Default Risk Characteristics of Poll-Based Bond Spreads

We develop a structural credit model to examine how interactions between default and liquidity affect corporate bond pricing. The model features debt rollover and bond-price-dependent holding costs. Over the business cycle and in the cross-section, the model matches average default rates and credit spreads in the data, and captures variations in bid-ask and bond-CDS spreads. A structural decomposition reveals that default-liquidity interactions can account for 10%–24% of the level of credit spreads and 16%–46% of the changes in spreads over the business cycle. Further, liquidity-related corporate bond financing costs amount to 6% of the total issuance amount from 1996 to 2015. Received July 12, 2015; editorial decision April 15, 2017 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press website next to the link to the final published paper online.

Many pricing and risk management models require credit spread curves as an input. Given the small number of bonds outstanding per issuer, the estimation of credit spread curves in the corporate bond market is not a trivial matter. In many cases, to ensure that one has a sufficient number of bonds for the estimation procedure it is necessary to use bonds issued in different currencies, which means that the estimation procedure has to take into account potential currency effects. Using a rather general pricing framework, we show that credit spreads for a particular issuer are expected to be equal across different currencies only if the correlation between the default variables and the exchange rates and relevant money market accounts is zero. We present a new model that makes it possible to estimate a credit spread curve for a single issuer with bonds in different currencies and which simultaneously allows testing for the existence of currency-related effects. This new model is based on the multicurve estimation approach, which allows a parsimonious joint estimation of a risk-free term structure and the credit spread curve of the issuer. We reject the hypothesis of zero correlation between credit risk and exchange rate and interest rate risk, respectively. We present empirical evidence that there are significant differences between issuer-specific credit spreads across different currencies in a representative sample of international corporate bonds. Moreover, this implies that dollar-related credit spread curves cannot be used without paying special attention to the pricing of defaultable claims denominated in other currencies.

The objective of this research is to study the relationship between various aspects of corporate bonds liquidity , transaction costs and trading activity, and their perceived credit quality as measured by credit ratings. For debt securities, Credit quality and liquidity are perhaps the most important factors that affect the investors’ decisions whether to trade or hold these assets on their portfolio, as several theoretical and empirical studies identify these factors as key components of bonds yield spreads. However, the interaction and relationship between these two characteristics is not sufficiently addressed in the literature. From the investors’ perspective, it is beneficial to know whether they face a tradeoff between credit quality and liquidity or if both desirable features move in the same direction. We use more than twelve years of Enhanced TRACE data from 2002 to 2014 to analyze the liquidity of corporate bonds both cross-sectionally across credit ratings and intertemporally around credit rating changes. The first manuscript studies the relationship between corporate bonds’ credit risk and their market liquidity, and the dynamics of this relationship over the period from 2002 to 2014. Unlike the implication of theoretical models, our findings do not empirically support a monotonically positive relation between credit risk and transaction costs. Instead, we find an inverted U-shaped relationship where bonds with ratings near the Investment grade/ High yield boundary have the largest transaction costs (lowest liquidity) after controlling for other relevant factors. One explanation for this finding is that bond dealers behave more as brokers in speculative grade bonds and are reluctant to enter overnight positions in these risky securities, unless they find the other side of the trade. This kind of dealers behavior potentially reduces the transaction costs of lower rated bond, as captured by bid-ask spreads, to only reflect the cost of searching for counterparty rather than inventory or adverse selection risks, and may be even more pronounced during distressed market conditions due to more capital constrains and less funding liquidity. Consistent with this explanation, using a Markov Switching time series model, we find that while bid-ask spreads significantly increase for investment grade bonds during the crisis, they stay invariant for junk bonds. The second essay expands this investigation to examine the impact of rating changes on corporate bonds’ liquidity around the rating change announcements using an event study methodology. Many institutional investors such as insurance companies or pension funds are prohibited by regulations from

Enhanced Corporate Bond Yield Modelling Incorporating Macroeconomic News Sentiment

Are Credit Spreads and Interest Rates Co-Integrated? Empirical Analysis in the USD Corporate Bond Market

In the current literature, the focus of credit-risk analysis has been either on the valuation of risky corporate bond and credit spread or on the valuation of vulnerable options, but never both in the same context. There are two main concerns with existing studies. First, corporate bonds and credit spreads are generally analyzed in a context where corporate debt is the only liability of the firm and a firm’s value follows a continuous stochastic process. This setup implies a zero short-term spread, which is strongly rejected by empirical observations. The failure of generating non-zero short-term credit spreads may be attributed to the simplified assumption on corporate liabilities. Because a corporation generally has more than one type of liability, modeling multiple liabilities may help to incorporate discontinuity in a firm’s value and thereby lead to realistic credit term structures. Second, vulnerable options are generally valued under the assumption that a firm can fully pay off the option if the firm’s value is above the default barrier at the option’s maturity. Such an assumption is not realistic because a corporation can find itself in a solvent position at option’s maturity but with assets insufficient to pay off the option. The main contribution of this study is to address these concerns. The proposed framework extends the existing equity-bond capital structure to an equity-bond-derivative setting and encompasses many existing models as special cases. The firm under study has two types of liabilities: a corporate bond and a short position in a call option. The risky corporate bond, credit spreads, and vulnerable options are analyzed and compared with their counterparts from previous models. Numerical results show that adding a derivative type of liability can lead to positive short-term credit spreads and various shapes of credit-spread term structures that were not possible in previous models. In addition, we found that vulnerable options need not always be worth less than their default-free counterparts. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:301–327, 2001

this paper we present a tree model for defaultable bond prices which can be used for the pricing of credit derivatives. The model is based upon the two-factor Hull-White (1994) model for default-free interest rates, where one of the factors is taken to be the credit spread of the defaultable bond prices. As opposed to the tree model of Jarrow and Turnbull (1992), the dynamics of default-free interest rates and credit spreads in this model can have any desired degree of correlation, and the model can be fitted to any given term structures of default-free and defaultable bond prices, and to the term structures of the respective volatilities. Furthermore the model can accommodate several alternative models of default recovery, including the fractional recovery model of Duffie and Singleton (1994) and recovery in terms of equivalent default-free bonds (see e.g. Lando (1998)). Although based on a Gaussian setup, the approach can easily be extended to non-Gaussian processes that avoid negative interest-rates or credit spreads

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This paper investigates a two-factor affine model for the credit spreads on corporate bonds. The first factor can be interpreted as the level of the spread and the second factor is the volatility of the spread. The riskless interest rate is modeled using a standard two-factor affine model, thus leading to a four-factor model for corporate yields. This approach allows us to model the volatility of corporate credit spreads as stochastic, and also allows us to capture higher moments of credit spreads. We use an extended Kalman filter approach to estimate our model on corporate bond prices for 108 firms. The model is found to be successful at fitting actual corporate bond credit spreads, resulting in a significantly lower root mean square error than a standard alternative model both in sample and out of sample. In addition, key properties of actual credit spreads such as the stochastic volatility of the credit spreads and the positive skewness of the credit spread distribution are better captured by the model.

This research aims to find an optimal solution for dynamic portfolio in finite-time horizon under defaultable assets, which means that the assets has a chance to be liquidated in a finite time horizon, e.g corporate bond. Besides investing on those assets, investors will also have benefit in the form of consumption. As a reference in making investment decisions the concept of utility functions and volatility will play a role. Optimal portfolio composition will be obtained by maximizing the total expected discounted utility of consumption in the time span during the investment is executed and also to minimize the risk, the volatility of the investment. Further the reduced form model is applied since the assets prices can be linked with the market risk and the credit risk. The interest rate and the rate of inflation will be allowed as a representation of market risk, while the credit spread will be used as a representation of credit risk. The dynamic of asset prices can be derived analytically by using Ito Calculus in the form of the movement of the three risk factors above. Furthermore, this problem will be solved using the stochastic dynamic programming method by assuming that market is incomplete. Depending on the chosen utility function, the optimal solution of the portfolio composition and the consumption can be found explicitly in the form of feedback control. This is possible since the dynamic of the wealth process of the control variable is linear. To apply dynamic programming as well as to find solutions we use Backward Stochastic Differential Equation (BSDE) where the solution can be solved explicitly, especially where the terminal value of the investment target is chosen random. Further, it will be modeled with Monte Carlo simulation and, calibrated using Indonesia data of stock and corporate bond.

Credit Derivatives and Risk Management

In this study, we investigate the determinants of credit spread using a Markov regime-switching model. We consider corporate governance variables and credit risk to analyze the determinants of credit spread. The corporate governance mechanism is an indicator of company sustainability, and credit spread is the main factor in profits obtained by banks. However, the relationship between credit spread and corporate governance is seldom discussed. We focus on loans from banks in Taiwan between 2000 and 2019 and apply a Markov regime-switching model, which is superior to other models in capturing different effects in various regimes. We specify two regime types: corporate governance and credit risk regimes. Furthermore, we consider four aspects of corporate governance: firm ownership structure, board structure, deviation, and information environment. In this study, the determinants of credit spread are investigated more thoroughly than in previous studies. Moreover, in this study, we examine the effects of monetary policy and economic status on credit spread using a Markov regime-switching model; such models are not employed to their full extent in related studies of credit spread. Empirical results indicate that credit spread has different effects in various regimes. Thus, understanding the determinants of credit spread in different regimes is crucial for financial analysts, investors, economic policymakers, and banks. Consequently, we expect that this study can improve the management and measurement of credit risk and be of value to financial institutions.

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.

Using event-study techniques, we investigate the impact of Brexit-related events on the corporate bond yield spreads in the United Kingdom and Eurozone, respectively. We want to find out whether Brexit-related news, including the Brexit referendum itself, had an impact on the risk conditions in those two corporate bond markets. Our estimation results indicate that the announcement of the referendum result is associated with increasing credit spreads in the UK and EA. However, only the actual announcement of the UK referendum result itself had an influence on the credit spreads. Furthermore, we distinguish between the financial and the non-financial economic sectors in order to analyze more specific sector-related effects of the referendum event. Our estimation results suggest that UK credit spreads were more strongly influenced by the announcement of the results of the Brexit referendum than credit bond spreads in the Eurozone were. Finally, we split our sample into pre-referendum and post-referendum periods to consider the potential changing evaluation of the determinants of corporate bond spreads due to altering risk pricing triggered by the Brexit referendum result. We find that the effect of credit default risk is far stronger and plays a significant role in the post-referendum period in UK and EA, respectively.

Corporate yield spreads and real interest rates