The Cross‐Section of Credit Risk Premia and Equity Returns

Friewald, Niels; Wagner, Christian; Zechner, Josef

doi:10.1111/jofi.12143

Cited by 175 publications

(84 citation statements)

References 43 publications

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“…Faust et al (2013) use credit spreads to forecast economic activity including real GDP, real personal consumption expenditures (PCE), etc. Friewald, Wagner, and Zechner (2014) find firms' stock return is linked with credit risk premia estimated from CDS spreads. Han and Zhou (2011) predict future stock returns with the slope of the term structure of CDS spreads.…”

Section: Introductionmentioning

confidence: 99%

CDS Inferred Stock Volatility

Guo

2016

Journal of Futures Markets

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Both CDS and out-of-money put option can protect investors against downside risk, so they are related while not being mutually replaceable. This study provides a straightforward linkage between corporate CDS and equity option by inferring stock volatility from CDS spread and, thus, enables a direct analogy with the implied volatility from option price. I find CDS inferred volatility (CIV) and option implied volatility (OIV) are complementary, both containing some information that is not captured by the other. CIV dominates OIV in forecasting stock future realized volatility. Moreover, a trading strategy based on the CIV-OIV mean reverting spreads generates significant risk-adjusted return. These findings complement existing empirical evidence on cross-market analysis.

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Section: Introductionmentioning

confidence: 99%

CDS Inferred Stock Volatility

Guo

2016

Journal of Futures Markets

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“…Therefore, we focus on risk-neutral distributions extracted using options and the appropriately compounded five-year CDS spread, even though the horizon to which the distribution refers is three months. Even if five-year spreads are the most liquid and thus more likely to have a higher signal-to-noise ratio, Friewald, Wagner, and Zechner (2014) find that the term-structure of CDS spreads is informative about the equity risk premium. We repeat our analysis using either one-year CDS spreads from…”

Section: Robustnessmentioning

confidence: 98%

“…The spreads on bonds and CDS are in theory linked through a no-arbitrage restriction. Among them, Friewald, Wagner, and Zechner (2014) show the CDS spread-term structure contains information about the equity premium. Cao, Yu, and Zhong (2010) document the covariation of CDS spreads and the volatility risk premium.…”

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confidence: 99%

Firm-Specific Risk-Neutral Distributions: The Role of CDS Spreads

Aramonte

Rosen

Schindler

2017

internationalfinance

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We propose a method to extract individual firms' risk-neutral return distributions by combining options and credit default swaps (CDS). Options provide information about the central part of the distribution, and CDS anchor the left tail. Jointly, options and CDS span the intermediate part of the distribution, which is driven by moderatesized jump risk. We study the returns on a trading strategy that buys (sells) stocks exposed to positive (negative) moderate-sized jump risk unspanned by options or CDS individually. Controlling for many known factors, this strategy earns a 0.5% premium per month, highlighting the economic value of combining options and CDS.

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“…The that high credit risk premia square with abnormally low equity risk premia. For example, Friewald et al (2013) provide evidence that firms' stock returns increase with credit risk premia obtained from CDS spreads.…”

Section: Figure 4: Evolution Of the Commonality Of Cds Spread Returnsmentioning

confidence: 99%

The Determinants of CDS Spreads: Evidence from the Model Space

Pelster

Vilsmeier

2016

SSRN Journal

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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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The Cross‐Section of Credit Risk Premia and Equity Returns

Cited by 175 publications

References 43 publications

CDS Inferred Stock Volatility

CDS Inferred Stock Volatility

Firm-Specific Risk-Neutral Distributions: The Role of CDS Spreads

The Determinants of CDS Spreads: Evidence from the Model Space

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