We analyze the impact of the introduction of credit default swaps (CDSs) on real decision-making within the firm. Our structural model predicts that CDS introduction increases debt capacity more when uncertainty about the credit events that trigger CDS payment is lower. Using a sample of more than 56,000 firms across 51 countries, we find that CDSs increase leverage more in legal and market environments where uncertainty about CDS obligations is reduced and when property rights are weaker. Our results highlight the importance of legal uncertainty in the interpretation of the underlying trigger events of global credit derivatives

We provide a model of nonredundant credit default swaps (CDSs), building on the observation that CDSs have lower trading costs than bonds. CDS introduction involves a trade-off: it crowds out existing demand for the bond, but improves the bond allocation by allowing long-term investors to become levered basis traders and absorb more of the bond supply. We characterize conditions under which CDS introduction raises bond prices. The model predicts a negative CDS-bond basis, as well as turnover and price impact patterns that are consistent with empirical evidence. We also show that a ban on naked CDSs can raise borrowing costs

We use credit default swaps (CDS) trading data to demonstrate that the credit risk of reference firms, reflected in rating downgrades and bankruptcies, increases significantly upon the inception of CDS trading, a finding that is robust after controlling for the endogeneity of CDS trading. Additionally, distressed firms are more likely to file for bankruptcy if they are linked to CDS trading. Furthermore, firms with more “no restructuring” contracts than other types of CDS contracts (i.e., contracts that include restructuring) are more adversely affected by CDS trading, and the number of creditors increases after CDS trading begins, exacerbating creditor coordination failure in the resolution of financial distress

This paper attempts to explain the credit default swap (CDS) premium, using a novel approach to identify the volatility and jump risks of individual firms from high-frequency equity prices. Our empirical results suggest that the volatility risk alone predicts 48% of the variation in CDS spread levels, whereas the jump risk alone forecasts 19%. After controlling for credit ratings, macroeconomic conditions, and firms' balance sheet information, we can explain 73% of the total variation. We calibrate a Merton-type structural model with stochastic volatility and jumps, which can help to match credit spreads after controlling for the historical default rates. Simulation evidence suggests that the high-frequency-based volatility measures can help to explain the credit spreads, above and beyond what is already captured by the true leverage ratio

Credit default swaps can be used to lower the capital requirements of dealer banks entering into uncollateralized derivatives positions with sovereigns. We show in a model that the regulatory incentive to obtain capital relief makes CDS contracts valuable to dealer banks and empirically that, consistent with the use of CDS for regulatory purposes, there is a disconnect between changes in bond yield spreads and in CDS premiums, especially for safe sovereigns. Additional empirical tests related to the volume of contracts outstanding, effects of regulatory proxies, and the corporate bond and CDS markets support that CDS contracts are used for capital relief

Using novel position and trading data for single-name corporate credit default swaps (CDSs), we provide evidence that CDS markets emerge as “alternative trading venues” serving a standardization and liquidity role. CDS positions and trading volume are larger for firms with bonds fragmented into many separate issues and with heterogeneous contractual terms. Whereas hedging motives are associated with trading volume in the bond and CDS markets, speculative trading concentrates in the CDS. Cross-market arbitrage links the CDS and bond market via the basis trade, compressing the negative CDS-bond basis and reducing price impact in the bond market

We document extreme disruption in debt markets during the COVID-19 crisis: a severe price crash accompanied by significant dislocations at the safer end of the credit spectrum. Investment-grade corporate bonds traded at a discount to credit default swaps; exchange-traded funds traded at a discount to net asset value, more so for safer bonds. The Federal Reserve’s announcement of corporate bond purchases caused these dislocations to disappear and prices to recover. These facts inform potential theories of the disruption. The best explanation is an acute liquidity need for specific bond investors, such as mutual funds, leading them to liquidate large positions

We propose an equilibrium model for defaultable bonds that are subject to contagion risk. Contagion arises because agents with “fragile beliefs” are uncertain about the underlying economic state and its probability. Estimation on sovereign European credit default swaps (CDS) data shows that agents require a time-varying risk premium for bearing state uncertainty. The model outperforms affine specifications with the same number of state variables, suggesting that there are important nonlinearities in credit spreads that are captured by our model. Contagion drives most of the variation in CDS spreads, especially before the crisis. However, economic fundamentals account for a significant fraction during the crisis

We study how listing in multiple markets affects the dynamics between firms’ credit default swap (CDS) and stock returns. We find that cross-listing increases (1) the sensitivity of CDS to stock returns, (2) the integration of CDS with world equity and bond markets, and (3) the statistical synchronicity of CDS and stock prices. Our results are stronger for firms with greater media attention, analyst and CDS coverage, and Google search intensity and for listings in familiar markets. We suggest that a firm’s presence in global equity markets comes with an improvement in the credit-equity integration through a reduction of informational frictions. Received April 20, 2017; editorial decision February 12, 2019 by Editor Andrew Karolyi

We examine the accuracy and contribution of the Merton distance to default (DD) model, which is based on Merton's (1974) bond pricing model. We compare the model to a “naïve” alternative, which uses the functional form suggested by the Merton model but does not solve the model for an implied probability of default. We find that the naïve predictor performs slightly better in hazard models and in out-of-sample forecasts than both the Merton DD model and a reduced-form model that uses the same inputs. Several other forecasting variables are also important predictors, and fitted values from an expanded hazard model outperform Merton DD default probabilities out of sample. Implied default probabilities from credit default swaps and corporate bond yield spreads are only weakly correlated with Merton DD probabilities after adjusting for agency ratings and bond characteristics. We conclude that while the Merton DD model does not produce a sufficient statistic for the probability of default, its functional form is useful for forecasting defaults