Academic literature on the topic 'Default Risk Charge (DRC)'

In the Fundamental Review of the Trading Book (FRTB), the latest regulation for minimum capital market risk requirements, one of the major changes, is replacing the Incremental Risk Charge (IRC) with the Default Risk Charge (DRC). The DRC measures only the default and does not consider the migration rating risk. The second new change in this approach was that the DRC now includes equity assets, contrary to the IRC. This paper studies DRC modeling under the Internal Model Approach (IMA) and the regulator conditions that every DRC component must respect. The FRTB presents the DRC measurement as Value at Risk (VaR) over a one-year horizon, with the quantile equal to 99.9%. We use multifactor adjustment to measure the DRC and compare it with the Monte Carlo Model to understand how the approach fits. We then define concentration in the DRC and propose two methods to quantify the concentration risk: the Ad Hoc and Add-On methods. Finally, we study the behavior of the DRC with respect to the concentration risk.

AbstractIn the present paper we consider the Default Risk Charge (DRC) measure as an effective alternative to the Incremental Risk Charge (IRC) one, proposing its implementation by a quasi exhaustive-heuristic algorithm to determine the minimum capital requested to a bank facing the market risk associated to portfolios based on assets issued by several financial agents. While most of the banks use the Monte Carlo simulation approach and the empirical quantile to estimate this risk measure, we provide new computational approaches, exhaustive or heuristic, currently becoming feasible because of both the new regulation and to the high speed - low cost technology available nowadays. Concrete algorithms and numerical examples are provided to illustrate the effectiveness of the proposed techniques.

This work evaluates some changes proposed by the Basel Committee on Banking Supervision in regulating capital allocation in the trading book for equities following a company default. In the last decade, the committee designed some measures to account for the risk of a company default that the ten-day value-at-risk measure does not capture. The first and more conservative measure designed to capture the effect of defaults was the incremental risk charge. With time, this measure evolved into the default risk charge. We use a Merton model to compute the probability of default and compare this probability to simulated asset returns in order to compute the one-year value-at-risk and capture the risk of a company default. The analysis compares portfolios of Ibovespa companies and S&P 500 companies. Additionally, we propose a method to account for the correlation in the companies and compare the effects of the standard method of capital allocation to those of our models.

This study selected factors influencing the default risk of micro- and small-sized enterprises (MSEs) from the perspective of both financial and non-financial indicators and constructed an identification model of the influencing factors for the default risk of MSEs by logistic regression, using the data on loans borrowed by 2492 MSEs from a city commercial bank in Gansu Province as the sample. In addition, the robustness and prediction effect of the model were tested. The empirical results showed that the logistic model has good robustness and high predictive ability. The quick ratio, total asset turnover, return on net assets, sales growth rate and total assets growth rate had significant negative impacts on the default risk for the loans taken out by MSEs; the loan maturity and loan amount had remarkable positive impacts on the default risk; non-financial indicators (e.g., the nature of the enterprise, method of obtaining the loan and educational background of the person in charge) had significant impacts on the default risk. Based on the results, this manuscript provides solutions to address the default risk of MSEs and makes suggestions from the perspectives of database building, full-cycle management and dynamic assessment of guarantee capacity.

We document that the credit spread on consumer unsecured debt exhibits a persistent, hump-shaped response to an increase in the charge-off rate. This stylized fact poses a significant challenge for a standard model of consumer default in which lenders have rational expectations and, therefore, the credit spread continuously adjusts to reflect the true default incentives of each borrower. In an effort to explain this feature of the data, we construct a model of consumer default with countercyclical income risk in which lenders learn about default risk over time by observing the history of repayment decisions, as is the case in practice. In addition to matching credit spread dynamics, allowing lenders to learn about default risk substantially improves the model’s ability to generate realistic business cycle fluctuations in the consumer unsecured credit market and match the cross-sectional distribution of unsecured debt and dispersion of interest rates observed in the data.

The peer-to-peer (P2P) strategy as a new trading scheme has recently gained attention in local electricity markets. This is a practical framework to enhance the flexibility and reliability of energy hubs, specifically for industrial prosumers dealing with high energy costs. In this paper, a Norwegian industrial site with multi-energy hubs (MEHs) is considered, in which they are equipped with various energy sources, namely wind turbines (WT), photovoltaic (PV) systems, combined heat and power (CHP) units (convex and non-convex types), plug-in electric vehicles (EVs), and load-shifting flexibility. The objective is to evaluate the importance of P2P energy transaction with on-site flexibility resources for the industrial site. Regarding the substantial peak power charge in the case of grid power usage, this study analyzes the effects of P2P energy transaction under uncertain parameters. The uncertainties of electricity price, heat and power demands, and renewable generations (WT and PV) are challenges for industrial MEHs. Thus, a stochastically based optimization approach called downside risk constraint (DRC) is applied for risk assessment under the risk-averse and risk-neutral modes. According to the results, applying the DRC approach increased by 35% the operation cost (risk-averse mode) to achieve a zero-based risk level. However, the conservative behavior of the decision maker secures the system from financial losses despite a growth in the operation cost.

We propose a Credit Value Adjustment (CVA) model capturing the Wrong Way Risk (WWR) that is not product-specific and is suitable for large-scale computations. The model is based on a doubly stochastic default process with the default intensities proxied by credit spreads. For different exposure structures, we show how credit–market correlation affects the CVA level, its sensitivities to credit and market factors, its volatility and the quality of hedging. The WWR is most significant for exposures highly sensitive to the market volatility in a situation when credit spreads are at moderate levels but both the market factors and credit spreads are volatile. In such conditions, ignoring credit–market correlations results in important CVA mispricing. While the benefits from hedging are always magnified in the situation of the WWR, the right way exposure case is more delicate: only a well-designed mix of credit and market hedges can bring volatility down. Our results raise doubts on the Basel III policy of recognizing credit but not market hedges for computing the CVA volatility capital charge.

Abstract We examine how bank capital and borrower bargaining power affect loan spreads. Consistent with previous studies, higher bank capital has a negative impact on loan rates, but borrower cash flow has a significant effect on this impact: compared with high-capital banks, low-capital banks charge more for borrowers with low cash flow, but offer greater marginal discounts as these borrowers’ cash flow rises. These effects are largely focused on more bank-dependent borrowers. We find some evidence that low-capital banks charge a higher premium for bank-dependent borrowers’ systematic risk, but not for their total equity risk or default risk. Received January 27, 2015; editorial decision July 7, 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.

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This is one of the first works to address the issue of evaluating the effect of default for capital allocation in the trading book, in the case of public equities. And more specifically, in the Brazilian Market. This problem emerged because of recent crisis, which increased the need for regulators to impose more allocation in banking operations. For this reason, the BIS committee, recently introduce a new measure of risk, the Incremental Risk Charge. This measure of risk, is basically a one year value-at-risk, with a 99.9% confidence level. The IRC intends to measure the effects of credit rating migrations and default, which may occur with instruments in the trading book. In this dissertation, the IRC was adapted for the equities case, by not considering the effect of credit rating migrations. For that reason, the more adequate choice of model to evaluate credit risk was the Moody’s KMV, which is based in the Merton model. This model was used to calculate the PD for the issuers used as case tests. After, calculating the issuer’s PD, I simulated the returns with a Monte Carlo after using a PCA. This approach permitted to obtain the correlated returns for simulating the portfolio loss. In our case, since we are dealing with stocks, the LGD was held constant and its value based in the BIS documentation. The obtained results for the adapted IRC were compared with a 252-day VaR, with a 99% confidence level. This permitted to conclude the relevance of the IRC measure, which was in the same scale of a 252-day VaR. Additionally, the adapted IRC was capable to anticipate default events. All result were based in portfolios composed by Ibovespa index stocks.
Esse é um dos primeiros trabalhos a endereçar o problema de avaliar o efeito do default para fins de alocação de capital no trading book em ações listadas. E, mais especificamente, para o mercado brasileiro. Esse problema surgiu em crises mais recentes e que acabaram fazendo com que os reguladores impusessem uma alocação de capital adicional para essas operações. Por essa razão o comitê de Basiléia introduziu uma nova métrica de risco, conhecida como Incremental Risk Charge. Essa medida de risco é basicamente um VaR de um ano com um intervalo de confiança de 99.9%. O IRC visa medir o efeito do default e das migrações de rating, para instrumentos do trading book. Nessa dissertação, o IRC está focado em ações e como consequência, não leva em consideração o efeito da mudança de rating. Além disso, o modelo utilizado para avaliar o risco de crédito para os emissores de ação foi o Moody’s KMV, que é baseado no modelo de Merton. O modelo foi utilizado para calcular a PD dos casos usados como exemplo nessa dissertação. Após calcular a PD, simulei os retornos por Monte Carlo após utilizar um PCA. Essa abordagem permitiu obter os retornos correlacionados para fazer a simulação de perdas do portfolio. Nesse caso, como estamos lidando com ações, o LGD foi mantido constante e o valor utilizado foi baseado nas especificações de basiléia. Os resultados obtidos para o IRC adaptado foram comparados com um VaR de 252 dias e com um intervalo de confiança de 99.9%. Isso permitiu concluir que o IRC é uma métrica de risco relevante e da mesma escala de uma VaR de 252 dias. Adicionalmente, o IRC adaptado foi capaz de antecipar os eventos de default. Todos os resultados foram baseados em portfolios compostos por ações do índice Bovespa.

The recent capital market shock events have drawn increased regulatory attention to the procyclical nature of the forthcoming Basel market risk regime, the Fundamental Review of Trading Book (FRTB). The overall objective of this research is to evaluate whether the quantitative improvements made in FRTB can mitigate the Basel market risk capital framework’s impact on business cycles. The research found no strong evidence that FRTB would reduce the overall procyclicality of the framework. This study also confirmed that banks can reduce their market risk charge via their modelling choices without amplifying procyclicality in their capital requirements.

Ratingsysteme sind ein zentraler Bestandteil der Kreditrisikomodellierung. Neben der Bonitätsbeurteilung auf der Ebene der Kreditnehmer und der Risikoquantifizierung auf der Ebene der Ratingklassen spielt dabei die Bildung der Ratingklassen eine wesentliche Rolle. Die Literatur zur Ratingklassenbildung setzt auf modellfreie, in gewisser Weise willkürliche Optimierungsverfahren. Ein Ziel der vorliegenden Arbeit ist es, stattdessen ein parametrisches statistisches Modell zur Bildung der Ratingklassen einzuführen. Ein geeignetes Modell ist im Bereich der Bruchpunktschätzung zu finden. Dieses Modell und die in der mathematischen Literatur vorgeschlagenen Parameter- und Intervallschätzer werden in der vorliegenden Arbeit dargestellt und gründlich diskutiert. Dabei wird Wert auf eine anwendungsnahe und anschauliche Formulierung der mathematisch-statistischen Sachverhalte gelegt. Anschließend wird die Methodik der Bruchpunktschätzung auf einen konkreten Datensatz angewendet und mit verschiedenen anderen Kriterien zur Ratingklassenbildung verglichen. Hier erweist sich die Bruchpunktschätzung als vorteilhaft. Aufbauend auf der empirischen Untersuchung wird abschließend weiterer Forschungsbedarf abgeleitet. Dazu werden insbesondere Konzepte für den Mehrklassenfall und für abhängige Daten entworfen
Rating systems are a key component of credit risk modeling. In addition to scoring at borrowers’ level and risk quantification at the level of rating classes, the formation of the rating classes plays a fundamental role. The literature on rating classification uses in a way arbitrary optimization methods. Therefore, one aim of this contribution is to introduce a parametric statistical model to form the rating classes. A suitable model can be found in the area of split-point estimation. This model and the proposed parameter and interval estimators are presented and thoroughly discussed. Here, emphasis is placed on an application-oriented and intuitive formulation of the mathematical and statistical issues. Subsequently, the methodology of split-point estimation is applied to a specific data set and compared with several other criteria for rating classification. Here, split-point estimation proves to be advantageous. Finally, further research questions are derived on the basis of the empirical study. In particular, concepts for the case of more than two classes and for dependent data are sketched

Objective: We consider the following problem from credit risk modeling: Our sample (Xi; Yi), 1 < i < n, consists of pairs of variables. The first variable Xi measures the creditworthiness of individual i. The second variable Yi is the default indicator of individual i. It has two states: Yi = 1 indicates a default, Yi = 0 a non-default. A default occurs, if individual i cannot meet its contractual credit obligations, i. e. it cannot pay back its outstandings regularly. In afirst step, our objective is to estimate the threshold between good and bad creditworthiness in the sense of dividing the range of Xi into two rating classes: One class with good creditworthiness and a low probability of default and another class with bad creditworthiness and a high probability of default. Methods: Given observations of individual creditworthiness Xi and defaults Yi, the field of change point analysis provides a natural way to estimate the breakpoint between the rating classes. In order to account for dependency between the observations, the literature proposes a combination of three model classes: These are a breakpoint model, a linear one-factor model for the creditworthiness Xi, and a Bernoulli mixture model for the defaults Yi. We generalize the dependency structure further and use a generalized link between systematic factor and idiosyncratic factor of creditworthiness. So the systematic factor cannot only change the location, but also the form of the distribution of creditworthiness. Results: For the case of two rating classes, we propose several estimators for the breakpoint and for the default probabilities within the rating classes. We prove the strong consistency of these estimators in the given non-i.i.d. framework. The theoretical results are illustrated by a simulation study. Finally, we give an overview of research opportunities.

Notre thèse consiste à expliquer, en apportant quelques éléments théoriques, les imperfections des stress tests macro-prudentiels d’EBA/BCE, et de proposer une nouvelle méthodologie de leur application ainsi que deux stress tests spécifiques en complément. Nous montrons que les stress tests macro-prudentiels peuvent être non pertinents lorsque les deux hypothèses fondamentales du modèle de base de Gordy-Vasicek utilisé pour évaluer le capital réglementaire des banques en méthodes internes (IRB) dans le cadre du risque de crédit (portefeuille de crédit asymptotiquement granulaire et présence d’une seule source de risque systématique qui est la conjoncture macro-économique), ne sont pas respectées. Premièrement, ils existent des portefeuilles concentrés pour lesquels les macro-stress tests ne sont pas suffisants pour mesurer les pertes potentielles, voire inefficaces si ces portefeuilles impliquent des contreparties non cycliques. Deuxièmement, le risque systématique peut provenir de plusieurs sources ; le modèle actuel à un facteur empêche la répercussion propre des chocs « macro ».Nous proposons un stress test spécifique de crédit qui permet d’appréhender le risque spécifique de crédit d’un portefeuille concentré, et un stress test spécifique de liquidité qui permet de mesurer l’impact des chocs spécifiques de liquidité sur la solvabilité de la banque. Nous proposons aussi une généralisation multifactorielle de la fonction d’évaluation du capital réglementaire en IRB, qui permet d’appliquer les chocs des macro-stress tests sur chaque portefeuille sectoriel, en stressant de façon claire, précise et transparente les facteurs de risque systématique l’impactant. Cette méthodologie permet une répercussion propre de ces chocs sur la probabilité de défaut conditionnelle des contreparties de ces portefeuilles et donc une meilleure évaluation de la charge en capital de la banque
Our thesis consists in explaining, by bringing some theoretical elements, the imperfections of EBA / BCE macro-prudential stress tests, and proposing a new methodology of their application as well as two specific stress tests in addition. We show that macro-prudential stress tests may be irrelevant when the two basic assumptions of the Gordy-Vasicek core model used to assess banks regulatory capital in internal methods (IRB) in the context of credit risk (asymptotically granular credit portfolio and presence of a single source of systematic risk which is the macroeconomic conjuncture), are not respected. Firstly, they exist concentrated portfolios for which macro-stress tests are not sufficient to measure potential losses or even ineffective in the case where these portfolios involve non-cyclical counterparties. Secondly, systematic risk can come from several sources; the actual one-factor model doesn’t allow a proper repercussion of the “macro” shocks. We propose a specific credit stress test which makes possible to apprehend the specific credit risk of a concentrated portfolio, as well as a specific liquidity stress test which makes possible to measure the impact of liquidity shocks on the bank’s solvency. We also propose a multifactorial generalization of the regulatory capital valuation model in IRB, which allows applying macro-stress tests shocks on each sectorial portfolio, stressing in a clear, precise and transparent way the systematic risk factors impacting it. This methodology allows a proper impact of these shocks on the conditional probability of default of the counterparties of these portfolios and therefore a better evaluation of the capital charge of the bank

碩士
國立高雄第一科技大學
風險管理與保險研究所
100
This study aims to explore the empirical experiences on the related influential factors of credit default risk on finance and tax compliance provided by national banks to the middle and small-sized enterprises. The data were collected from one of the domestic commercial banks and the samplings were accumulated from March 2006 to March 2007. There are files from five seasons for credit reference and establishing criteria for credit. From a total of 4760 cases of conforming loans, 3707 were normal interest receivable cases which is about 78%, and 1053 were non-normal interest receivable cases, accounting for about 22%. Based on the financial variables, the predicting model of financial crises was constructed and logistic regression analysis was applied in order to analyze the finance and tax compliance provided to the middle and small-sized enterprises, as well as the predictable capability regarding the incidence of financial crises after adding twelve models formed by financial variables. Consequently, the statistics show low default ratios of finance and tax compliance. In general, the goodness of fit in the entire model is not very significant. The test of significance with regard to the financial variables is not all significant. As a result, the financial report has a window dressing effect and the evaluation of standard criteria for credit cannot be made either by finance compliance or tax compliance alone.

This chapter discusses trading book models. Risk models come in a variety of types. However, for market risk purposes there have been a number of types which may be used within the framework. The simplest is the ‘CAD 1’ model — named after the first Capital Adequacy Directive, which permitted such models to be used in the calculation of regulatory capital. VaR models, permitted by Basel 2, were more complex, and this complexity was increased by Basel 2.5, which required the use of ‘stressed VAR’. In due course all of this will be replaced by the Basel 3 FRTB calculation, which rejects VAR and is based on the calculation of an expected shortfall (ES) market risk charge, a VaR based default risk charge (DRC) (for those exposures where the bank is exposed to the default of a third party), and a stressed ES-based capital add-on.