Future Default Research Articles

Real regulatory capital management and bank payouts: Evidence from available‐for‐sale securities

AbstractThis study examines whether payout policies create incentives for banks to engage in cherry‐picking activities using available‐for‐sale (AfS) securities, otherwise known as gains trading. Such activities are more likely to arise in situations in which capital ratios would otherwise constrain banks’ ability to distribute resources to shareholders. Using a large sample comprising 766 unique US banks, we find a significant and positive association between total payout and realized gains on AfS securities for banks with low regulatory capital. This is consistent with the conjecture that capital‐constrained banks engage in gains trading to free up resources for dividend payments or share repurchases. When partitioning our sample, we find that capital‐constrained banks realize gains and losses on AfS securities only when it is costly to decrease the payout and when the monitoring level is weaker. Further analyses reveal that banks engaging in gains trading to distribute cash to shareholders exhibit significantly higher levels of future default risk and more negative extreme bank‐specific daily returns, patterns that are consistent with risk‐shifting. Finally, with the advent of Basel III, the practice seems to continue among banks that chose to retain prudential filters.

Sovereign Debt Restructuring: A Modest Proposal

Many sovereigns, including weaker credits, have taken on substantial debt during the COVID-19 pandemic. This raises the prospect of future defaults and sovereign restructurings, which will be informed by debt-sustainability analysis. But when analyzing notable recent sovereign defaults, we find a pattern of serially correlated errors: the analysis at the time of the restructuring is too optimistic about future sovereign debt dynamics. In light of this, I propose that future sustainability analysis should be based on more pessimistic expectations. In turn, this implies that sovereign creditors should face larger losses in future restructurings.

COVID-19 and the Credit Cycle: 2020 Revisited and 2021 Outlook

This study continues the author’s examination and forecasts as to the impact of Covid-19 on the U.S. credit cycle after one and a half years since the pandemic first began. We explore the enormous build-up of global debt even before the pandemic commenced and the subsequent record debt expansion through mid-2021. New debt peaks, especially for non-financial corporate debt, is analyzed as to its potential impact on future default rates and the implications for the U.S. credit markets once again starting a new benign cycle in a continuing low interest rate environment. We ask the question whether the spectacular success of the U.S. central bank and its monetary policy and secondary market purchases has also promoted potentially destructive unforeseen consequences for debt rated BBB and below. Large versus small firm defaults and bankruptcies are examined in both 2020 and 2021, as well as our expectations about these firms’ solvency status once the government and central bank supports diminish and are eliminated. Finally, we introduce the concept of global zombie firms and suggest that this growing phenomenon be analyzed more robustly and critically with new criteria and empirical analysis.

Constrained Efficient Borrowing with Sovereign Default Risk

Using a quantitative sovereign default model, we characterize constrained efficient borrowing by a Ramsey government that commits to income-history-contingent borrowing paths taking as given ex-post optimal future default decisions. The Ramsey government improves upon the Markov government because it internalizes the effects of borrowing decisions in period t on borrowing opportunities prior to t. We show the effect of borrowing decisions in t on utility flows prior to t can be encapsulated by two single dimensional variables. Relative to a Markov government, the Ramsey government distorts borrowing decisions more when bond prices are more sensitive to borrowing, and changes in bond prices have a larger effect on past utility. In a quantitative exercise, more than 80% of the default risk is eliminated by a Ramsey government, without decreasing borrowing. The Ramsey government also has a higher probability of completing a successful deleveraging (without defaulting), while smoothing out the fiscal consolidation.

The Term Structure of Default Probabilities

Accounting standards require that financial institutions must measure default risk with respect to the full maturity of a financial instrument. This requires forecasting of future default probabilities. The forecast of future default probabilities concerns two aspects: forecasting macroeconomic scenarios and future average (with respect to the macro-economy) default probabilities. The present paper addresses the modelling of future average default probabilities. Due to the small number of defaults and observations this poses a difficult problem in the corporate area and requires a parsimonious model to deal with the sparse data situation. The degree of difficulty is made greater by the fact that default probabilities change for corporations via different patterns over time, with the pattern being depend on the initial rating grade. For initial investment-grade ratings the risk of a default increases, while for the bottom of the rating scale default probabilities decrease. To model these different patterns the paper proposes to extend the existing discrete-time survival model and to incorporate an additional time- and co-variate-dependent shape parameter into the hazard function. Using data from Standard & Poor's the paper shows that the shape parameter is able to reproduce the different patterns. The proposed model is bench-marked against a model which does not employ a shape parameter and the results show that the shape parameter improves in-sample and out-of-sample prediction.

Downside Variance Premium, Firm Fundamentals, and Expected Corporate Bond Returns

We find a positive relationship between individual downside variance premia, the difference between risk-neutral and physical expected downside variances, and future corporate bond returns. The hedge portfolio earns the economically substantial and statistically significant excess return of 0.35% (0.39%) per month in value (equal)-weighted returns. The predictive power of downside variance premium is stronger in non-investment-grade (long-maturity) corporate bonds than in investment-grade (short-maturity) ones. We show that downside variance premium positively relates to the likelihood of future default and cash flow uncertainty and negatively relates to future cash flow. When rational investors anticipate a high likelihood of future default, high cash flow uncertainty, or low future cash flow, the current bond price has to decline, resulting in higher future bond returns.

Systemic credit freezes in financial lending networks

This paper develops a network model of interbank lending, in which banks decide to extend credit to their potential borrowers. Borrowers are subject to shocks that may force them to default on their loans. In contrast to much of the previous literature on financial networks, we focus on how anticipation of future defaults may result in ex ante “credit freezes,” whereby banks refuse to extend credit to one another. We first characterize the terms of the interbank contracts and the patterns of interbank lending that emerge in equilibrium. We then study how shifts in the distribution of shocks can result in complex credit freezes that travel throughout the network. We use this framework to analyze the effects of various policy interventions on systemic credit freezes.

Asthma in children during the COVID-19 pandemic: lessons from lockdown and future directions for management

Asthma in children during the COVID-19 pandemic: lessons from lockdown and future directions for management

Do Better Managers Get Better Loan Contracts?

This paper examines the impact of managerial ability on bank loan contracting. We find that firms with higher ability managers obtain more favorable loan contract terms, such as lower loan spreads, less stringent covenants, and longer term maturity. Furthermore, the negative relation between managerial ability and loan spread is concentrated in firms with higher information asymmetry, higher default risk, or lower agency costs of debt. Finally, we find that managerial ability is positively related to firm future performance and negatively related to firm future default risk, indicating that managerial ability affects bank loan contracting via both valuation and information effects.

Creditworthiness Standards between Theory and Practice (a Field Study on the Syrian Commercial Bank in Lattakia)

Creditworthiness standards aims to identify the possibility of future customer credit and payment of benefits, the researcher has sought to verify the use of these standards in the Commercial Bank of Syria as a tool to avoid future credit risk. The research problem stems from the fact that the granting of credit without study the creditworthiness of clients leads to face risks of future defaults in credit repayment, Therefore researcher seeks to investigate whether the Commercial Bank of Syria is studying various standards of creditworthiness as a way to reduce the risk of grants of defaults in the future. The research aims to verify the use of creditworthy standards content in the creditworthiness models in the Commercial Bank of Syria, and in the way it has been introduced to the concept of creditworthiness and justifications for its study, and display the most important models, and a comparison between models. the research has been relying on the historical approaches and the descriptive and analytical in the presentation of the creditworthiness of models, as well as inductive approach in order to access to the most comprehensive creditworthiness of variables to be recommended to follow in the exhibition take credit decision making model. The researcher reached a set of results most important is that there are five standards that are personal, capital, ability to repay, conditions, and guarantees, should be included in any model creditworthiness, and the Commercial Bank of Syria is studying factors, reputation query banking, Purpose, experience and management, and field visit, these standards are contained in the PRISM model, in addition to the big focus at the Commercial Bank of Syria to provide safeguards as a basic term of the credit granting.

Reverse Calculus and nested optimization

Nested optimization problems arise when an agent must take into account the effect of their decisions on their own future behaviour, or the behaviour of others. In these problems, calculating marginal costs and benefits involves differentiating the solutions to nested problems. But are these solutions differentiable functions? We develop a tool called Reverse Calculus, and establish first-order conditions for (i) a Stackelberg leader considering the follower's best response function, (ii) a sovereign borrower considering its own future default policy, and (iii) non-convex dynamic programming problems.

Systemic Credit Freezes in Financial Lending Networks

This paper develops a network model of interbank lending, in which banks decide to extend credit to their potential borrowers. Borrowers are subject to shocks that may force them to default on their loans. In contrast to much of the previous literature on financial networks, we focus on how anticipation of future defaults may result in ex ante “credit freezes,” whereby banks refuse to extend credit to one another. We first characterize the terms of the interbank contracts and the patterns of interbank lending that emerge in equilibrium. We then study how shifts in the distribution of shocks can result in complex credit freezes that travel throughout the network. We use this framework to analyze the effects of various policy interventions on systemic credit freezes. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Slow Moving Debt Crises

We study slow moving debt crises: self-fulfilling equilibria in which high interest rates, due to the fear of a future default, lead to a gradual but faster accumulation of debt, ultimately validating investors’ fear. We show that slow moving crises arise in a variety of settings, both when fiscal policy follows a given rule and when it is chosen by an optimizing government. A key assumption, in all these settings, is that the borrowing government cannot commit to issue a fixed amount of bonds in a given period. We discuss how multiplicity is avoided for low debt levels, for sufficiently responsive fiscal policy rules, and for long enough debt maturities. When the equilibrium is unique, debt dynamics are characterized by a tipping point, below which debt falls and stabilizes and above which debt and default rates grow. (JEL E43, E62, H50, H63)

A consumer credit risk structural model based on affordability: balance at risk

This paper introduces an approach designed for personal credit risk, with possible applications in risk assessment and optimization of debt contracts. We define a structural model related to the financial balance of an individual, allowing for cashflow seasonality and deterministic trends in the process. Based on the proposed model, we develop risk measures associated with the probability of default rates conditional on time. This formulation is best suited to short-term loans, where the dynamics of individuals’ cashflow, such as seasonality and uncertainty, can significantly impact future default rates. In the empirical section of this paper, we illustrate an application by estimating risk measures using simulated data. We also present the specific case of optimization of a financial contract, where, based on an estimated model, we find the yield rate/time to maturity pair that maximizes the expected profit or minimizes the default risk of a short-term debt contract.

Forecasting of Power Corporations’ Default Probability With Nonlinear Kalman Filtering

This paper proposes a systematic method for forecasting default probabilities for financial firms with particular interest in electric power corporations. According to the credit risk theory, a company's closeness to default is determined by the distance of its assets’ value from its debts. The assets’ value depends primarily on the company's market (option) value through a complex nonlinear relation. By forecasting with accuracy the enterprize's option value, it becomes also possible to estimate the future value of the enterprize's asset value and the associated probability of default. This paper proposes a systematic method for forecasting the probability to default for companies (option/asset value forecasting methods) using a new nonlinear Kalman filtering method under the name derivative-free nonlinear Kalman filter. The firm's option value is considered to be described by the Black–Scholes nonlinear partial differential equation (PDE). Using a differential flatness theory, the PDE is transformed into an equivalent state-space model in the so-called canonical form. Using the latter model and by redesigning the derivative-free nonlinear Kalman filter as a m -step-ahead predictor, estimates are obtained of the company's future option values. By forecasting the company's market (option) values, it becomes also possible to forecast the associated asset value and volatility, and finally, to estimate the company's future default risk.

The Relevance of Credit Ratings in Transparent Bond Markets

Mandated public dissemination of over-the-counter transactions in corporate debt securities via the TRACE system dramatically reduces the average short-term market reaction to rating downgrades by both issuer-paid and investor-paid rating agencies. Ratings become relatively more accurate predictors of default and more sensitive to innovations in credit spreads after the introduction of dissemination. However, in transparent markets, they provide no significant information about future defaults beyond that provided by credit spreads. Dissemination increases the efficiency of information aggregation and transmission in bond markets, thereby reducing the incremental information content of ratings and the price impact of rating revisions. Received June 8, 2017; editorial decision January 24, 2018 by Editor Philip Strahan. 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.

Mortgages: Estimating Default Correlation and Forecasting Default Risk

Default correlation is a key driver of credit risk. In the Basel regulatory framework it is measured by the asset value correlation parameter. Though past studies suggest that the parameter is over-calibrated for mortgages — generally the largest asset class on banks’ balance sheets — they do not take into account bias arising from small samples or non-Gaussian risk factors. Adjusting for these biases using a non-Gaussian, non-linear state space model I find that the Basel calibration is appropriate for UK and US mortgages. This model also forecasts mortgage default rates accurately and parsimoniously. The model generates value-at-risk estimates for future mortgage default rates, which can be used to inform stress-testing and macroprudential policy.

Rating Through-the-Cycle: What Does the Concept Imply for Rating Stability and Accuracy?

Credit rating agencies face a difficult trade-off between delivering both accurate and stable ratings. In particular, its users have consistently expressed a preference for rating stability, driven by the transactions costs induced by trading when ratings change frequently. Rating agencies generally assign ratings on a through-the-cycle basis whereas banks' internal valuations are often based on a point-in-time performance, that is they are related to the current value of the rated entity's or instrument's underlying assets. This paper compares the two approaches and assesses their impact on rating stability and accuracy. We find that while through-the-cycle ratings are initially more stable, they are prone to rating cliff effects and also suffer from inferior performance in predicting future defaults. This is because they are typically smooth and delay rating changes. Using a through-the-crisis methodology that uses a more stringent stress test goes halfway toward mitigating cliff effects, but is still prone to discretionary rating change delays.

Default contagion among credit modalities: evidence from Brazilian data

The aim of this paper is to assess the impact of the default of some personal credit modality in the future default of the other modalities. Using Brazilian microdata, we run a logistic regression to estimate the probability of default in a given credit modality, including among the explanatory variables the personal overdue exposure in the other credit modalities. Our results show that such effect is positive and significant, although quantitatively heterogeneous. We also discuss the rationale behind these results. Specifically, it was found that financing credit modalities (vehicle and real estate financing) contaminate more the other credit modalities, as their default may bring to the debtor the loss of the financed good. Moreover, riskier loan categories (overdraft, non-payroll-deducted personal credit and credit card) are more contaminated by the default of other modalities, what is explained by the fact that defaulted individuals have a limited access to less risky credit modalities.

A Classification Approach of Neural Networks for Credit Card Default Detection

By using a neural network system, which is more complex and sophisticated than a simple linear regression model, the classification simulation shall have a better performance. The data was extracted from UCI machine learning Lab which represents Taiwan credit card defaults in 2005 and their previous payment histories. This article mainly tried to determine the factors that strongly predict the future default probability with neural network comparing with linear model shows the advantage of deep learning in financial area.