The article reviews the Bank's credit risk modeling issues. The substance of the article analyzes the credit risk structure and methods for measuring its components. Credit risk is measured as a loss, that is the function of several variables. The amount of open credit risk position in case of default, expected proba-bility of credit default and recovery ratio after the default are the main variables of the given function presented in the arti-cle. These variables are reviewed as random values and meth-ods are given for its evaluation and integration as one indica-tor.The article also reviews the tasks of forming the bank's internal credit ratings and issues related to the use of these ratings in credit risk evaluation model.
In: In 'THE RISK MODELING RISK EVALUATION HANDBOOK: RETHINKING FINANCIAL RISK MANAGEMENT METHODOLOGIES IN THE GLOBAL CAPITAL MARKETS', G. Gregoriou, C. Hoppe, and C. Wehn, eds, McGraw-Hill, 2010
"Examines the multidisciplinary applications, problems, and case histories in risk modeling, assessment, and management This book examines risk analysis, focusing on quantifying risk and constructing probability in conjunction with real-world decision-making problems, including institutional, organizational, and political considerations. The author presents basic concepts (hierarchical holographic modeling; decision analysis; multi-objective trade-off analysis) as well as advanced material (extreme events and the partitioned multi-objective risk method; multi-objective decision-tree analysis; multi-objective risk impact analysis method); avoids higher mathematics whenever possible; and reinforces the material with examples and case studies.The fourth edition of Risk Modeling, Assessment, and Management features: Expanded chapters covering systems-based guiding principles for risk modeling, planning, assessment, management, and communication; modeling complex systems of systems with phantom system models; and hierarchical holographic modeling An expanded appendix including the Bayesian analysis for the prediction of chemical carcinogenicity, and the Farmer's Dilemma formulated and solved using a deterministic linear model Updated case studies including a new case study on sequential Pareto-optimal decisions made during emergent complex systems of systems A new companion website with over 200 solved exercises and problems that feature risk analysis theories, methodologies, and applications Risk Modeling, Assessment, and Management, Fourth Edition, is written for both undergraduate and graduate students in systems engineering and systems management courses. The text also serves as a resource for academic, industry, and government professionals in the fields of homeland and cyber security, healthcare, physical infrastructure systems, engineering, business, and more"--
"Preface Second Edition The first edition of this book appeared eight years ago. Since then the banking industry experienced a lot of change and challenges. The most recent financial crisis which started around May 2007 and lasted in its core period until early 2009 gave rise for a lot of scepticism whether credit risk models are appropriate to capture the true nature of risks inherent in credit portfolios in general and structured credit products in particular. In a recent article two of us discuss common credit risk modeling approaches in the light of the most recent crisis and invite readers to participate in the discussion; see [25]. A key observation in a discussion like the one in [25] is that the universe of available models and tools is sufficiently rich for doing a good job even in a severe crisis scenario as banks recently experienced it. What seems to be more critical is an appropriate model choice, parameterization of models, dealing with uncertainties, e.g., based on insufficient data, and communication of model outcomes to decision makers and executive senior management. These are the four main areas of challenge where we think that a lot of work and rethinking needs to be done in a p︠ost-crisis ̕reflection of credit risk models. In the first edition of this book we focussed on the description of common mathematical approaches to model credit portfolios. We did not change this philosophy for the second edition. Therefore, we left large parts of the book unchanged in its core message but supplemented the exposition with new model developments and with details we omitted in the first edition"--
As is well known, most models of credit risk have failed to measure the credit risks in the context of the global financial crisis. In this context, financial industry representatives, regulators and academics worldwide have given new impetus to efforts to improve credit risk modeling for countries, corporations, financial institutions, and financial instruments. The paper summarizes some of the recent advances in this regard. It considers modifications of structural models, including of the classical Merton model, and efforts to reconcile the structural and the reduced-form models. It also di
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AbstractBy building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.
