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Credit spread change is a key issue for investors to earn the excess return from corporate bonds. Generally, there are two kinds of different effects that support the changing of credit spread: asset allocation effect and credit risk change effect. The existing literature based on US data supports credit spread change is driven by credit risk change; however, this issue based on Chinese market data has not been investigated clearly. This paper seeks to address this issue.

The authors adopt Markov regime switching model to investigate the changing mode of the credit spread in the Chinese bond market. They select three kinds of variables: the credit risk variables, the asset allocation variables and the liquidity condition variables. With ML estimators, the authors can find the changing mode and further they study the relationship between the regime switching and some observed variables, such as macro economy variables and turnover of stock market.

The authors find it is driven by asset allocation effect in most time and by credit risk change only in shorter period. Empirical results show that the switching of dominance from one effect to another isn't closely related with macro‐economy variables, but related with the turnover of stock market.

These results indicate that in China the credit risk of corporate bonds is relatively low and the corporate bonds are still auxiliary asset for investors.

In this paper, the authors have the following two contributions: first, they discuss the asset allocation effect in the Chinese bond market and introduce the stock market variables and bank credit variable to describe the asset allocation effect; second, based on Chinese bond market data, they find different findings from the existing literature about US and European bond markets, showing that the changing of credit spread is mostly related with asset allocation effect but not credit risk change.

This study aims to examine the association between predictive accounting downside risk measures and changes in credit spreads. Building upon the earnings downside risk (EDR) measure developed in prior literature, this paper introduces cash flow downside risk (CFDR).

This study modifies an existing empirical framework (root lower partial moment) to calculate CFDR and applies it to a sample of firms between 2002 and 2013 for which credit default swap data are available.

After validating the measure, this study identifies a positive association between CFDR and changes in credit spreads. This paper further shows the association between CFDR and credit spread changes is stronger than that between EDR and credit spread changes. Financial stability moderates the relationship between CFDR and credit spreads.

This study proposes a novel measure of accounting downside risk, CFDR and demonstrates a negative association between this measure and future cash flow and a positive association between this measure and future credit spreads.

The creditworthiness of corporates is most visible in credit ratings. This paper aims to present an alternative credit rating measure independently of credit rating agencies. The credit rating score (CRS) is based on the credit default swap (CDS) market trading.

A CRS is developed which is a linear function of logarithmized CDS spreads. This new CRS is the first one that is completely independent of the rating agency. The estimated ratings are compared with ratings provided by Fitch Ratings for 310 European and US non-financial corporates.

The empirical analysis shows that logarithmized CDS spreads and issuer credit ratings by agencies have a linear relationship. The new CRS provides market participants with an alternative risk assessment, which is solely based on market factors, and does not rely on credit rating analysts. The results indicate that our CRS is able to anticipate agency ratings in advance. Moreover, the analysis shows that the trading volume has only a limited influence in the anticipation of rating changes.

This study shows a new approach to measure the creditworthiness of firms by analyzing CDS spreads. This is highly relevant for regulation, firm monitoring and investors.

Most of the credit spread literature deals with the determinants of credit spread changes for individual bonds. The purpose of this paper is to investigate the explanatory power of credit spread changes and their determinants for portfolios.

Using ordinary least squares (OLS) regressions and monthly data from 1990 to 1997, this paper tests several new potential determinants (e.g. portfolio diversification) and expectations (and realizations) for some previously identified determinants (e.g. gross domestic product (GDP)) of credit spread changes for portfolios of financials as derived from spot curves.

Strong empirical support is reported that default risk and undiversified risk are priced in credit spreads. The paper finds that forecasts for GDP and inflation are better determinants of credit spread changes than the realized values previously used in the literature, which is consistent with the notion that term structures convey expectations about future interest rates.

Interesting issues for future research include the sensitivity of the results to the use of other procedures for deriving zero‐coupon spot rates, and whether forecasts of macrovariables (such as GDP) are better determinants of credit spreads for other industrial categories, such as utilities and industrials.

The findings provide guidance for the management of risk for fixed income portfolios, for the pricing of fixed income securities differentiated by the difficulties encountered in achieving well‐diversified portfolios, and for assessing the performance of credit spread portfolios managed by financial institutions.

The empirical model, which achieves substantial explanatory power while being parsimonious, is the first to support the usage of forecasts instead of realized values in determining credit spreads, and to show that undiversifiable risk is an important component of the credit spreads of portfolios.

Peer-to-Peer (P2P) lending, which makes borrowers and investors meet directly through online platforms bypassing traditional financial institutions, is an emerging financing market after the traditional financial institutions crushed during the global financial crisis from 2007 to 2009. P2P lending platforms meet the credit demand more efficiently and play a vital role for the credit market and economic activity. This study sheds light on whether the credit spread of P2P lending is well predictive of economic activity compared to the bond credit spread which has been fully investigated in prior studies. Our findings show that the P2P credit spread performs similarly in predicting the economic activity as bond credit spread only during the financial crisis. However, the predictive power of P2P credit spread becomes inverse during the noncrisis periods since P2P lending platforms provide an alternative and easier financing channel to individuals who hardly borrow money for refinancing from traditional financial institutions. This study highlights the alternative role of P2P lending platform in financing and provides the evidence of different predictive powers of P2P credit spread on economic activity in different time periods.

The purpose of this paper is to investigate two research questions: do accounting reports provide information that helps bondholders assess credit risk of financial institutions? What are the relevant accounting variables related to financial institutions’ credit spreads?

The authors estimate all models of credit spread by specifying fixed effects with year dummies.

The authors’ document that financial institutions’ cash flows and loan loss provisions (LLP) are significantly correlated with bond spreads. The authors observe that an increase in nondiscretionary LLP predicts an increase in credit spreads, as the former reflects a higher default risk. Bondholders also react negatively to an increase in discretionary LLP, viewed as evidence that a financial institution is engaged in opportunistic earnings or tax management. Finally, the authors demonstrate that the relationship between accounting data and credit spreads is stronger for high-yield bonds than for low-yield bond.

This study has certain limitations due to the sample size and data frequency.

First, this paper provides strong evidence to all market participants that financial accounting reports are useful in forecasting credit risk in emerging markets. Second, the paper highlights the importance of disclosure policies and accounting transparency of financial institutions in emerging markets. Third, the results are also of practical interest to standard setters and financial regulators. The latter should consider monitoring accruals, especially the discretionary component of LLP, to mitigate the effects of accounting manipulations and managers’ opportunism.

First, the previous literature does not focus on financial institutions despite their key role in the economy. Second, the paper is the first to study the credit relevance of accounting information in emerging markets (Tunisia).

In this second installment, the author addresses some of the problems associated with empirically validating contingent‐claim models for valuing risky debt. The article uses a simple contingent claims risky debt valuation model to fit term structures of credit spreads derived from data for U.S. corporate bonds. An essential component to fitting this model is the use of expected default frequency; the estimate of the firms' expected default probability over a specific time horizon. The author discusses the statistical and econometric procedures used in fitting the term structure of credit spreads and estimating model parameters. These include iteratively reweighted non‐linear least squares are used to dampen the impact of outliers and ensure convergence in each cross‐sectional estimation from 1992 to 1999.

The purpose of this paper is to study the correlation structure of the credit spreads.

The minimal spanning tree is used to find the risk center node and the basic correlation structure of the credit spreads. The dynamic copula and pair copula models are applied to capture the dynamic and non-linear correlation structure.

The authors take the enterprise bond with trading data from January 2013 to December 2013 as the research sample. The empirical study of minimum spanning tree shows that the credit risk of corporate bonds forms a network structure with a center node. Meanwhile, the correlation between credit spreads shows dynamic characteristics. Under the framework of dynamic copula, the lower tail dependence is less than the upper tail dependence, thus, in economic boom period, the dynamic correlation is more significant than in recession period. The authors also find that the centrality of credit risk network is not significant according to the pair copula and Granger causality test. The empirical study shows that the goodness-of-fit of D vine is superior to Canonical vine, and the Granger causality test additionally proves that the center node has influence on few other nodes in the risk network, thus the center node captured by the minimum spanning tree is a weak center node, and this characteristic of credit risk network indicates that the risk network of credit spreads is generated mostly by the external shocks rather than the internal risk contagion.

This paper provides new ideas for investors and researchers to analyze the credit risk correlation or contagion.

This study aims to investigate the determinants of credit spreads in hotel loans securitized into commercial mortgage-backed securities (CMBS) between 2010 and 2015.

The sample represents 1,579 US hotel fixed interest rate whole loans with an aggregate mortgage value of $26.6bn at loan origination. The relationship between credit spreads and property, loan and market characteristic is examined via multiple regression analysis. Additionally, the method of 2-stage least squares is used to control for endogeneity bias and identify the effect of the loan-to-value (LTV) ratio on credit spreads.

The multiple regression models explain 80 per cent of the variation in credit spreads and show a significant association of credit spreads with hotel and loan characteristics and market conditions. The findings indicate the debt coverage ratio to be the most important predictor of credit spreads followed by the loan maturity term, implied capitalization rate, LTV and yield curve. The results show the debt yield premium to be a stronger predictor of credit spreads than the debt yield ratio. The spread between the debt yield ratio and mortgage interest rate could be used in future research as an instrumental variable to identify the effect of the LTV on credit spreads.

This study is limited to the CMBS market and the period after the financial crisis. Additional limitations include sample selection bias, exclusion of multi-property loans and variable interest rate loans.

Interest rate increases in an expanding economy would likely increase the cost of borrowing for hotel owners leading to higher debt service payments and lower profitability. If an increase in interest rates is offset by a decline in credit spreads, hotel owners will still benefit from the ensuing stability in borrowing interest rates. The evidence also suggests that CMBS lenders favor select service and extended stay hotels. Owners and operators of these efficient and profitable hotels will likely obtain loans with lower credit spreads given their lower risk of default.

The current study provides evidence on the effects of loan and property characteristics in the pricing of loan risk and serves to inform CMBS market participants about the factors that drive credit spreads in hotel mortgage loans.

The authors investigate the implications of environmental, social and governance (ESG) practices of firms for the pricing of their credit default swaps (CDS). In doing so, the authors compare European and US firms and consider nonlinear and indirect effects. This complements the previous literature focusing on linear and direct effects using bond yields and credit ratings of US firms.

For this purpose, the authors apply fixed effects regressions on a comprehensive panel data set of US and European firms. Further, nonlinear and indirect effects are investigated utilizing quantile regressions and a path analysis.

The evidence indicates that higher ESG ratings mitigate credit risks of US and European firms from 2007 to 2019. The risk mitigation effect is U-shaped across ESG quantiles, which is consistent with opposing effects of growing stakeholder influence capacity and diminishing marginal returns on ESG investments. The authors further reveal a mediating indirect volatility channel that substantially amplifies the direct effect of ESG on credit risk. A one-standard-deviation improvement in ESG ratings is estimated to reduce CDS spreads of low, medium and high ESG firms by approximately 4%, 8% and 3%, respectively.

This is the first study to examine whether credit markets reflect regional differences between Europe and the US with regard to the ESG-CDS-relationship. In addition, this paper contributes to the existing literature by investigating differences in the response of CDS spreads across ESG quantiles and to study potential indirect channels connecting ESG and CDS spreads using structural credit risk variables.