Event Of Default Research Articles

The Implicit Government Guarantee: Evidence from an Unexpected Corporate Bond Default Event

Using an unexpected state-owned-enterprise bond default in 2020 and a difference-in-difference method, we identify the size of implicit government guarantee in the corporate bond market of China. We find that the local government fiscal burden is the source of the implicit government guarantee. The default event has uneven impacts on different regions through the fiscal burden channel.

Dependent Credit Default Risk Modelling Using Bernoulli Mixture Models

Generalized Linear Mixed Models (GLMMs) can be used to model the occurrence of defaults in a loan or bond portfolio. In this paper, we used a Bernoulli mixture model, a type of GLMMs, to model the dependency of default events. We discussed how Bernoulli mixture models can be used to model portfolio credit default risk, with the probit normal distribution as the link function. The general mathematical framework of the GLMMs was examined, with a particular focus on using Bernoulli mixture models to model credit default risk measures. We showed how GLMMs can be mapped into Bernoulli mixture models. An important aspect in portfolio credit default modelling is the dependence among default events, and in the GLMM setting, this may be captured using the so called random effects. Both fixed and random effects influence default probabilities of firms and these are taken as the systemic risk of the portfolio. After describing the model, we also conducted an empirical study for the applicability of our model using Standard and Poor’s data incorporating rating category (fixed effect) and time (random effect) as components of the model that constitute to the systemic risk of the portfolio. We were able to find the estimates of the model parameters using the Maximum Likelihood (ML) estimation method.

Green finance and the restructuring of the oil-gas-coal business model under carbon asset stranding constraints

Against the background of climate change, oil-gas-coal companies are particularly concerned by the notion of stranded assets, i.e., the fact that known fossil reserves cannot be burnt should limitations on greenhouse gas emissions become more stringent. Those assets can suffer from unanticipated or premature write-downs, devaluations or conversion to liabilities. This paper simulates the impacts of carbon stranded assets for 17 major oil-gas-coal firms' value until the horizon 2050. The core of the paper is a stochastic model with stopping times that determines by initial conditions (reserves and extraction rates) which companies are left with ‘stranded assets.’ In the business-as-usual scenario, one-quarter of the Earth's capacity for absorbing emissions will be depleted by 2050. With stringent emissions-curbing policies, an environmental gain of 80% can be achieved. Without a restructuring of their business model, many oil-gas-coal companies stand out from our simulations as being particularly vulnerable to the financial risks of bankruptcies and default events.

An Incentive Mechanism Model of Credit Behavior of SMEs Based on the Perspective of Credit Default Swaps

The rapid development of credit default swap (CDS) market has changed the manner of credit risk management of banks to some extent and has had a new influence on the bank-enterprise credit model. In this study, the credit financing process of credit risk in small- and medium-sized enterprises (SMEs) gathers within a bank, which makes it difficult for SMEs to raise funds. On the basis of the perspective of CDS, we construct an incentive game model of bank-enterprise credit behavior and analyze the influence mechanism of the credit financing of SMEs on CDS contract coupon rate, CDS payout ratio, bank-enterprise credit effort, and loan recovery rate when considering CDS. The result shows that the CDS contract leads to insufficient supervision after a bank loan, the moral hazard of the SMEs rises, and the probability of credit default events increases. In addition, in view of CDS, the SMEs can access more credit funds.

The economics of debt collection

This paper considers the role of policies that protect consumers from aggressive debt collection tactics. In general, rational consumers will select credit contracts based not only on price but also on the lender’s practices in the case of default and might prefer a contract that offers higher interest rates but more lenient collection tactics. We consider a model in which consumers can be optimistic in that they underestimate, at the time they borrow, the chance of a negative income shock that could cause them to default. This mindset induces consumers to perceive the welfare associated with borrowing to be greater than it is and to place less weight on the lender’s collection practices, which may cause them to prefer lower interest rate contracts even if they are accompanied by high collection effort in the event of default. We report supporting evidence that is consistent with consumers being optimistic when they borrow. We develop a two-period model of consumer borrowing that incorporates debt collection: consumers borrow in the first period, and repay, settle or default in the second. We show in both partial and general equilibrium settings that a restriction on debt collection effort can be welfare improving under some conditions. The restriction operates by reducing the gap between perceived and actual consumer welfare for optimistic consumers.

Loss given default decomposition using mixture distributions of in-default events

Modeling loss in the case of default is a crucial task for financial institutions to support the decision making process in the risk management framework. It has become an inevitable part of modern debt collection strategies to keep promising loans on the banking book and to write off those that are not expected to be recovered at a satisfactory level. Research tends to model Loss Given Default directly or to decompose it based on the dependent variable distribution. Such an approach neglects the patterns which exist beneath the recovery process and are mainly driven by the activities made by collectors in the event of default. To overcome this problem, we propose a decomposition of the LGD model that integrates cures, partial recoveries, and write-offs into one equation, defined based on common collection strategies. Furthermore, various levels of data aggregation are applied to each component to reflect the domain that influences each stage of the default process. To assess the robustness of our approach, we propose a comparison with two benchmark models on two different datasets. We assess the goodness of fit on out-of-sample data and show that the proposed decomposition is more effective than state-of-the-art methods, maintaining a strong level of interpretability.

Optimal dividend strategy for an insurance group with contagious default risk

This paper studies the optimal dividend for a multi-line insurance group, in which each subsidiary runs a product line and is exposed to some external credit risk. The default contagion is considered such that one default event may increase the default probabilities of all surviving subsidiaries. The total dividend problem for the insurance group is investigated and we find that the optimal dividend strategy is still of the barrier type. Furthermore, we show that the optimal barrier of each subsidiary is modulated by the default state. That is, how many and which subsidiaries have defaulted will determine the dividend threshold of each surviving subsidiary. These conclusions are based on the analysis of the associated recursive system of Hamilton–Jacobi–Bellman variational inequalities (HJBVIs). The existence of the classical solution is established and the verification theorem is proved. In the case of two subsidiaries, the value function and optimal barriers are given in analytical forms, allowing us to conclude that the optimal barrier of one subsidiary decreases if the other subsidiary defaults.

European Puts, Credit Protection, and Endogenous Default

In a default corridor [Formula: see text] that the stock price can never enter, a deep out-of-the-money American put option replicates a pure credit contract (Carr and Wu, 2011, A Simple Robust Link between American Puts and Credit Protection, Review of Financial Studies 24, 473–505). Assuming discrete (one-period-ahead predictable) cash flows, we show that an endogenous credit-risk model generates, along with the default event, a default corridor at the cash-outflow dates, where [Formula: see text] is given by these outflows (i.e., debt service and negative earnings minus dividends). In this endogenous setting, however, the put replicating the credit contract is not American, but European. Specifically, the crucial assumption that determines an endogenous default corridor at the cash-outflow dates is that equityholders’ deep pockets absorb these outflows; that is, no equityholders’ fresh money, no endogenous corridor.

Measuring the Default Risk of Small Business Loans: Improved Credit Risk Prediction using Deep Learning

This paper suggests using a multilayer artificial neural network (ANN) method, known as deep learning ANN, to predict the probability of default (PD) within the survival analysis framework. Deep learning ANN structures consider hidden interconnections among the covariates determining the PD which can lead to prediction gains compared to parametric statistical methods. The application of the ANN method to a large data set of small business loans demonstrates prediction gains for the method relative to the logit and skewed logit models. These gains mainly concern short term prediction horizons and are more apparent for the type I misclassification error of loan default events, which has important implications for bank loans portfolio management. To identify the effects of covariates on the PD by the ANN structure, the paper proposes a bootstrap sampling method obtaining the distribution of changes of the PD over discrete covariate changes, while controlling for possible interactions among the covariates. We find that the covariates with the most important influence on the PD include the delinquent amount of a loan over its total balance, the payments and the balance of the loan over its installment, as well as the delinquency buckets of a loan. The duration of a loan is also found to be an important factor of default risk.

Tanggung Jawab Pelaku Usaha terhadap Konsumen Go Food dalam Transaksi E-Commerce

Economic transactions have been integrated with internet technology which is often called e-commerce. Although it has been arranged in such a way to protect the rights of consumers, the use of standard agreements in e-commerce transactions still has the risk of large losses to consumers. Based on the background of the problems above, this research was conducted with the aim of elaborating the responsibilities of business actors to consumers in the event of default in the Go Food transaction using e-commerce and describing sanctions for Go Food businesses that apply fraudulent use of e-commerce. Referring to the formulation of the problem, this study was designed using a normative research approach. The method used in the processing of legal materials, namely analytical descriptive data analysis methods. In accordance with the results of data analysis, the responsibility of business operators for defaults in Go Food transactions using e-commerce was to return Go-Pay balances. Sanctions for Go Food businesses that apply cheating in using e-commerce, which result in losses to consumers, were not only limited to the dismissal of partners, but sanctions could also be imposed on irresponsible business actors. The sanctions in question could be in the form of civil, criminal or administrative sanctions.

LEGAL PROTECTION ASPECT OF SELLING DRUG TRANSACTIONS THROUGH ONLINE PHARMACY SERVICE

This writing aims to find out how the form of legal protection against consumers in the event of default in conducting drug sales transactions through online pharmacy services and to find out the form of liability by the seller or pharmacist when committing negligence to consumers in the transaction. This writing uses the normative legal research method with the data collection method used is the Literature Method; Tracing the research material is done by reading, studying, and quoting legislation, and related literature and then the data obtained are analyzed qualitatively.The results of this study address that: (1) the form of legal protection for online pharmacy consumers has been specifically regulated in the health law, the law on health workers and generally regulated in the consumer protection law. (2) The legal consequences obtained by the pharmacist or negligent seller is to provide compensation to consumers if proven to have neglected and are responsible for their profession both in civil and administrative terms. Based on these results, it is recommended: (1) pharmacists or sellers to pay close attention to every doctor's prescription that enters the pharmacy system in order tomaintain the safety and security of consumers in consuming drugs. (2) Consumers or the public must be more careful when receiving drugs from pharmacists so that if pharmacists are negligent, drugs can be replaced immediately before consumption.

Asset redeployability and the choice between bank debt and public debt

A firm with less redeployable assets, which are assets that have fewer alternative uses outside the firm, is more likely to borrow from banks than issue public debt. These findings are consistent with firms with less redeployable assets valuing the ability to renegotiate bank debt contracts instead of selling assets in the event of default. Consistent with this mechanism, firms with lower asset redeployability sell fewer assets following covenant violations. Our results contribute to work on the determinants of which debt markets a firm chooses to borrow from and the role that banks play as intermediaries.

Modeling Portfolio Credit Risk Taking into Account the Default Correlations Using a Copula Approach: Implementation to an Italian Loan Portfolio

This work aims to illustrate an advanced quantitative methodology for measuring the credit risk of a loan portfolio allowing for diversification effects. Also, this methodology can allocate the credit capital coherently to each counterparty in the portfolio. The analytical approach used for estimating the portfolio credit risk is a binomial type based on a Monte Carlo Simulation. This method takes into account the default correlations among the credit counterparties in the portfolio by following a copula approach and utilizing the asset return correlations of the obligors, as estimated by rigorous statistical methods. Moreover, this model considers the recovery rates as stochastic and dependent on each other and on the time until defaults. The methodology utilized for coherently allocating credit capital in the portfolio estimates the marginal contributions of each obligor to the overall risk of the loan portfolio in terms of Expected Shortfall (ES), a risk measure more coherent and conservative than the traditional measure of Value-at-Risk (VaR). Finally, this advanced analytical structure is implemented to a hypothetical, but typical, loan portfolio of an Italian commercial bank operating across the overall national country. The national loan portfolio is composed of 17 sub-portfolios, or geographic clusters of credit exposures to 10,500 non-financial firms (or corporates) belonging to each geo-cluster or sub-portfolio. The outcomes, in terms of correlations, portfolio risk measures and capital allocations obtained from this advanced analytical framework, are compared with the results found by implementing the Internal Rating Based (IRB) approach of Basel II and III. Our chief conclusion is that the IRB model is unable to capture the real credit risk of loan portfolios because it does not take into account the actual dependence structure among the default events, and between the recovery rates and the default events. We underline that the adoption of this regulatory model can produce a dangerous underestimation of the portfolio credit risk, especially when the economic uncertainty and the volatility of the financial markets increase.

Computing credit valuation adjustment solving coupled PIDEs in the Bates model

Credit Value Adjustment is the charge applied by financial institutions to the counter-party to cover the risk of losses on a counterpart default event. In this paper we estimate such a premium under the Bates stochastic model (Bates in The Review of Financial Studies 9(1): 69–107, 1996), which considers an underlying affected by both stochastic volatility and random jumps. We propose an efficient method which improves the Finite-Difference Monte Carlo (FDMC) approach introduced by de Graaf et al. (Journal of Computational Finance 21, 2017) In particular, the method we propose consists in replacing the Monte Carlo step of the FDMC approach with a finite difference step and the whole method relies on the efficient solution of two coupled partial integro-differential equations which is done by employing the Hybrid Tree-Finite Difference method developed by Briani et al. ( arXiv:1603.07225 2016;IMA Journal of Management Mathematics 28(4): 467–500, 2017;The Journal of Computational Finance 21(3): 1–45, 2017). Moreover, the direct application of the hybrid techniques in the original FDMC approach is also considered for comparison purposes. Several numerical tests prove the effectiveness and the reliability of the proposed approach when both European and American options are considered. Subject classification numbers as needed.

Importance sampling with splitting for portfolio credit risk

We consider a credit portfolio with highly skewed exposures. In the portfolio, small number of obligors have very high exposures compared to the others. For the Bernoulli mixture model with highly skewed exposures, we propose a new importance sampling scheme to estimate the tail loss probability over a threshold and the corresponding expected shortfall. We stratify the sample space of the default events into two subsets. One consists of the events that the obligors with heavy exposures default simultaneously. We expect that typical tail loss events belong to the set. In our proposed scheme, the tail loss probability and the expected shortfall corresponding to this type of events are estimated by a conditional Monte Carlo, which results in variance reduction. We analyze the properties of the proposed scheme mathematically. In numerical study, the performance of the proposed scheme is compared with an existing importance sampling method.

Restructuring Lebanese Sovereign Debt: Tackling the Holdout Problem

Lebanon can threaten to take the following three actions to restructure its debt and weaken the position of a potential holdout creditor, such as the Ashmore Group, that holds more than 25% of the outstanding debt. First, unilaterally amend the terms of the Fiscal Agency Agreement pursuant to Para. 23.1 to allow bondholders representing a simple majority of the outstanding debt to amend non-core terms, contrary to the 75% required to pass an extraordinary resolution. Second, initiate the debt restructuring through a debt exchange program with an exit consent that requires participating bondholders to vote to amend the two non-core terms in the Fiscal Agency Agreement: (1) amend the forum selection clause to ensure that in the event of default, the creditor may only bring a lawsuit against the debtor in a Lebanese court (para. 8.3); (2) remove the pari passu clause (para. 3). Third, the Lebanese Parliament enacts a law effectively accomplishing that (1) the New bonds are senior to the Old Bonds; (2) the government may make selective payments on its senior debt until they are paid in full without resulting in default on junior claims; (3) successful creditor-litigants have the obligation to apply the proceeds of a successful judgment against Lebanon to senior creditors. In the event that Lebanon fails in these three actions, Lebanon can still rely on the necessity doctrine as an affirmative defense.

Seeking Alpha From Stock Splits

The authors assess the announcement trading returns on the stocks of splitting firms. The authors join the traditional cumulative abnormal return (CAR) approach to estimating abnormal returns with the economic value added (EVA) style approach to investing. The authors’ empirical results are robust for a sample of more than 4,900 stock splits during the 1997–2014 period. The authors argue that alpha returns may be available by longing the stocks of splitting firms at the announcement and holding the trading position open for 5- to 10-trading days thereafter. A notable finding linked to EVA style is that alpha returns were available by longing the stocks of value-creating growth companies and the underinvesting but still positive EVA companies in need of a growth signal. This contrasts with the stocks of value-destroying growth companies where potential shorting opportunities were available. The authors conclude with CAR results for companies having a one-time stock split versus companies with repeat splits, and the authors assess announcement wealth effects on splitting firms around pre- and postdecimalization of US equity prices. TOPICS:Security analysis and valuation, exchange-traded funds and applications, performance measurement Key Findings • Our CAR and EVA style findings on stock-splitting firms confirm both information signaling at time of split announcement and the potential for alpha-generating trading returns thereafter. • For active investors, alpha-generating trading opportunities may be available by longing the stocks of splitting firms at announcement and holding the position open for some 5- to 10-trading days, with potential shorting opportunities on splitting firms thereafter (default event trading window of 30 days). • Based on EVA style classifications, we find that alpha-generating trading returns on stock splitting firms were potentially available on stocks of value-creating growth companies with positive EVA momentum and the stocks of currently underinvesting but still positive EVA companies in need of a growth signal.

Closed-Form Solution for Defaultable Bond Options under a Two-Factor Gaussian Model for Risky Rates Modeling

In this article, the authors provide a closed-form solution for defaultable bond options under a two-factor Gaussian model for risky rates. The key feature of the proposed stochastic model is the introduction of two stochastic dynamics to address the behavior of both risk-free interest rates and credit spreads where the two sources of risk are correlated. Moreover, the model can match exactly the term structure of defaultable interest rates. In order to model credit-sensitive bonds, the authors assume an intensity-based reduced-form framework where the approach based on the recovery of market value is considered and where the recovery rate is one of the model’s input. Under this framework, the authors derive closed formulas for the price of European options having as their underlying both defaultable zero-coupon bonds and coupon-bearing bonds, which the authors assume are approximately log-normally distributed. Moreover, the options are assumed to be knocked out upon a default event of the bond, with zero value to the option holder. An empirical analysis is performed to illustrate the calibration process of the proposed model using financial market data to price call and put options across different credit ratings and different levels of the loss given default. TOPICS:Factor-based models, derivatives, options Key Findings • An option pricing model that considers the dynamic of risky rates under a two-factor Gaussian model where risky rates are the sum of risk-free rates and credit spreads is proposed. This distributional hypothesis is consistent with negative interest rates and benefits from analytical tractability. • Under this framework, closed formulas for the price of European options having both defaultable zero-coupon bonds and coupon-bearing bonds as their underlying are derived under an approximately log-normal distribution assumption. • A detailed two-step calibration procedure is provided. The first step estimates the parameters of the one-factor Hull-White model for the risk-free rates and the second step estimates the parameters of the model’s credit component under a grid of correlation values.

Pricing vulnerable European options with dynamic correlation between market risk and credit risk

In this paper, we study the valuation of vulnerable European options incorporating the reduced-form approach, which models the credit default of the counterparty. We provide an analytical pricing model in which the components of the state processes, including the dynamics of the underlying asset value and the intensity process corresponding to the default event, are cross-exciting and they could facilitate the description of complex structure of events dependence. To illustrate how our model works, we present an application when the state variables follow specific affine jump-diffusion processes. Semi-analytical pricing formulae are obtained through a system of matrix Riccati equations. The derived formula can be implemented numerically, and we give numerical analysis to investigate the impact of the dynamic correlation between jump risk of the underlying asset value and default risk of the counterparty.

HepMC3 Event Record Library for Monte Carlo Event Generators

We present the HepMC3 library designed to perform manipulations with event records of High Energy Physics Monte Carlo Event Generators (MCEGs). The library is a natural successor of HepMC and HepMC2 libraries used in the present and in the past. HepMC3 supports all functionality of previous versions and significantly extends them. In comparison to the previous versions, the default event record has been simplified, while an option to add arbitrary information to the event record has been implemented. Particles and vertices are stored separately in an ordered graph structure, reflecting the evolution of a physics event and enabling usage of sophisticated algorithms for event record analysis. The I/O functionality of the library has been extended to support common input and output formats of HEP MCEGs, including formats used in Fortran HEP MCEGs, formats used in HepMC2 library and ROOT. The functionality of the library allows the user to implement a customised input or output format. The library is already supported by popular modern MCEGs (e.g. Sherpa and Pythia8) and can replace the older HepMC versions in many others.