Crash Sensitivity and the Cross Section of Expected Stock Returns

Chabi-Yo, Fousseni; Ruenzi, Stefan; Weigert, Florian

doi:10.1017/s0022109018000121

Cited by 139 publications

(80 citation statements)

References 130 publications

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“…ACX (2006) suggest that downside beta estimates tend to contain substantial measurement error when return volatility for a stock is high, and therefore, sorting stocks based on their downside betas reduces the precision of the portfolio analyses. This conjecture is also consistent with Chabi‐Yo et al (2018) who point out the difficulty of measuring downside betas in a reliable manner. Second, since downside beta can be expressed as the correlation between a stock's excess return and the market's excess return times the ratio of the standard deviation of the stock's excess return to that of the market's excess return conditional on down markets, there is a mechanical relation between downside beta and equity volatility.…”

Section: Resultssupporting

confidence: 90%

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Downside beta and the cross section of equity returns: A decade later

Atılgan

Demirtaş

Günaydın

2020

Euro Fin Management

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This study reexamines the relation between downside beta and equity returns in the United States. First, we replicate the 2006 work of Ang, Chen, and Xing who find a positive relation between downside beta and future equity returns for equal‐weighted portfolios of NYSE stocks. We show that this relation doesn't hold after using value‐weighted returns or controlling for various return determinants. We also extend the original sample, add AMEX/NASDAQ stocks or utilize alternative downside beta measures and still find no downside risk premium. We focus on factor analysis results, persistence of downside beta, and various subsamples to understand the economic reasons behind the findings.

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Section: Resultssupporting

confidence: 90%

“…Chabi-Yo et al (2018) differentiate between the concepts of crash risk and sensitivity towards market movements. We also calculate downside beta measures using the 5th and 3rd percentile of the distribution of market returns during the past year to better identify market crashes.…”

mentioning

confidence: 99%

Downside beta and the cross section of equity returns: A decade later

Atılgan

Demirtaş

Günaydın

2020

Euro Fin Management

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“…Instead, these investors would prefer assets that are crash-insensitive. In line with these arguments, Chabi-Yo et al (2014) provide evidence that crash-sensitive stocks bear a premium, while Meine et al (2015b) show that CDS spreads of banks bear a premium for tail risk during the financial crisis. This pricing of downside risk can be interpreted as a non-linear addendum to the CAPM asset pricing theory in which the sensitivity of asset prices to market movements is modeled linearly (Chabi-Yo et al, 2014).…”

Section: Introductionmentioning

confidence: 59%

“…We do not observe convergence to some factors in the literature. The recent literature on the impact of crash aversion on the pricing of individual financial instruments, including CDS, shows that crash-sensitive financial instruments bear a premium (Chabi-Yo et al, 2014;Meine et al, 2015b) which can be interpreted as a non-linear addendum to the CAPM asset pricing theory.…”

Section: Resultsmentioning

confidence: 99%

“…There is increasing evidence that investors demand compensation to redeem their fear of extreme tail events in financial markets and reward their risk-taking (see, e.g., Bollerslev and Todorov, 2011;Chabi-Yo et al, 2014;Meine et al, 2015b;Bollerslev et al, 2015;Ait-Sahalia and Lo, 2000;Jackwerth and Rubinstein, 1996). First evidence of investors being crash-averse was provided by Rubinstein (1994), but especially the recent literature studies the impact of crash aversion on the pricing of individual financial instruments.…”

Section: Introductionmentioning

confidence: 99%

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The determinants of CDS spreads: evidence from the model space

Pelster

Vilsmeier

2017

Rev Deriv Res

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The CDS market plays an important role in understanding the dynamics of financial markets and financial stability and consequently has received growing attention in the recent literature. Despite the rich literature there is still no consensus about which factors mainly drive CDS spreads. Instead we observe an increasingly large number of potential determinants being suggested. We aim to provide a transparent and robust analysis for the determinants of CDS spreads by focusing on the information provided by a large number of models (the "model space") instead of focusing on one or few "hand-picked" specifications. Our model space covers most of the specifications used elsewhere in the literature and includes several variables measuring the sensitivity of firms to extreme market movements (tail dependence). ContributionWe show that the model space includes models which allow backing many different theories by significant coefficients and good fit to the data. Thus, the information content from single model specifications is rather restricted. In contrast to previous studies, we use information from the entire model space instead of restricting ourselves to some "hand-picked" models. Doing so, we provide a transparent and robust analysis for the determinants of CDS spreads. Besides covering the informational content of most previous studies in our analysis we also give clear evidence on the impact of using different estimation procedures to measure tail dependence in CDS markets. ResultsUsing a large data-set, we find that CDS price dynamics can be mainly explained by factors describing firms' sensitivity to extreme market movements. More precisely, our results suggest that variables measuring tail dependence -based on so-called dynamic copula models -incorporate almost all essential pricing information making other potential determinants such as default risk (Merton-type) factors or variables capturing the systematic market evolutions negligible. The determinants of CDS spreads: evidence from the model space * Matthias Pelster Leuphana University of Lueneburg Johannes Vilsmeier Deutsche Bundesbank AbstractWe apply Bayesian Model Averaging and a frequentistic model space analysis to assess the pricing-determinants of credit default swaps (CDS). Our study focuses on the complete model space of plausible models covering most of the variables and specifications used elsewhere in the literature, including different copula models. The approach followed supports ultimate transparency and robustness for the empirical study at hand. Using a large data-set of CDS contracts we find that CDS price dynamics can be mainly explained by factors describing firms' sensitivity to extreme market movements. More precisely, our results suggest that dynamic copula based measures of tail dependence incorporate almost all essential pricing information making other potential determinants such as Merton-type factors or variables measuring the systematic market evolution -based on simple means or principal component analysis -negligible.

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COVID‐19 and tail risk contagion across commodity futures markets

Qiao

Han

2022

Journal of Futures Markets

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This paper examines the impact of COVID-19 on tail risk contagion across commodity futures markets using a copula-based network method. We document a significant increase in the lower and upper tail contagiousness of commodities following the COVID-19 outbreak. Contagion shows an obvious clustering characteristic, that is, there is higher tail risk connectedness between commodities in the same category. Agricultural commodities are significantly less contagious than metals and energy commodities; soft commodities in particular can offer investors significant diversification benefits. There are several hub commodities in the contagion network, chief among them copper, which are good transmitters of shocks and should be treated with caution by investors and regulators. Although tail risk and contagiousness of individual commodities increase together during the pandemic, we find a negative cross-sectional relationship between tail risk and contagiousness, that is, commodities with high tail risk are not necessarily highly contagious and may even be less so.

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Crash Sensitivity and the Cross Section of Expected Stock Returns

Cited by 139 publications

References 130 publications

Downside beta and the cross section of equity returns: A decade later

Downside beta and the cross section of equity returns: A decade later

The determinants of CDS spreads: evidence from the model space

COVID‐19 and tail risk contagion across commodity futures markets

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