Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics

Understanding if credit risk is driven mostly by idiosyncratic firm characteristics or by systematic factors is an important issue for the assessment of financial stability. By exploring the links between credit risk and macroeconomic developments, we observe that in periods of economic growth there may be some tendency towards excessive risk-taking. Using an extensive dataset with detailed information for more than 30 000 firms, we show that default probabilities are influenced by several firm-specific characteristics. When time-effect controls or macroeconomic variables are also taken into account, the results improve substantially. Hence, though the firms’ financial situation has a central role in explaining default probabilities, macroeconomic conditions are also very important when assessing default probabilities over time.     

Sovereign credit spreads

The paper develops a structural credit risk model to study sovereign credit risk and the dynamics of sovereign credit spreads. The model features endogenous default and recovery rates that both depend on the interaction between domestic output fluctuations and global macroeconomic conditions. We show that sovereigns choose to default at higher levels of economic output once global macroeconomic conditions are bad. This yields to default rates and credit spreads that are substantially higher compared to normal times. We derive closed-form expressions for sovereign debt values and default times and focus on the dynamics of sovereign credit spreads. As opposed to standard theories of sovereign debt, this paper’s structural model generates much richer default patterns and non-linearities through regime-shifts in the global macroeconomic environment. Moreover, changes in the global environment reveal the interconnectedness of the financial system.     

Capital structure,credit risk,and macroeconomic conditions

This paper develops a framework for analyzing the impact of macroeconomic conditions on credit risk and dynamic capital structure choice. We begin by observing that when cash flows depend on current economic conditions, there will be a benefit for firms to adapt their default and financing policies to the position of the economy in the business cycle phase. We then demonstrate that this simple observation has a wide range of empirical implications for corporations. Notably, we show that our model can replicate observed debt levels and the countercyclicality of leverage ratios. We also demonstrate that it can reproduce the observed term structure of credit spreads and generate strictly positive credit spreads for debt contracts with very short maturities. Finally, we characterize the impact of macroeconomic conditions on the pace and size of capital structure changes, and debt capacity.     

Multi-period corporate default prediction with stochastic covariates

We provide maximum likelihood estimators of term structures of conditional probabilities of corporate default, incorporating the dynamics of firm-specific and macroeconomic covariates. For US Industrial firms, based on over 390,000 firm-months of data spanning 1980 to 2004, the term structure of conditional future default probabilities depends on a firm's distance to default (a volatility-adjusted measure of leverage), on the firm's trailing stock return, on trailing S&P 500 returns, and on US interest rates. The out-of-sample predictive performance of the model is an improvement over that of other available models.     

Modeling default probabilities: The case of Brazil

Using disaggregated data from the Brazilian stock market, we calculate default probabilities for 30 different economic sectors. Empirical results suggest that domestic macroeconomic factors can explain these default probabilities. In addition, we construct the Minimum Spanning Tree (MST) and the ultrametric hierarchical tree with the MST based on default probabilities to disclose common trends, which reveals that some sectors form clusters. The results of this paper imply that macroeconomic variables have distinct effects on default probabilities, which is important to take into account in credit risk modeling and the generation of stress test scenarios.     

Asymmetric effects of the business cycle on bank credit risk

Prior empirical research on the relation between credit risk and the business cycle has failed to properly investigate the presence of asymmetric effects. To fill this gap, we examine this relation both at the aggregate and the bank level exploiting a unique dataset on Italian banks’ borrowers’ default rates. We employ threshold regression models that allow to endogenously establish different regimes identified by the thresholds over/below which credit risk is more/less cyclical. We find that not only are the effects of the business cycle on credit risk more pronounced during downturns but cyclicality is also higher for those banks with riskier portfolios.     

Correlation in credit risk changes

The current economic climate makes understanding credit risk correlation particularly important. After allowing for a comprehensive set of observable firm-specific, industry, market, and macroeconomic factors, there is an economically significant co-movement in credit default swap spreads that remains to be explained. Including a time dummy completely accounts for the remaining co-movement, confirming the existence of a systematic component that has been previously unaccounted for. Our findings suggest that it may be important to consider unobservable risk factor(s) in credit risk models.     

A reduced form model of default spreads with Markov-switching macroeconomic factors

We examine the ability of observed macroeconomic factors and the possibility of changes in regime to explain the proportion of yield spreads caused by the risk of default in the context of a reduced form model. For this purpose, we extend the Markov-switching risk-free term structure model of Bansal and Zhou (2002) to the corporate bond setting and develop recursive formulas for default probabilities, risk-free and risky zero-coupon bond yields as well as credit default swap premia. The model is calibrated with consumption, inflation, risk-free yields and default data for Aa, A and Baa bonds from the 1987 to 2008 period. We find that our macroeconomic factors are linked with two out of three sharp increases in the spreads during this sample period, indicating that the spread variations can be related to macroeconomic undiversifiable risk.     

Analysing the determinants of insolvency risk for general insurance firms in the UK

This paper estimates a reduced-form model to assess the insolvency risk of General Insurance (GI) firms in the UK. In comparison to earlier studies, it uses a much larger sample including 30 years of data for 515 firms, and also considers a much wider set of possible determinants of insolvency risk. The empirical results suggest that macroeconomic and firm-specific factors both play important roles. Other key findings are the following: insolvency risk varies across firms depending on their business lines; there is default clustering in the GI industry; different reinsurance levels also affect the insolvency risk of insurance firms. The implications of these findings for regulators of GI firms under the newly launched Solvency II are discussed.     

A comparative study of the probability of default for global financial firms

This article presents a modification of Merton’s (1976) ruin option pricing model to estimate the implied probability of default from stock and option market prices. To test the model, we analyze all global financial firms with traded options in the US and focus on the subprime mortgage crisis period. We compare the performance of the implied probability of default from our model to the expected default frequencies based on the Moody’s KMV model and agency credit ratings by constructing cumulative accuracy profiles (CAP) and the receiver operating characteristic (ROC). We find that the probability of default estimates from our model are equal or superior to other credit risk measures studied based on CAP and ROC. In particular, during the subprime crisis our model surpassed credit ratings and matched or exceeded KMV in anticipating the magnitude of the crisis. We have also found some initial evidence that adding off-balance-sheet derivatives exposure improves the performance of the KMV model.     

The determinants of bank loan recovery rates

Using Moody’s Ultimate Recovery Database, we estimate a model for bank loan recoveries using variables reflecting loan and borrower characteristics, industry and macroeconomic conditions, and several recovery process variables. We find that loan characteristics are more significant determinants of recovery rates than are borrower characteristics prior to default. Industry and macroeconomic conditions are relevant, as are prepackaged bankruptcy arrangements. We examine whether a commonly used proxy for recovery rates, the 30-day post-default trading price of the loan, represents an efficient estimate of actual recoveries and find that such a proxy is biased and inefficient.     

Business credit information sharing and default risk of private firms

We investigate whether and how business credit information sharing helps to better assess the default risk of private firms. Private firms represent an ideal testing ground because they are smaller, more informationally opaque, riskier, and more dependent on trade credit and bank loans than public firms. Based on a representative panel dataset that comprises private firms from all major industries, we find that business credit information sharing substantially improves the quality of default predictions. The improvement is stronger for older firms and those with limited liability, and depends on the sharing of firms’ payment history and the number of firms covered by the local credit bureau office. The value of soft business credit information is higher the smaller the firms and the lower their distance from the local credit bureau office. Furthermore, in spatial and industry analyses we show that the higher the value of business credit information the lower the realized default rates. Our study highlights the channel through which business credit information sharing adds value and the factors that influence its strength.     

The heterogeneous impact of macroeconomic information on firms' earnings forecasts

This paper re-examines the role of macroeconomic information in forecasting firm earnings. We adopt a Fama–MacBeth regression model with the important extension of including information from over 140 macroeconomic variables that enter into the model in a reduced dimension form as a consequence of common factor analysis. The resulting factor-augmented model is then used to evaluate the importance of macroeconomic information on earnings forecasts for U.S. firms from 1962 to 2009. The same model is also examined for each individual time window and industry subsample. The results reveal a clear and heterogeneous impact of macroeconomic information on firm-specific earnings forecasts, and that these effects differ markedly during certain periods and across industries. In addition, when compared to analyst forecasts, we show that our model is generally more accurate over longer forecast horizons. The results of the identified heterogeneous impacts are used to define the conditions under which macroeconomic information becomes important for the firm.     

Do star analysts know more firm-specific information? Evidence from China

Using a unique database in China, we extend the literature to further distinguish the information production role of star vs. non-star analysts. We confirm the general conclusion of a positive association between analyst coverage and stock return synchronicity measured by a firm’s R² in China. The findings from star analysts, however, show that star analyst coverage actually decreases stock return synchronicity. We contend that the firm-specific human capital in star analysts helps the analysts overcome the challenges of information production in an emerging market. The superior firm-specific human capital argument of star analysts is further supported by the negative association of star analysts’ firm-specific experiences and stock return synchronicity. Our conclusions are robust to different specifications of star analyst presence and different definitions of analysts’ firm-specific experiences. We also find that star analysts exhibit a more accurate earnings forecast than non-star analysts.     

Macroeconomic Conditions and the Puzzles of Credit Spreads and Capital Structure

I build a dynamic capital structure model that demonstrates how business cycle variation in expected growth rates, economic uncertainty, and risk premia influences firms' financing policies. Countercyclical fluctuations in risk prices, default probabilities, and default losses arise endogenously through firms' responses to macroeconomic conditions. These comovements generate large credit risk premia for investment grade firms, which helps address the credit spread puzzle and the under‐leverage puzzle in a unified framework. The model generates interesting dynamics for financing and defaults, including market timing in debt issuance and credit contagion. It also provides a novel procedure to estimate state‐dependent default losses.     

Valuation of insurers’ contingent capital with counterparty risk and price endogeneity

This study develops a structural framework to value insurers’ contingent capital with counterparty risk (CR) and overcomes the problem of price endogeneity (PE) in the valuation model. Our results on the focal contingent capital instrument – catastrophe equity put option (CatEPut) – indicate that prices can be significantly overestimated without considering CR and be significantly underestimated without considering PE. This study also examines how CatEPuts affect the buyer’s probability of default (PD). Our results show that buying a CatEPut lowers the PD for high-risk insurers, but not necessarily so for low-risk insurers; however, without taking CR and PE into account, one may significantly overestimate the credit enhancement provided by the CatEPuts.     

How do policy and information shocks impact co-movements of China’s T-bond and stock markets?

We investigate the impacts of policy and information shocks on the correlation of China’s T-bond and stock returns, using originally the asymmetric dynamic conditional correlation (DCC) model that allows for the coexistence of opposite-signed asymmetries. The co-movements of China’s capital markets react to large macroeconomic policy shocks as evidenced by structural breaks in the correlation following the drastic 2004 macroeconomic austerity. We show that the T-bond market and the bond–stock correlations bear more of the brunt of the macroeconomic contractions. We also find that the bond–stock correlations respond more strongly to joint negative than joint positive shocks, implying that investors tend to move both the T-bond and stock prices in the same direction when the two asset classes have been hit concurrently by bad news, but tend to shift funds from one asset class to the other when hit concurrently by good news. However, the stock–stock correlation is found to increase for joint positive shocks, indicating that investors tend to herd more for joint bullish than joint bearish stock markets in Shanghai and Shenzhen.     

The importance of IRS monitoring to debt pricing in private firms

We examine the link between Internal Revenue Service (IRS) monitoring and yield spreads on private firms’ 144A bond issues. After controlling for security-specific and other firm-specific determinants, we provide evidence that debt financing is cheaper when the probability of a face-to-face IRS audit is higher. Consistent with another prediction, we find that IRS oversight has a stronger impact on bond pricing for private firms with high ownership concentration, which suffer worse agency problems between controlling shareholders and outside investors. Collectively, our research implies that IRS monitoring plays a valuable corporate governance role by reducing information asymmetry evident in borrowing costs.     

Risk capital allocation for RORAC optimization

This paper considers the financial optimization problem of a firm with several sub-businesses striving for its optimal RORAC. An insightful example shows that the implementation of classical gradient capital allocation can be suboptimal if division managers are allowed to venture into all business whose marginal RORAC exceeds the firm’s RORAC. The marginal RORAC requirements are refined by adding a risk correction term that takes into account the interdependencies of the risks of different lines of business. It is shown that under certain stationarity conditions this approach can guarantee that the optimal RORAC will eventually be achieved.     

Default and Recovery Risk Dependencies in a Simple Credit Risk Model

This paper provides evidence for the relationship between credit quality, recovery rate, and correlation. The paper finds that rating grade, rating shift, and macroeconomic factors provide a highly significant explanation for default risk and recovery risk of US bond issues. The empirical data suggest that default and recovery processes are highly correlated. Therefore, a joint approach is required for estimating time‐varying default probabilities and recovery rates that are conditional on default. This paper develops and applies such a model.