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About this book

This book introduces to basic and advanced methods for credit risk management. It covers classical debt instruments and modern financial markets products. The author describes not only standard rating and scoring methods like Classification Trees or Logistic Regression, but also less known models that are subject of ongoing research, like e.g. Support Vector Machines, Neural Networks, or Fuzzy Inference Systems. The book also illustrates financial and commodity markets and analyzes the principles of advanced credit risk modeling techniques and credit derivatives pricing methods. Particular attention is given to the challenges of counterparty risk management, Credit Valuation Adjustment (CVA) and the related regulatory Basel III requirements. As a conclusion, the book provides the reader with all the essential aspects of classical and modern credit risk management and modeling.

Table of Contents

Frontmatter

1. Introduction

Abstract
Many monographs on credit risk start, and deal, essentially only with different computational methods for measuring and modeling credit risk. As the title indicates, we want to focus not only on various modeling techniques for credit risk pricing and measurement, but also on key issues of credit risk management; i.e., we also want to look at the proper setting of the credit risk organization, credit risk processes, powers, and controls. It may be observed from many failures and mishaps which have occurred in various banks around the world, including the recent systemic crisis, that the key problem often lies, rather, in conflicts of interest, insufficient controls, or the low levels of power vested in those who knew that there was a problem (and who were able to gauge the risks correctly) but were not able to prevent, or limit, the risky transactions. This experience can also be confirmed by the author, who was responsible for overseeing the risks inherent in trading activities, as well as the later credit risks in the classical banking activities of a large Czech bank in the late nineties, and in the first half of the previous decade. On the other hand, in many cases, in particular during the recent crisis, insufficient controls and regulation have been partially connected to the low level of understanding, and consequent underestimation, of the risks involved. Hence, to summarize, one should neither overestimate nor underestimate the importance of credit measurement techniques with respect to the classical credit risk management issues. We shall start discussing the credit risk organization and management issues in Chap. 2.
Jiří Witzany

2. Credit Risk Management

Abstract
If a person or an organizational unit within a bank or corporation performs credit assessment, then there are two essential questions: Do they have the necessary skills and techniques to assess the credit risk properly, and, secondly, will the assessment really be independent and unbiased? For example, if the assessment is entrusted to a salesperson remunerated according to the number and volume of loans granted, then there is a clear danger of underestimating the risks; i.e., being too optimistic when looking at the applicants’ financial situation in order to maximize the business target. The situation is even worse if there is a relationship, potentially even corrupt, between the salesperson and the applicant. Such a situation is, unfortunately, not impossible. Hence a bank may have excellent credit modeling software, and many qualified mathematicians, but if those simple issues (one could say operational risks) are omitted, then there is a big problem. Therefore, we need to discuss, firstly, the appropriate or recommended models of credit risk organization, as well as the separation of powers, both in the case of classical banking (or corporate business) activities, as well as in the case of trading and investment activities. Those recommendations, in fact, go hand in hand with the Basel risk management process standards.
Jiří Witzany

3. Rating and Scoring Systems

Abstract
The main goal of the credit assessment process is to approve acceptable loan applications, reject clients that will probably default in the future, and, moreover, set up loan pricing so that the credit losses are covered by collected credit margins. This does not have to be necessarily achieved through a rating system. Nevertheless, the approach of assigning a credit grade on a finite scale to each client, and/or exposure, has become a general keystone of the modern credit risk management process. The rating can be obtained in many different ways. There are external agencies like Standard & Poor’s and Moody’s which have been assigning ratings to bond issuers, large corporations, and countries for more than 100 years. Ratings can be also produced by banks internally in many different ways—by experienced credit analysts, by statistical, or even artificial intelligence methods, or by a combination of human and machine assessment. Before we start describing in detail any particular methods, we should, first of all, define our expectation of a rating system, and the methods used to measure how well the expectations are met.
Jiří Witzany

4. Portfolio Credit Risk

Abstract
So far we have focused on methods how to properly measure credit risk and approve individual loan transactions. But even if this process is under control and loan underwriting is going well, a prudent bank management must ask the question; “When is enough enough?” Can the bank portfolio grow without limitations, or is there a limit? Moreover, is it optimal to specialize in one client segment, or economic sector, or is it better to split the underwriting activities among more segments and sectors? More specifically, can we optimize the risk/return relationship in the sense of the Markowitz Portfolio Theory (Fig. 4.1)?
Jiří Witzany

5. Credit Derivatives and Counterparty Credit Risk

Abstract
Financial derivatives are generally contracts whose financial payoffs depend on the prices of certain underlying assets. The contracts are traded Over the Counter (OTC), or in a standardized form on organized exchanges. The most popular derivative types are forwards, futures, options, and swaps. The underlying assets are, typically, interest rate instruments, stocks, foreign currencies, or commodities. The reasons for entering into a derivative contract might be hedging, speculation, or arbitrage. Compared to on-balance sheet instruments, derivatives allow investors and other market participants to hedge their existing positions, or to enter into new exposures with no, or very low, initial investment. This is an advantage in the case of hedging, but at the same time, in the case of a speculation, a danger, since large risks could be taken too easily. Derivatives are sometimes compared to electricity; something that is very useful if properly used, but extremely dangerous if used irresponsibly. In spite of those warnings, the derivatives market has grown tremendously in recent decades, with OTC outstanding notional amounts exceeding 650 trillion USD, as of the end of 2014, and exchange traded derivatives’ annual turnover exceeding 1450 trillion USD in 2014.
Jiří Witzany

6. Conclusion

Abstract
The art and science of credit risk pricing, measurements, and management have been on a long journey and made a significant progress during recent decades. The classic credit risk question as to whether a loan application should be approved or not, and, possibly, under which conditions, used to be approached by experts based on their experience and analytical skills. Advances in mathematics, statistics, and computer power have brought new sophisticated, automated methods that either support or completely replace skilled credit analysts. The growth of banking portfolios, in particular in the area of consumer and mortgage financing, has highlighted the issue of portfolio credit risk that is not driven by losses on any single exposure, but rather by losses due to higher than expected default rates. As we have seen in Chap. 4, portfolio modeling involves not only the estimation of individual default probabilities, but also the concept of default correlation, which remains challenging even today. The development of credit risk management standards in financial institutions has gone hand in hand with changes in the Basel regulation, which aims to set basic risk management standards and define regulatory capital based on the risk undertaken. The concepts of rating, PD, LGD, EAD, or expected and unexpected loss, were used by many advanced credit risk managers before Basel II, but since the introduction of the new regulation, these concepts have really become standard and widespread. Since the nineties we have seen rapid growth of the derivative markets. Counterparty credit risk used to be handled in a relatively simple way through limits on counterparty exposures based on various simple equivalents. Recently, in particular after the financial crisis, the issue of counterparty credit has become much more complicated with the advance of the many different valuation adjustments (XVAs) discussed in Sect. 5.6. Last but not least, we should mention the credit derivatives and credit derivative-linked securities, such as CDOs, which started to be traded actively at the beginning of the previous decade. Their pricing and risk management, seriously challenged by the financial crisis, still pose a real challenge to financial engineers, as shown in Chap. 5. We hope that this text has provided not only an overview of credit risk pricing, measurement, and management methods, but also that it contributes to the ongoing research in this area.
Jiří Witzany

Erratum to: Credit Risk Management: Pricing, Measurement, and Modeling

Without Abstract
Jiří Witzany

Backmatter

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