Credit Default Swap Prices Research Articles

The Credit-Risk Relevance of Loan Impairments Under IFRS 9 for CDS Pricing: Early Evidence

Since 2018, banks have implemented the expected credit loss (ECL) model under International Financial Reporting Standard (IFRS) 9 to estimate loan losses, which replaces the incurred loss model under International Accounting Standard (IAS) 39. The key novelty of the ECL model is the incorporation of forward-looking information for recognizing accounting loan loss provisions (LLPs), which provides ample room for managerial discretion. Over the period 2014–2019, I first show that the shift to the ECL model improves the timeliness of loan loss recognition. However, under the IFRS 9 regime managers also use their accounting discretion more aggressively over LLP estimates to smooth earnings. I then investigate whether IFRS 9 improves the relevance of LLPs for credit default swap (CDS) pricing. I report that LLPs under IFRS 9 are incrementally more relevant than under IAS 39 for CDS pricing but mostly concentrated amongst banks with weaker pre-IFRS 9 information environments. I further show that under the IFRS 9 regime, LLPs are relevant for CDS pricing only when LLPs consistently reflect future expected losses while earnings smoothing via LLP generally impair the credit-risk relevance of LLPs. Finally, I find that strong governance is imperative for providing useful LLP estimates for CDS pricing.

Sovereign CDS Premiums’ Reaction to Macroeconomic News: An Empirical Investigation

We assess the efficiency of the sovereign credit default swap (CDS) market by investigating how sovereign CDS spreads react to macroeconomic news announcements. Contrary to the vast majority of the existing literature, one of our main findings supports the hypothesis that news announcements reduce market uncertainty and, thus, that both better- and worse-than-expected news lower CDS prices during our sample period. In addition, we find that CDS spreads respond differently to the four macroindicators across the three different regions. Our findings might help investors in these areas to interpret the surprises of macronews announcements when making decisions in CDS markets.

AN ANALYTICAL APPROXIMATION FORMULA FOR THE PRICING OF CREDIT DEFAULT SWAPS WITH REGIME SWITCHING

AbstractWe derive an analytical approximation for the price of a credit default swap (CDS) contract under a regime-switching Black–Scholes model. To achieve this, we first derive a general formula for the CDS price, and establish the relationship between the unknown no-default probability and the price of a down-and-out binary option written on the same reference asset. Then we present a two-step procedure: the first step assumes that all the future information of the Markov chain is known at the current time and presents an approximation for the conditional price under a time-dependent Black–Scholes model, based on which the second step derives the target option pricing formula written in a Fourier cosine series. The efficiency and accuracy of the newly derived formula are demonstrated through numerical experiments.

Sovereign credit default swaps and macroeconomic fundamentals

We provide evidence in favour of a significant non-linear, time-varying dependence between sovereign credit default swap (CDS) spreads and macroeconomic fundamentals for OECD countries. Macroeconomic conditions alone explain more than 80% of the out-of-sample variation in CDS spreads when non-linear random forest regressions are used. This is almost double the predictive performance of sparse and dense linear statistical learning methods. We test the coherence of the expected CDS spreads across different pure out-of-sample scenarios, e.g. training a random forest regression on EU countries and predicting CDS spreads for non-EU economies. The results suggest that non-linear machine learning methods enable “shadow” sovereign CDS pricing for countries and historical periods in which tradable sovereign CDS contracts are not available.

Is the Tone of Risk Disclosures in MD&As Relevant to Debt Markets? Evidence from the Pricing of Credit Default Swaps*

ABSTRACTThis paper examines whether the tone of corporate textual disclosures related to risk and uncertainty conveys relevant information to the credit default swap (CDS) market. Prior studies largely focus on the amount of risk disclosures and provide inconclusive evidence on the usefulness of risk disclosures for investors in assessing firm risk. Using a large sample of textual risk disclosures in the Management's Discussion and Analysis (MD&A) section of 10‐K and 10‐Q filings, I predict and find that the change in CDS spreads over the three‐day window surrounding the 10‐K/Q filing date is positively associated with the pessimism of the language used in the risk disclosures. I conduct several analyses to show that the effect of the tone of risk disclosures is distinguishable from that of the amount of such disclosures. Cross‐sectional analyses reveal that the CDS market reaction to the tone of MD&A risk disclosures is more pronounced for reference entities closer to default, consistent with creditors' particular concern about downside risk. Further, the CDS market reacts more significantly to the tone of MD&A risk disclosures for reference entities with a weaker information environment. Overall, these results support the view that the tone of textual risk disclosures in MD&As has information content for investors in the CDS market in particular and debt markets in general. My findings improve the understanding of textual risk disclosures by showing that the tone and the amount of such disclosures have different implications for debt market investors' risk perceptions.

Common Determinants of Credit Default Swap Premia in the North American Oil and Gas Industry. A Panel BMA Approach

This study discovered market determinants of credit default swap (CDS) spreads in the North American oil and gas industry. Due to the limited theoretical background on market sources of CDS price fluctuations, we chose to alleviate model uncertainty and possible misspecification issues using Bayesian model averaging. This robust framework allowed us to aggregate results from a vast number of linear panel models estimated over the 2017–2020 period. We identified oil price volatility, major shifts in the OPEC+ supply policy, natural gas prices and industrial metal prices as the most robust determinants of CDS spreads. We show that following the onset of the COVID-19 pandemic, oil prices ceased to be a notably important determinant of credit risk, as factors indirectly related to oil prices, such as global and sectoral uncertainty, financial conditions and the macroeconomic stance became more influential. Additionally, we show that the CDS spreads of shale companies are determined by similar common factors, but they are more sensitive to the OPEC+ decisions on the global supply and are less affected by the domestic activity. Finally, we also prove that our modelling approach may help investors and risk officers to identify robust determinants behind the dynamics of credit risk.

Cost Structure, Operating Leverage, and CDS Spreads

ABSTRACTWe provide evidence that credit investors do not fully impound the implications of firms' cost structure (or operating leverage) when pricing credit default swaps. Information about firms' cost structure is not disclosed and needs to be estimated. Furthermore, the performance implications of firms' cost structure depend on the expected macroeconomic conditions. We focus on the debt market because of the strong emphasis of this market on downside risk. To measure expected aggregate macroeconomic conditions, we employ the change in the anxious index (AI), which is the probability of a decline in real GDP provided by the SPF—the survey of professional forecasters. We find that the interaction between the firm's cost structure and change in AI predicts one-quarter-ahead CDS spreads. Portfolio-level analysis confirms this result.JEL Classifications: G12; G14; G32.

Modeling CDS spreads: A comparison of some hybrid approaches

Abstract According to the credit risk model proposed by Cathcart and El-Jahel (2006), default can occur either expectedly, when a certain signaling variable breaches a lower barrier, or unexpectedly, as the first jump of a Poisson process, whose intensity depends on the signaling variable itself and on the interest rate. In the present paper we test the performances of such a model and of other three models generalized by it in fitting the term structure of credit default swap (CDS) spreads. In order to do so, we derive a semi-analytical formula for pricing CDSs and we use it to fit the observed term structures of 65 different CDSs. The analysis reveals that all the model parameters yield a relevant contribution to credit spreads. Moreover, if the dependence of the default intensity on both the signaling variable and the interest rate is removed, the pricing of CDSs becomes very simple, from both the analytical and the computational standpoint, while the goodness-of-fit is reduced by only a few percentage points. Therefore, when using the credit risk model proposed by Cathcart and El-Jahel (2006), assuming a constant default intensity provides an interesting and efficient compromise between parsimony and goodness-of-fit. Furthermore, by fitting the term structure of CDS spreads on a period of about twelve years, we find that the parameters of the model with constant default are rather stable over time, and the goodness-of-fit is maintained high.

An Empirical Examination of the Term Structure Fundamentals of Credit Spreads

Using an exponential model of jumps to default, this research investigates the fundamentals that determine the spreads required by investors for corporate credit risk at different maturities. The empirical results indicate that credit default swap (CDS) prices across the term spectrum are strongly associated with interrelated probabilities of cash, income, and valuation insolvency, as well as with market-based measures of systematic risk. Although the financial functions best-fitting CDS prices are found to vary somewhat by maturity, an integrated model with constant parameters across time, company, and term is found to explain about 50% of individual credit spreads both in sample and ex post. <b>TOPICS:</b>Credit risk management, credit default swaps <b>Key Findings</b> • Using nonnegative least squares procedures to find the structural model forms which best fit credit spreads across the term structure, this empirical research explains more than half of individual credit default swap prices in and out of sample. • Credit default swap prices with shorter terms (like 1–3 years) are found to be most strongly determined by the probability of a company generating negative income and running out of cash, but the likelihood of cash and income insolvency is discovered to have important impacts at longer maturities of 5–10 years. • The 378,401 individual credit spreads in the sample over the 2008–2019 interval are found to incorporate time-varying systematic risk premiums (estimated from market prices), as well as reflect the integrated risks of income, cash, and valuation insolvency on any day.

CREDIT DEFAULT SWAPS IN TWO-DIMENSIONAL MODELS WITH VARIOUS INFORMATIONS FLOWS

We study a credit risk model of a financial market in which the dynamics of intensity rates of two default times are described by linear combinations of three independent geometric Brownian motions. The dynamics of two default-free risky asset prices are modeled by two geometric Brownian motions which are dependent of the ones describing the default intensity rates. We obtain closed form expressions for the no-arbitrage prices of both risk-free and risky credit default swaps given the reference filtration initially and progressively enlarged by the two default times. The accessible default-free reference filtration is generated by the standard Brownian motions driving the model.

The Long and Short of It: The Post-Crisis Corporate CDS Market

The authors establish key stylized facts about the post-crisis evolution of trading and pricing of credit default swaps. Using supervisory contract-level data, they show that dealers became net buyers of credit protection starting in the second half of 2014, both through reducing the amount of protection they sell in the single-name market and switching to buying protection in the index market. More generally, they argue that considering simultaneous positions in different types of credit derivatives is crucial for understanding institutions’ decisions to participate in these markets and how these decisions affect prices.

Corporate Credit Default Swap Systematic Factors

This study examines the statistical significance of systematic and firm-specific determinants of Credit Default Swap (CDS) price variations. We cast doubt on the firm-specific determinants showed in prior research to be statistical significance to CDS price variations. In this paper, two research questions are studied: (1) “Which and to what extent systematic factors can explain the individual CDS price variations?” and (2) “Which and to what extent the firm-specific factors can predict CDS spread variations that are not ex- plained by systematic factors?”. We find that systematic factors account for the majority changes of the CDS spreads (R2 = 35%). Merely 4 of 28 firm-specific factors are statistically significant predictors for CDS changes that are not explained by the systematic factors and they have little explanatory power (R2 = 8%). We document that individual CDS variations can

Comparing Prediction Power of Artificial Neural Networks Compound Models in Predicting Credit Default Swap Prices through Black–Scholes–Merton Model

Default risk is one of the most important types of risks, and credit default swap (CDS) is one of the most effective financial instruments to cover such risks. The lack of these instruments may reduce investment attraction, particularly for international investors, and impose potential losses on the economy of the countries lacking such financial instruments, among them, Iran. After the 2007 financial crisis, the importance of CDS has increasingly augmented because theoretically and practically, this instrument could significantly prevent catastrophes such as the mentioned crisis. The present study seeks to predict the price of CDS contracts with the Merton model as well as the compound neural network models including ANFIS, NNARX, AdaBoost, and SVM regression, and compare the predictive power of these algorithms which are among the most prestigious, intelligent models in finance. The research statistical population includes the A-rated North American and European companies which are known as the reference entities for credit default swaps. Data were collected from the Bloomberg Terminal for an eight-year period from 2008 to 2015. Contracts of 125 companies were selected as the statistical sample. The results reveal that the average predictive power of the NNARX is higher than that of other algorithms under scrutiny.

Sovereign risk evaluation for European Union countries

This study investigates the short run and the long run relationship between government bond yields and their associated credit default swaps (CDS), using co-integration and Granger causality techniques, for eight major European Union countries, over three different periods, considering the global financial and the resultant European debt crises. Moreover, this study tries to explore the interactions between changes in sovereign debt ratings and the corresponding credit premiums as an effort to examine the role of top credit rating agencies in defining CDS prices and as an attempt to illuminate the importance of the announcement date based on an event study.

The risk management implications of using end of day consensus pricing for single name CDS

When markets are opaque, market participants rely to a large extent on end-of-day consensus prices published by service providers; such as in the case of the single name credit default swaps (CDS) market. End-of-day consensus CDS prices are critical for valuation and risk management purposes, since profits and losses of inventory positions can be determined by marking to market using end-of-day consensus prices. Deviations between end-of-day prices and intraday quotes can occur due to many factors, including market illiquidity. This paper identifies determinants of such deviations, lays out the times when they can occur, and examines the impact of these deviations for risk management purposes. End-of-day consensus prices are representative of average and median intraday quotes; however, in periods of high volatility and market distress, end-of-day prices can notably differ from actual intraday quotes. Such analysis can allow market participants to better rely on end-of-day consensus prices during normal market conditions.

Linear credit risk models

We introduce a novel class of credit risk models in which the drift of the survival process of a firm is a linear function of the factors. The prices of defaultable bonds and credit default swaps (CDS) are linear–rational in the factors. The price of a CDS option can be uniformly approximated by polynomials in the factors. Multi-name models can produce simultaneous defaults, generate positively as well as negatively correlated default intensities, and accommodate stochastic interest rates. A calibration study illustrates the versatility of these models by fitting CDS spread time series. A numerical analysis validates the efficiency of the option price approximation method.

Pricing credit default swaps with Parisian and Parasian default mechanics

This paper proposes Parisian and Parasian default mechanics for modeling the credit risks of the CDS (credit default swap) contracts. Unlike most of the structural models used in the literature, our new model assumes that the default will occur only if the price of the reference asset stays below a certain level for a pre-described period of time. To work out the corresponding CDS price, a general pricing formula containing the unknown no-default probability is derived first. It is then shown that the determination of such a probability is equivalent to the valuation of a Parisian or Parasian down-and-out binary options, depending on how the time is recorded. After the option price is solved with a θ finite difference scheme, the CDS price is obtained through the derived general pricing formula. Finally, some numerical experiments are carried out to study the effects of the new default mechanics on the CDS prices.

Do Credit Default Swap Prices Reveal the Presence of Informed Trading?

We attempt to answer a key question whether credit default swap (CDS) prices and price changes contain private information that are used by informed traders within 24 hours to cross-trade in the related equity market. By disaggregating daily stock return into day (exchange trading hours), evening, and night returns, we find that the credit default swap return of a stock, with high credit spread and high idiosyncratic volatility (hard-to-value), negatively impacts the next market-hours stock return in a significant way both via panel regression tests and via portfolio profitability tests. For stocks with low idiosyncratic volatility and thus higher predictability, the CDS return impact is milder and is opposite in direction, indicating a pattern of hedged investing. We show empirically that by evening after market-hours, the impacted stock return would reverse partially explanable by the well-established observation of trading overconfidence due to private information bias. For the case of low credit ratings stocks with high opaqueness or being hard-to-value, there is the additional interesting finding that large stock price changes due to CDS impact do not evidence any partial reversal on the eve of days in which actual credit rating downgrade happened. This reinforces the evidence of the presence of informed trading. We also find that limit-to-arbitrage such as stock illiquidity and short-sale constraint cannot fully explain the predictive results on stock returns based on CDS return signals.

Ambiguity, Volatility, and Credit Risk

Abstract We explore the implications of ambiguity for the pricing of credit default swaps (CDSs). A model of heterogeneous investors with independent preferences for ambiguity and risk shows that, because CDS contracts are assets in zero net supply, the net credit risk exposure of the marginal investor determines the sign of the impact of ambiguity on CDS spreads. We find that ambiguity has an economically significant negative impact on CDS spreads, on average, suggesting that the marginal investor is a net buyer of credit protection. A 1-standard-deviation increase in ambiguity is estimated to decrease CDS spreads by approximately 6%. (JEL C65, D81, D83, G13, G22) Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Analytically Pricing Credit Default Swaps Under a Regime-Switching Model

In this paper, we consider the valuation of a CDS (credit default swap) contract when the reference asset is assumed to follow a regime-switching model with the volatility allowed to jump among different states. Our motivation originates from empirical evidence demonstrating the existence of regime-switching in real markets. The default probability is analytically derived first, based on which a closed-form formula for the CDS price is obtained so that it can be easily implemented for practical purposes. Finally, numerical experiments are carried out to show quantitatively some properties of the CDS price under the regime-switching model.