alternative set of equity portfolios (25 portfolios sorted on size and momentum) show that the scaled ICAPM prices particularly well the momentum portfolios. Moreover, the scaled ICAPM compares favorably with alternative asset pricing models in pricing both sets of equity portfolios. The scaled factor is decisive to account for the dispersion in average excess returns between past winner and past loser stocks. More specifically, past winners are riskier than past losers in times of high price of risk. Therefore, a time-varying cash-flow beta/price of risk provides a rational explanation for momentum. This paper was accepted by Wei Xiong, finance.

We uncover a strong comovement of the stock market risk—return trade-off with the consumption—wealth ratio (CAY). The finding reflects time-varying investment opportunities rather than countercyclical aggregate relative risk aversion. Specifically, the partial risk—return trade-off is positive and constant when we control for CAY as a proxy for investment opportunities. Moreover, conditional market variance scaled by CAY is negatively priced in the cross-section of stock returns. Our results are consistent with a limited stock market participation model, in which shareholders require an illiquidity premium that increases with CAY, in addition to the risk premium that is proportional to conditional market variance.

This article develops a new methodology for estimating and testing conditional factor models in finance. We propose a two-stage procedure that naturally unifies the two existing approaches in the finance literature-the parametric approach and the nonparametric approach. Our combined approach possesses important advantages over both methods. Using our two-stage combined estimator, we derive new test statistics for investigating key hypotheses in the context of conditional factor models. Our tests can be performed on a single asset or jointly across multiple assets. We further propose a novel test to directly check whether the parametric model used in our first stage is correctly specified. Simulations indicate that our estimates and tests perform well in finite samples. In our empirical analysis, we use our new method to examine the performance of the conditional capital asset pricing model (CAPM), which has generated controversial results in the recent asset-pricing literature.

Several recent articles find that stocks with high probabilities of bankruptcy or default earn anomalously low returns and negative unconditional capital asset pricing model (CAPM) alphas in the post-1980 period. I show that the conditional CAPM resolves the performance difference between high- and low-distress stocks. In particular, financially distressed stocks have relatively low exposure to market risk during bad economic times. I help to explain these findings through a theoretical model in which a levered firm's equity beta is negatively related to uncertainty about the unobserved value of its underlying assets. This paper was accepted by Wei Xiong, finance.

Testing the validity of the conditional capital asset pricing model (CAPM) is a puzzle in the finance literature. Lewellen and Nagel[14] find that the variation in betas and in the equity premium would have to be implausibly large to explain important asset-pricing anomalies. Unfortunately, they do not provide a rigorous test statistic. Based on a simulation study, the method proposed in Lewellen and Nagel[14] tends to reject the null too frequently. We develop a new test procedure and derive its limiting distribution under the null hypothesis. Also, we provide a Bootstrap approach to the testing procedure to gain a good finite sample performance. Both simulations and empirical studies show that our test is necessary for making correct inferences with the conditional CAPM.

Recent empirical work suggests that predictability of future returns is related to a time-varying component that expected returns exhibit. In this paper, I use conditional asset pricing models to investigate whether return anomalies exhibit common dynamic patterns in returns. The prediction of a model might hinge on the specific interaction between its underlying state variables and considered portfolios. Using well known anomalies and alternative state variables I study such interaction. I document that different state variables identify similar time-varying behavior for the anomalies in extreme economic conditions, but such anomalies show no commonalities in their overall patterns.

The article presents an empirical validation for mean-variance CAPM, using a Downside and Higher-moment framework of CAPM in the Russian stock market. The authors test the unconditional and conditional CAPM specifications on a sample of weekly returns of the most liquid Russian stocks over the financially stable period of 2004–2007 and over the crisis period of 2008–2009. The primary contribution of this study is ranking the models with respect to their explanatory power of cross-sectional return variations. The unconditional classical CAPM (where market risk is approximated by the beta coefficient) is compared to the downside (mean-semivariance) CAPM extended to incorporate the third (skewness) and fourth (kurtosis) moments. The ranking methodology is based on Fama and MacBeth’s (1973) two-stage estimation procedure. The unconditional CAPMs prove to have low explanatory power for the financially stable period and test results that are not statistically significant for the crisis period. Incorporating additional risk measures of the third and fourth moments and adopting one-sided risk measures only slightly increases the explanatory power. The highest explanatory power is offered by the unconditional CAPM of the Harlow-Rao downside systematic risk measure with zero benchmark. Our study confirms the feasibility of employing conditional CAPMs extended for systematic asymmetry (co-skewness) and systematic kurtosis (co-kurtosis) for the Russian stock market since these models display better explanatory power for cross-sectional return variations.