2019
DOI: 10.1111/jofi.12765
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Time‐Varying Asset Volatility and the Credit Spread Puzzle

Abstract: Most extant structural credit risk models underestimate credit spreads—a shortcoming known as the credit spread puzzle. We consider a model with priced stochastic asset risk that is able to fit medium‐ to long‐term spreads. The model, augmented by jumps to help explain short‐term spreads, is estimated on firm‐level data and identifies significant asset variance risk premia. An important feature of the model is the significant time variation in risk premia induced by the uncertainty about asset risk. Various ex… Show more

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Cited by 72 publications
(20 citation statements)
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References 85 publications
(181 reference statements)
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“…There remain forty firms in the sample. This is less than in studies that use short periods with discontinuous series (such as Arora, Bohn and Zu, 2005;Bao, 2009;and Bao and Pan, 2013), but similar to other studies that require continuous data (such as Christoffersen, Ericsson, Jacobs andJin, 2009, andDu, Elkamhi andEricsson, 2018). CDS data on prices and quotes are collected from Markit and checked against Bloomberg prices and quotes for the period January 2005 to December 2012.…”
Section: Data Samplementioning
confidence: 67%
See 1 more Smart Citation
“…There remain forty firms in the sample. This is less than in studies that use short periods with discontinuous series (such as Arora, Bohn and Zu, 2005;Bao, 2009;and Bao and Pan, 2013), but similar to other studies that require continuous data (such as Christoffersen, Ericsson, Jacobs andJin, 2009, andDu, Elkamhi andEricsson, 2018). CDS data on prices and quotes are collected from Markit and checked against Bloomberg prices and quotes for the period January 2005 to December 2012.…”
Section: Data Samplementioning
confidence: 67%
“…Cremers, Driessen, Maenhout and Weinbaum (2008) show how implied volatilities relate to bond spreads, while Cremers, Driessen and Maenhout (2009) estimate jump parameters for firm value and bond spreads using both individual and equity-index options. Carr and Wu ( 2009), Bao ( 2009), Chen and Kou (2009), Cao, Yu and Zhong (2010), Wang, Zhou and Zhou (2013), Bai and Wu (2016), Kelly, Manzo and Palhares (2016) and Du, Elkamhi and Ericsson (2019) all use equity options (some in more formal ways than others) to estimate the Q distribution and generate credit 9 Because of the limited range of leverage across the 40 firms, the analysis can only reveal the left-hand tail of the implied distribution. A 17% volatility is used for the lognormal distribution as that is the volatility computed with the method of Schaefer and Strebulaev (2008) in that week.…”
Section: Fat Tails and The Merton Modelmentioning
confidence: 99%
“…The Merton model finds that default probability is negatively associated with (present) firm value and positively associated with volatility of firm value. Many empirical studies [17,[23][24][25] have supported this prediction. Therefore, we need to examine how CSR activities affect the credit risks via the two channels: (i) the current value of a firm and (ii) the volatility (or uncertainty) of future firm values.…”
Section: Theoretical Background and Hypothesesmentioning
confidence: 81%
“…Balachandran et al (2010) analyze specifically the effects of executive compensation on extreme risk. Finally, Du et al (2019) also applies the time-varying asset volatility to address the credit spread puzzle while Jessen and Lando (2015) highlights the importance of "a volatility adjustment of the distance-to-default measure" in order to significantly improve default forecasting. Finally, our paper is related to the variance risk premium literature.…”
Section: Introductionmentioning
confidence: 99%