Level Of Credit Spreads Research Articles

Understanding the Performance of the Equity Value Factor

After decades of sound performance, doubts have been raised on the ability of the equity value factor to continue to deliver a positive performance in the aftermath of the 2008 Global Financial Crisis. Indeed, in a context dominated by low yields, sluggish growth and subdued inflation combined with an accelerating digitalization of the economy, the performance of value strategies struggled over the past decade. In this paper, we investigate potential drivers behind this performance lag, such as macroeconomic and microeconomic determinants, ESG characteristics or credit-borrowed components. Based on European and American data, we find that inflation and tightening credit spread levels are the most supportive factors for value stocks. As far as interest rates are concerned, their sustained low levels prevented the value stock universe from clearing its most distressed issuers, also known as deep value, and thus dampened value performance. As a matter of fact, we show that value has not been systematically an investment strategy bearing a heightened default risk. Our ESG analysis corroborates the transatlantic divide, the historical gap between the U.S. and Europe on this front, and shows that value and growth stocks are not necessarily all brown and green stocks. In addition, we demonstrate that the small cap segment has not been the magical cure to value underperformance. Finally, we conclude that value is not dead yet, and might even have bright days ahead considering the current improvements in market sentiment, especially if inflation does materialize. Nevertheless, we also emphasize that the current value risk factor is probably different in nature from the one we observed during the golden age of value investing at the beginning of the 2000s. Indeed, trading facilities, ease of access to fundamental data for a large number of investors, ESG investing and the digitalization of the economy may have changed the rules of the game.

Implied Asset Value Volatility from a New Structural Model of Credit Risk

Well-known structural models of credit risk have been shown to underpredict credit spreads, and these models all assume a lognormal firm value diffusion process (FVDP). In this article, I present the formula for pricing corporate liabilities using a normal FVDP that allows negative firm value scenarios that are plausible in real life but are not considered by the lognormal FVDP. And I further show that model-implied asset value volatility from the normal FVDP, unlike those from lognormal structural models, are very close to the empirically estimated asset value volatility for investment-grade companies of different leverage ratios. The same pattern of model-implied asset volatility versus estimates of historical asset volatility is observed from both credit default swap spread and historical default-loss data. Thus, the normal model, by incorporating the economic consideration of negative firm value, is able to explain both observed level of credit spreads and historical default experience with estimates of realized asset value volatility. <b>TOPICS:</b>Project finance, statistical methods, credit risk management <b>Key Findings</b> • By considering the full range of firm value, the new structural model of credit risk can relate traded credit spreads to empirically estimated asset value volatility. • Thus, real-time readings of model-implied asset volatility may be used to quantify the difference between an issuer’s credit quality and its credit spreads. • The new model also demonstrates that investment-grade (IG) credit ratings correctly predicting IG companies’ expected default risk, because for IG companies their historical default rate-implied asset value volatility agrees well with estimated historical asset value volatility.

Procyclicality in tradeable credit risk: Consequences for South Africa

Background: Tradeable credit assets are vulnerable to two varieties of credit risk: default risk (which manifests itself as a binary outcome) and spread risk (which arises as spreads change continuously). Current (2017) regulatory credit risk rules require banks to hold capital for both these risks. Aggregating these capital amounts is non-trivial. Aim: The aim was to implement the bubble value at risk (buVaR) approach, proposed by Wong (2011) to overcome the risk aggregation problem. This method accounts for diversification and for procyclicality and operates by inflating the positive side of the underlying return distribution, in direct proportion to prevailing credit spread levels (usually liquid credit default swap spreads). Setting: The principal setting for the study was the South African credit market which represents a developing market. Previous work by Wong (2011) focussed only on developed markets. Methods: Using South African data, closed form solutions were derived for free parameters of Wong’s formulation, and the relationship between the spread level and the response function was developed and calibrated. Results: The results indicate that the original calibrations and assumptions made by Wong (2011) would result in excessive capital requirement for South African banks. Estimates obtained from this work suggest further calibration is required to cover the unique features of the South African milieu. Considerable differences compared with other markets were also found. Conclusion: The application of buVaR to South African government bond credit default swaps spreads highlighted the metric’s countercyclical properties that would potentially have countered bubble developments had they been implemented during the credit crisis of 2008/2009. Regulatory authorities should take this important metric into account when allocating South African bank’s credit risk capital.

Can a Normal Firm Value Diffusion Process Improve the Performance of the Structural Approach to Pricing Corporate Liabilities?

We derive the pricing formulas for corporate liabilities by integrating their loss functions with firm value distributions from a normal and a lognormal firm value diffusion process (FVDP). By using credit spreads as the input to the pricing formulas, we find that the credit spread-implied asset value volatility from the lognormal FVDP is much higher than the empirically estimated asset value volatility for investment-grade companies, whereas the spread-implied asset volatility from the normal FVDP is almost identical to the estimated asset volatility for companies with different leverage ratios. Consequently, the normal FVDP can explain (i) the observed level of credit spreads when calibrated to historical default-loss experience, and (ii) the expected return on the equity market from traded credit spreads.

Option Implied Volatilities and Corporate Bond Yields: A Dynamic Factor Approach

This paper estimates dynamic factors from the term structure of credit spreads and the term structure of equity option implied volatilities, and it provides a comprehensive characterization of the dynamic relationships among those credit spread factors and equity volatility factors. The paper finds strong evidence that the volatility factors, especially the volatility level factor, Granger cause credit spread levels, confirming the theoretical predictions of Merton (1974) in a significantly richer and more nuanced environment than previously achieved. Simultaneously, the paper also finds evidence of reverse Granger causality from credit spreads to equity volatility, operating through the slope factors, consistent with the market microstructure literature, which finds that price discovery often happens first in bond markets. Hence the results extend both the corporate bond pricing literatures, deepening our understanding of stock and bond market interaction and suggesting profitable trading strategies.

A Multifactor Model of Credit Spreads

We represent credit spreads across ratings as a function of common unobservable factors of the Vasicek form. Using a state-space approach we estimate the factors, their process parameters, and the exposure of each observed credit spread series to each factor. We find that most of the systematic variation across credit spreads is captured by three factors. The factors are closely related to the implied volatility index (VIX), the long bond rate, and S&P500 returns, supporting the predictions of structural models of default at an aggregate level. By making no prior assumption about the determinants of yield spread dynamics, our study provides an original and independent test of theory. The results also contribute to the current debate about the role of liquidity in corporate yield spreads. While recent empirical literature shows that the level and time-variation in corporate yield spreads is driven primarily by a systematic liquidity risk factor, we find that the three most important drivers of yield spread levels relate to macroeconomic variables. This suggests that if credit spread levels do contain a large liquidity premium, the time variation of this premium is likely driven by the same factors as default risk.

Credit gap risk in a first passage time model with jumps

The payoff of many credit derivatives depends on the level of credit spreads. In particular, credit derivatives with a leverage component are subject to gap risk, a risk associated with the occurrence of jumps in the underlying credit default swap spreads. In the framework of first passage time models, we consider a model that addresses these issues. The principal idea is to model a credit quality process as an Itô integral with respect to a Brownian motion with a stochastic volatility. Using a representation of the credit quality process as a time-changed Brownian motion, one can derive formulas for conditional default probabilities and credit spreads. An example for a stochastic volatility process is the square root of a Lévy-driven Ornstein–Uhlenbeck process. The model can be implemented efficiently using a technique called Panjer recursion. Calibration to a wide range of dynamics is supported. We illustrate the effectiveness of the model by valuing a leveraged credit-linked note.

Credit Gap Risk in a First Passage Time Model with Jumps

The payoff of many credit derivatives depends on the level of credit spreads. In particular, credit derivatives with a leverage component are subject to gap risk, a risk associated with the occurrence of jumps in the underlying credit default swaps. In the framework of first passage time models, we consider a model that addresses these issues. The principal idea is to model a credit quality process as an Ito integral with respect to a Brownian motion with a stochastic volatility. Using a representation of the credit quality process as a time-changed Brownian motion, one can derive formulas for conditional default probabilities and credit spreads. An example for a volatility process is the square root of a Levy-driven Ornstein-Uhlenbeck process. The model can be implemented efficiently using a technique called Panjer recursion. Calibration to a wide range of dynamics is supported. We illustrate the effectiveness of the model by valuing a leveraged credit-linked note.

Credit Dynamics in a First Passage Time Model with Jumps

The payoff of many credit derivatives depends on the level of credit spreads. In particular, the payoff of credit derivatives with a leverage component is sensitive to jumps in the underlying credit spreads. In the framework of first passage time models we address these issues by specifying a credit quality process to be driven by an Ito integral with respect to a Brownian motion with stochastic volatility. We derive formulas for conditional default probabilities and credit spreads. An example for a volatility process is the square root of a Levy-driven Ornstein-Uhlenbeck process, for which we show that jumps in the volatility translate into jumps in credit spreads. We examine the dynamics of the model and provide examples.

Explaining the Level of Credit Spreads: Option-Implied Jump Risk Premia in a Firm Value Model

We study whether option-implied jump risk premia can explain the high observed level of credit spreads. We use a structural jump-diffusion firm value model to assess the level of credit spreads generated by option-implied jump risk premia. Prices and returns of equity index and individual options are used to estimate the jump parameters. We further calibrate the model to historical information on default risk and the equity premium. The results show that incorporating option-implied jump risk premia brings predicted credit spread levels much closer to observed levels. The introduction of jumps also helps to improve the fit of the volatility of credit spreads and equity returns.

Empirical Evaluation of Hybrid Defaultable Bond Pricing Models

A four‐factor model (the extended model of Schmid and Zagst) is presented for pricing credit risk related instruments such as defaultable bonds or credit derivatives. It is an advancement of an earlier three‐factor model. In addition to a firm‐specific credit risk factor, a new systematic risk factor in the form of GDP growth rate is included. This new model is set in the context of other hybrid defaultable bond pricing models and empirically compared to specific representatives. We find that a model based only on firm‐specific variables is unable to capture changes in credit spreads completely. However, it is shown that in this model, market variables such as GDP growth rates, non‐defaultable interest rates and firm‐specific variables together significantly influence credit spread levels and changes.

THE SLOPE OF THE TERM STRUCTURE OF CREDIT SPREADS: AN EMPIRICAL INVESTIGATION

AbstractIn this article we analyze the slope of the term structure of credit spreads. We investigate the explanatory role of interest rate, market, and idiosyncratic equity variables that the recent empirical literature highlights as important determinants of credit spread levels. This study extends the analysis and assesses its effect on credit slopes for a sample of corporate bonds. We find that these factors affect credit spreads at short and long maturities in a significantly different way. A closer inspection of the credit spread slope also reveals that it is a useful indicator of the direction of changes in future short‐term credit spreads. This evidence has important implications for the trading and risk management of portfolios of bonds with different maturities.

Explaining the Level of Credit Spreads: Option-Implied Jump Risk Premia in a Firm Value Model

Prices of equity index put options contain information on the price of systematic downward jump risk. We use a structural jump-diffusion firm value model to assess the level of credit spreads that is generated by option-implied jump risk premia. In our compound option pricing model, an equity index option is an option on a portfolio of call options on the underlying firm values. We calibrate the model parameters to historical information on default risk, the equity premium and equity return distribution, and S&P 500 index option prices. Our results show that a model without jumps fails to fit the equity return distribution and option prices, and generates a low out-of-sample prediction for credit spreads. Adding jumps and jump risk premia improves the fit of the model in terms of equity and option characteristics considerably and brings predicted credit spread levels much closer to observed levels.

Explaining the Level of Credit Spreads: Option-Implied Jump Risk Premia in a Firm Value Model

Prices of equity index put options contain information on the price of systematic downward jump risk. We use a structural jump-diffusion firm value model to assess the level of credit spreads that is generated by option-implied jump risk premia. In our compound option pricing model, an equity index option is an option on a portfolio of call options on the underlying firm values. We calibrate the model parameters to historical information on default risk, the equity premium and equity return distribution, and S&P 500 index option prices. Our results show that a model without jumps fails to fit the equity return distribution and option prices, and generates a low out-of-sample prediction for credit spreads. Adding jumps and jump risk premia improves the fit of the model in terms of equity and option characteristics considerably and brings predicted credit spread levels much closer to observed levels.

Default Probability Dynamics in Structural Models

While much attention has been paid to the level of credit spreads, very little research has focused on the volatility dynamics implied by common models of credit risk. This article demonstrates that the structural framework for modeling credit risk implies that credit spread volatility is bounded by asset volatility, and derives a simple expression for the process followed by default probabilities. Volatility declines with time to maturity and the risk-free rate. Default probabilities exhibit a theoretical smoothness that is not an artifact of the data. Structural models also achieve maximal volatility only at very low asset values, and below the face value of the debt, and there is also limited evidence that higher-rated debt generates higher heteroscedasticity than lower-rated debt.

Is Default Event Risk Priced in Corporate Bonds?

We provide an empirical decomposition of the default, liquidity, and tax factors that determine credit spreads and expected corporate bond returns. In particular, we estimate the risk premium associated with a default event. We model default as a jump process with stochastic intensity. The default intensities are a function of common and firm-specific factors. In the model, excess corporate bond returns can be due to risk premia on factors driving the intensities and due to a risk premium on the default jump risk, besides tax and liquidity effects. The model is estimated using data on corporate bond prices for 104 US firms and historical default rates. We find significant risk premia on the common factors that drive intensities, as well as important tax and liquidity effects. These components go a long way towards explaining the level of credit spreads and expected corporate bond returns. Adding a positive default event risk premium helps to explain the remaining error, although this premium cannot be estimated with high statistical precision.