Pricing Credit Default Swaps with Observable Covariates

Doshi, Hitesh; Ericsson, Jan; Jacobs, Kris; Turnbull, Stuart M.

doi:10.1093/rfs/hht015

Cited by 65 publications

(30 citation statements)

References 63 publications

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“…We provide a more detailed discussion of equity and asset return Sharpe ratios in the Internet Appendix. 20 These ratios are comparable to what has been reported in Berndt et al (2018), and Doshi et al (2013). 21 The asset volatility value is based on Feldhütter and Schaefer (2018) and the leverage value from Huang and Huang (2012).…”

Section: Variance Risk Premia and Credit Spreads For A Representativesupporting

confidence: 55%

Time‐Varying Asset Volatility and the Credit Spread Puzzle

Du¹,

Elkamhi

Ericsson

2019

The Journal of Finance

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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 extensions are considered, among them optimal leverage and endogenous default.

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Section: Variance Risk Premia and Credit Spreads For A Representativesupporting

confidence: 55%

Time‐Varying Asset Volatility and the Credit Spread Puzzle

Du¹,

Elkamhi

Ericsson

2019

The Journal of Finance

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“…In Doshi et al. (), the intensity is modeled as a quadratic function of the leverage, among other factors. We could obtain a similar intensity process by setting

α^{false(i false)}

to 2.…”

mentioning

confidence: 99%

Credit and Systemic Risks in the Financial Services Sector: Evidence From the 2008 Global Crisis

Bégin¹,

Boudreault²,

Doljanu³

et al. 2017

J of Risk & Insurance

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We develop a portfolio credit risk model that includes firm‐specific Markov‐switching regimes as well as individual stochastic and endogenous recovery rates. Using weekly credit default swap premiums for 35 financial firms, we analyze the credit risk of each of these companies and their statistical linkages, putting emphasis on the 2005–2012 period. Moreover, we study the systemic risk affecting both the banking and insurance subsectors.

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“…Some might argue that if CDS spreads are nonstationary, then the results employing level CDS spreads may be spurious and differencing the spreads may help alleviate the statistical problems. However, the theoretical underpinnings of CDS spreads are not intrinsically described by processes that display drifts, which would imply nonstationarity (Doshi et al, 2013). Additionally, the impact of the nonstationary series on panel model estimates may not be as central as in pure time-series analysis (Kao, 1999;Phillips and Moon, 1999).…”

Section: Robustnessmentioning

confidence: 99%

Measuring Sovereign Risk: Are CDS Spreads Better than Sovereign Credit Ratings?

Rodríguez¹,

Dandapani²,

Lawrence

2018

Financial Management

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Using data for 54 countries over a 12‐year period, we find that the variation in average sovereign ratings in a given year can be explained by average credit default swap (CDS) spreads over the previous three years. In a horse race between CDS spreads and sovereign ratings, we find that CDS spread changes can predict sovereign events, while rating changes cannot. The predictability of CDS spreads is greater when there is disagreement between Moody's and the S&P for a country's rating.

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Pricing Credit Default Swaps with Observable Covariates

Cited by 65 publications

References 63 publications

Time‐Varying Asset Volatility and the Credit Spread Puzzle

Time‐Varying Asset Volatility and the Credit Spread Puzzle

Credit and Systemic Risks in the Financial Services Sector: Evidence From the 2008 Global Crisis

Measuring Sovereign Risk: Are CDS Spreads Better than Sovereign Credit Ratings?

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