Joint Default Research Articles

Depicting Risk Profile over Time: A Novel Multiperiod Loan Default Prediction Approach

With the rapid development of fintech, the need for dynamic credit risk evaluation is becoming increasingly important. While previous studies on credit scoring have mostly focused on single-period loan default prediction, we call for a new avenue—multiperiod default prediction (MPDP)—to depict risk profiles over time. To address the challenges raised by MPDP, such as monotonic default probability prediction and complex relationship accommodation, we propose a novel approach, hybrid and collective scoring (HACS). We design a hybrid modeling strategy to predict whether and when a borrower will default separately through a default discrimination model and a default time estimation model, respectively, and synthesize them through a probabilistic framework. To accommodate various possible patterns of default time and measure the distribution of default probability over successive time intervals, we propose a joint default modeling method to train the default time estimation model. Empirical evaluations at the model (time-to-default prediction performance and discrimination performance) and mechanism (identifiability and discriminability) levels, as well as impact analyses at the application (granting performance and profitability performance) level, show that HACS outperforms the benchmarked survival analysis and multilabel learning methods on all fronts. It can more accurately predict time-to-default and provide financial institutions and investors better decision-support in granting loans and selecting loan portfolios.

The impact of Federal Reserve Interest Rate Hikes on the Systemic Risk of the U.S. Banking Industry: Based on Semi-Parametric Vine Copula SCCA Model

We use the Vine Copula SCCA model to measure the systemic risk of the US banking industry, and compares the impact of two different events, external risk and Fed rate hike on systemic risk. The results demonstrate that the systemic risk of the US banking industry reached its peak when affected by external shocks (2020) but was quickly released by market rescue measures. When the Fed raised interest rates consecutively in 2022, large banks showed good resistance to risk, but the joint default risk of small and medium-sized banks remained at a significant level for a long time, indicating that interest rate hikes may have increased the systemic risk of the US banking industry. US banking industry should maintain good risk management and prudent operation, and the regulatory intensity they receive also needs to be strengthened.

THE CORRELATION IN CORPORATE DEFAULTS: EVIDENCE FROM NON-FINANCIAL LISTED COMPANIES IN SRI LANKA

Default correlation is the extent to which one company's default causes another's default. It measures the relationship between a firm’s individual default probability and the joint default probability among firms. This study makes theoretical contributions by examining the degree of default correlation among non-financial listed firms in terms of credit quality, time horizon, systematic risk, firm size, and liquidity. The researcher selected 550 firm-year observations (240 for default and 310 for non-default) consisting of 55 non-financial listed companies in Sri Lanka from 2012 to 2021. The default firms are selected using the three criteria during the selection period: suffering from losses, having negative net worth, or suffering from negative operating cash flows for more than three consecutive years. The non-default firms are selected by a couple with the default firms based on the same industry and the highest market capitalization. Altman’s (1968) Z-score model is used to determine the credit ratings as high, medium, and low. The study follows Lucas’s (1995) proposed standard binomial approach to measure the default correlation. The study revealed that time horizon, poor credit ratings, high beta, and small size increase the default correlation in the Sri Lankan context; however, liquidity is not a good measurement for default correlation analysis in Sri Lankan context. Credit risk management could underestimate the portfolio credit risk without considering the default correlation. The dynamic nature of default correlation shows that an increment in default risk of individual credit assets engages with a disproportionate increment in portfolio credit assets.

Evolutionary game analysis of rent seeking in inventory financing based on blockchain technology

AbstractThis study constructs a tripartite evolutionary game model composed of a small and medium‐sized enterprise (SME), a supervisory enterprise, and a bank for the inventory financing model. The effects of blockchain technology on rent seeking in supply chain finance are calculated with focuses on its mechanism and advantages in inventory financing from the theoretical perspective. The influence of the parameters on the evolutions is likewise determined. Results show that for high‐value financing business, compared with the traditional model, the bank's application of blockchain can accelerate the system to the stable state, in which the SME complies with the contract, the supervisor does not seek collusion, and the bank thoroughly investigates the relationships of enterprises, and the enhancement of blockchain's automatic monitoring capability does not lead to the bank's inaction on regulation. For low‐value financing business, in response to the problem of collusion between the SME and the supervisor for additional benefits in the traditional model, the bank can monitor fund transfers within enterprises and unauthorized pledge release through smart contracts embedded in the blockchain system to automatically detect joint defaults, and effectively avoid the negative stable state of the bank indulging in inter‐enterprise relations which triggers collusion to embezzle financing. In addition, the bank can enhance the robustness of inventory financing service and curb rent‐seeking behavior in the blockchain inventory financing through strict penalties on the supervisor for its violations, monitoring abnormal capital flows of the SME, confirming appropriate credit rate gap, and moderately raising the service value threshold.

A contagion process with self-exciting jumps in credit risk applications

The modeling of the probability of joint default or total number of defaults among the firms is one of the crucial problems to mitigate the credit risk since the default correlations significantly affect the portfolio loss distribution and hence play a significant role in allocating capital for solvency purposes. In this article, we derive a closed-form expression for the default probability of a single firm and probability of the total number of defaults by time $t$ in a homogeneous portfolio. We use a contagion process to model the arrival of credit events causing the default and develop a framework that allows firms to have resistance against default unlike the standard intensity-based models. We assume the point process driving the credit events is composed of a systematic and an idiosyncratic component, whose intensities are independently specified by a mean-reverting affine jump-diffusion process with self-exciting jumps. The proposed framework is competent of capturing the feedback effect. We further demonstrate how the proposed framework can be used to price synthetic collateralized debt obligation (CDO). Finally, we present the sensitivity analysis to demonstrate the effect of different parameters governing the contagion effect on the spread of tranches and the expected loss of the CDO.

Assessing systemic fragility: a probabilistic perspective

This paper introduces several measures of systemic fragility of euro area banks and sovereigns using a procedure for a consistent estimation of individual and joint default risk. Our measures not only capture the dynamic dependence between sovereigns and banks but also provide rankings of banks and sovereigns according to their systemic risk contribution by applying a leave-one-out approach and quantifying cross-sectoral contagion. Our analysis documents a rise in banking systemic fragility in the euro area from the onset of the subprime crisis, which was followed by an increase in sovereign systemic risk after Lehman Brothers filed for bankruptcy in September 2008. Our ranking measure manages to detect that Greece, Portugal and Ireland made the largest systemic fragility contributions during the sovereign debt crisis in the euro area. We also find that a hypothetical default of one of the safest countries in the euro area (eg, Germany) would have far-reaching detrimental consequences for the stability of the banking system. These results have important policy implications and add to our understanding of systemic risk of sovereigns and banks.

Machine Learning Techniques in Joint Default Assessment

Machine Learning Techniques in Joint Default Assessment

Implications of Default Dependency on Portfolio Risk

We highlight important and specific characteristics of default risk and methodological implications. In a simulation contrasting independent, Gaussian and Clayton copulas, we also show that joint default probabilities might be a hidden source of risk in conventional portfolio models of default.

Counterparty risk valuation on credit-linked notes under a Markov Chain framework

A credit-linked note (CLN) is a note paying an enhanced coupon to investors for bearing the credit risk of a reference entity. In this paper, we study the counterparty risk on CLNs under a Markov chain framework, and introduce a Markov copula model to describe joint defaults between the reference entity underlying the CLN and CLN issuer. Assuming that the respective default intensities are directly and inversely proportional to the interest rate, which follows a CIR process, we obtain the explicit formulae for CLN values through a PDE approach. Finally, credit valuation adjustment (CVA) formula is derived to price counterparty credit risk.

ESTIMATING A JOINT PROBABILITY OF DEFAULT INDEX FOR INDONESIAN BANKS: A COPULA APPROACH

We develop a joint default probability index to signal potential systemic risks in the highly concentrated Indonesian banking industry. To build the index, we estimate bank-level tail risks using monthly bank financial reports. We use the copula approach to derive the joint multivariate dependencies at the bank level, as reflected in the monthly financial reports. Our results, which are based on a sample of 104 banks fromDecember 2003 to April 2020, show joint multivariate dependencies at the bank level suggesting that the standard univariate normal distribution is unsuitable for capturing tail risks of individual banks. Our index accurately captures the global financial crisis of 2007-2008 indicating that it is a valid joint default probability index. Further, our index also signaled a higher degree of joint default before the COVID-19 outbreak in2020, suggesting that it is a good indicator of potential systemic risk in the economy.

A Study on Operational Risk and Credit Portfolio Risk Estimation Using Data Analytics*

ABSTRACTIn this article we consider operational risk and use data analytics to estimate the credit portfolio risk. Specifically, we consider situations in which managers need to make the optimal operational decision on total provision for risk to hedge against the potential risk in the entire supply chain. We build a new structural credit model integrated with data analytics to analyze the joint default risk of credit portfolio. Our model enables the decision maker to better assess the risk of a supply chain, so that they could determine the optimal operational decisions with total provision for risk, and react in a timely manner to economic and environmental changes. We propose an efficient simulation method to estimate the default probability of the credit portfolio with the risk factors having the multivariate t‐copula. Moreover, we develop a three‐step importance sampling (IS) method for the t‐copula credit portfolio risk measurement model to achieve an accurate estimation of the tail probability of the credit portfolio loss distribution. We apply the Levenberg–Marquardt algorithm to estimate the mean‐shift vector of the systematic risk factors after the probability measure change. Besides, we empirically examine the changes in the credit portfolio risks of 60 listed Chinese firms in different industries using our proposed method. The results show that our model can help the decision maker make the optimal operational decisions with total provision for risk, which hedges against the potential risk in the entire supply chain.

Financial derivatives and default dependence: a time-varying copula approach

ABSTRACT The fast development of financial derivatives links financial institutions more closely. In this paper, we investigate the joint default dependence among financial institutions and its association with the recent development of financial derivatives. Two interesting findings emerge. First, time-varying default risk dependencies of financial institutions are found during our sample period. Second, the fast growth of derivatives markets contributes to the rising correlated default risk among financial institutions and further leads to an increase in systemic risk. We show that the default correlation spike coincides with the boom in the US credit derivatives market.

Corporate governance and correlation in corporate defaults

AbstractResearch Question/IssueThis study examines the effect of weak corporate governance in terms of concentrated ownership, low board effectiveness, low financial transparency and higher shareholder rights on default correlation when firms have different credit qualities.Research Findings/InsightsUsing historical default data in the United States from 2000 to 2015, we find that the degree of default correlation increases disproportionately for firms with concentrated ownership, low board effectiveness, low financial transparency and disclosures, and higher shareholder rights. More importantly, the effect of weak corporate governance on default correlation is high during a financial crisis.Theoretical/Academic ImplicationsThis is one of the first studies testing the impact of corporate governance on the correlation in corporate defaults. It indicates new avenues of research for both corporate governance and credit risk management in relation to why joint default probabilities vary among firms.Practitioner/Policy ImplicationsOur results imply that good corporate governance is essential for credit risk management because poor corporate governance may increase individual default risk and create the domino effect of credit defaults. Practitioners and policy makers should enhance control over poor governance practices to reduce the probabilities of default. Moreover, the impact of corporate governance on correlation in corporate defaults is more pronounced in financial crises and warrants consideration from policy makers to take steps toward cushioning its effects.

네트워크 모형을 이용한 우리나라 은행부문의 시스템리스크 측정 (Measurement of the Korean Banking Sector Systemic Risk using Network Model)

Korean Abstract: 글로벌 금융위기 이후 시스템리스크의 중요성이 부각되고 있다. 본 논문은 은행부문에서 개별 은행의 신용위험을 측정하고 은행 사이의 네트워크 구조에 따른 위험 전이를 반영한 시스템리스크 측정방법을 사용하였다. 먼저 개별 금융기관 재무 상태를 반영한 신용위험의 시계열적 변화를 살펴보았다. 또한 신용위험 측정 과정에서 추산된 개별 은행 자산가치 사이의 횡단면적 연계구조를 상관계수, 조건부 부도발생확률, 결합부도확률, Granger 인과관계 등 다양한 방법으로 추정하여 은행 부문 시스템리스크를 측정하고, 도출된 시스템리스크의 시계열적 움직임을 살펴보았다. 다음으로 시스템리스크 내에서 개별 은행들이 가지고 있는 파생적 특성을 찾고, 각 은행이 시스템 연계구조 네트워크에서 어떤 역할을 하고 있는지, 어떤 은행이 중심적 위치에 해당하는 지에 대해 분석하였다. 이는 기존 시스템리스크 연구들이 시도하지 못한 부분이다. 본 논문이 사용한 분석방법은 은행부문 시스템 안정성을 관리 감독해야 하는 감독당국에게 다양한 시스템리스크 척도를 제공할 뿐만 아니라 상시로 시장을 감독할 수 있는 유용한 수단을 제시한다. 결론적으로 시장 전체 특성을 파악하는 척도와 함께 개별은행의 미시적 특성도 같이 제공한다는 측면에서 본 연구 의의가 있다. English Abstract: The importance of systemic risk has been highlighted since the 2008 global financial crisis. In this paper, we propose a model that considers the cross-sectional and time series aspects of systemic risk simultaneously. Using a network model, we analyze the systemic risk within the banking sector. As a result, we measured the time series of credit risk reflecting the financial status of individual financial institutions. We also estimate the banking sector systemic risk using various methods such as correlation coefficient, conditional default probability, joint default probability, and Granger causality method. The analysis method of this paper provides a variety of systemic risk measures to central banks that analyze the stability of the system in the banking sector and may prove a useful means of supervising the market at all times. In conclusion, this study is meaningful in that it provides both the characteristics of the market as a whole and the micro characteristics of individual banks connected with system risk.

Singularity Bias, Systemic Risk and Credit Indexes

Singular distributions, such as the Marshall-Olkin one, assign a probability mass to the simultaneous occurrence of events. Aim of this paper is to: i) provide systemic risk measures based on singular distributions; ii) evaluate the presence of a singular component in the joint default distribution of financial institutions; iii) evaluate the impact on the systemic measure of the use of absolutely continuous distributions if the true distribution is singular (we call this singularity bias). The analysis is carried out in a set of hidden common shocks models with Archimedean dependence for which the simultaneous shock intensity is proportional to the CDS credit index of the banks involved, a key feature to define a test of the presence of a singularity. The model is applied to a panel of European banks in the sovereign crisis period and after the crisis, and the specification test based on credit indexes shows clear evidence of the presence of a singular component during the crisis, while this hypothesis is rejected at the 10% probability level in the post crisis period. In the crisis period we evaluate the impact of the singularity bias on two measures representative of the actuarial approach to systemic risk. The singularity bias induces a severe underestimation of last-to-default systemic risk measures, while the sign and magnitude of the effect is more difficult to predict for measures that take into account the default of all subsets of banks.

Fair valuation of cliquet-style return guarantees in (homogeneous and) heterogeneous life insurance portfolios

ABSTRACTParticipating life insurance contracts allow the policyholder to participate in the annual return of a reference portfolio. Additionally, they are often equipped with an annual (cliquet-style) return guarantee. The current low interest rate environment has again refreshed the discussion on risk management and fair valuation of such embedded options. While this problem is typically discussed from the viewpoint of a single contract or a homogeneous* insurance portfolio, contracts are, in practice, managed within a heterogeneous insurance portfolio. Their valuation must then – unlike the case of asset portfolios – take account of portfolio effects: Their premiums are invested in the same reference portfolio; the contracts interact by a joint reserve, individual surrender options and joint default risk of the policy sponsor. Here, we discuss the impact of portfolio effects on the fair valuation of insurance contracts jointly managed in (homogeneous and) heterogeneous life insurance portfolios. First, in a rather general setting, including stochastic interest rates, we consider the case that otherwise homogeneous contracts interact due to the default risk of the policy sponsor. Second, and more importantly, we then also consider the case when policies are allowed to differ in further aspects like the guaranteed rate or time to maturity. We also provide an extensive numerical example for further analysis.

Market-Implied Systemic Risk and Shadow Capital Adequacy

This paper presents a forward-looking approach to measure systemic solvency risk using contingent claims analysis (CCA) as a theoretical foundation for determining an institution’s default risk based on the uncertainty in its asset value relative to promised debt payments over time. Default risk can be quantified as market-implied expected losses calculated from integrating equity market and balance sheet information in a structural default risk model. The expected losses of multiple banks and their non-parametric dependence structure define a multivariate distribution that generates portfolio-based estimates of the joint default risk using the aggregation technique of the Systemic CCA framework (Jobst and Gray, 2013). This market-implied valuation approach (‘shadow capital adequacy’) endogenises bank solvency as a probabilistic concept based on the perceived default risk (in contrast to accounting-based prudential measures of capital adequacy). The presented model adds to the literature of analytical tools estimating market-implied systemic risk by augmenting the CCA approach with a jump diffusion process of asset changes to inform a more comprehensive and flexible assessment of common vulnerabilities to tail risks of the four largest UK commercial banks.

Score-Driven Systemic Risk Signaling for European Sovereign Bond Yields and CDS Spreads

We compute joint sovereign default probabilities as coincident systemic risk indicators. Instead of commonly used CDS spreads, we use government bond yield data which provide a longer data history. We show that for the more recent sample period 2008--2015, joint default probabilities based on CDS and bond yield data yield similar results. For the period 1987-2008, only the bond yield data can be used to shed light on European sovereign systemic stress. We also show that simple averages of rolling pairwise correlations do not always yield intuitive systemic risk indicators.

Contagion in Financial Systems: A Bayesian Network Approach

We develop a structural default model for interconnected financial institutions in a probabilistic framework. For all possible network structures we characterize the joint default distribution of the system using Bayesian network methodologies. Particular emphasis is given to the treatment and consequences of cyclic financial linkages. We further demonstrate how Bayesian network theory can be applied to detect contagion channels within the financial network, to measure the systemic importance of selected entities on others, and to compute conditional or unconditional probabilities of default for single or multiple institutions.

Model Risk in FRTB: The Case of the Default Risk Charge

The recent Fundamental Review of the Trading Book (FRTB) resulted in revised standards for capital requirements for market risks in a bank’s trading book. As part of the ruleset, default risk needs to be measured and capitalized through a dedicated Default Risk Charge (DRC). With the DRC as an extreme tail risk measure at 99.9% confidence level for portfolio default losses at a one-year horizon, there is inherent model risk associated with the reflection of joint defaults. Wilkens and Predescu (2017) proposed an overall framework for modeling the DRC that is based on a Gaussian factor copula model to capture the coincidence of defaults. This paper assesses the resulting model risk by analyzing alternative copulas (Gaussian, Student t, and Clayton) and the influence on the DRC figures with the help of a set of example portfolios. The copula choice can affect the DRC considerably, especially for directional and less diversified portfolios; the influence on typical larger-scale, diversified portfolios is much less pronounced. The uncertainty arising from the calibration of any copula from only a few data points – as implied by the regulation – is at least of equal importance as the selection of the dependence model itself.