Introduction to Credit Risk

Giulio Carlone

• Publisher: Chapman and Hall/CRC
• Year: 2020
• Print ISBN: 9780367478490
• eBook ISBN: 9781000171471
• Edition: 1
• Topic: Business
• Subtopic: Entrepreneurship
• Index: Business

Introduction to Credit Risk focuses on analysis of credit risk, derivatives, equity investments, portfolio management, quantitative methods, and risk management. In terms of application, this book can be used as an important tool to explain how to generate data rows of expected exposure to counterparty credit risk. The book also directs the reader on how to visualize, in real time, the results of this data, generated with a Java tool.

Features

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• Uses an in-depth case study to illustrate multiple factors in counterparty credit risk exposures

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• Suitable for quantitative risk managers at banks, as well as students of finance, financial mathematics, and software engineering

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• Provides the reader with numerous examples and applications

Giulio Carlone has an MBA, a PhD, and a Master's degree in Computer Science from the University of Italy. He is a member of the software system engineering staff of the Department of Computer Science at University College London. He has 20 years of practical experience in technical software engineering and quantitative finance engineering in the commercial sector. His research interests include the use of communication strategies and the implementation of plans and projects using financial software for requirement specifications, requirements analysis, and architectural design.

CHAPTER 1

Background of Credit Risk and Java Visualization for Expected Exposure

1.1 Financial Risk

In finance, the term “risk” is associated with the uncertainty derived from a possible future loss of value of an activity, financial instrument, or more generally, of an investment. A financial activity is, therefore, considered risky if the monetary return deriving from it cannot be anticipated. Buying shares is a classic example of a risky activity because it is impossible to know in advance whether their value will increase or decrease over time, or whether the company selling them will pay dividends regularly. Buying market shares is considered a classic example of a risky activity; however, as we will see later on, there are many others too.

Risk manifests in a symmetrical form. There is a possibility of obtaining a worse result than the most probable one, as well as a better one. In statistical terms, we can think of risk as a distribution of probability. The left-hand side of the average expected result represents the downside risk (in other words, the possibility of earning less, or losing more, than expected), whereas the right-hand side is the upside opportunity (or the possibility of earning more, or losing less, than expected).

A careful understanding, assessment, and management of risk is essential for financial institutions because for them, taking risks is a vital element of increasing their earnings. These institutions take risks, implicitly, by offering services to their clients, taking the role of risk takers in return for profit, and, explicitly, by exposing themselves on the financial market.

There are various forms of financial risk, but, for the sake of simplicity, we will distinguish between five categories: credit risk, liquidity risk, counterparty risk, market risk, and operational risk. Our attention will be focused on counterparty risk. However, to understand this form of risk fully, we need to outline the nature of some of the others as well.

Operational risk is defined as the risk of making losses owing to the inadequacy or malfunction of procedures, human resources, and internal systems, or to external events. In reality, the nature of this risk is not characteristic of financial intermediation. It only became so when the Basel Committee on Banking Supervisors, of which we will talk later, declared that a malfunction in one of the sectors linked to a bank’s operations might put its stability at risk. Operational risk is, therefore, defined more by its causes than its effects, particularly with regard to four risk factors: human resources, information systems, external procedures, and external events.

Market risk refers to the possibility of banking activities receiving negative variations in their sale value. In the case of banking activities, this risk is mainly dependent on financial activities whose value can drop because of market factors such as interest rates, exchange rates, and share prices; technically, market risk relates to interest risk, exchange risk, and equity risk. As we will discuss later, interest rate-sensitive variations of value have by far the largest impact on a bank’s portfolio.

Credit risk may be defined as the eventuality of a financial contractor not honouring its obligations, thus causing a loss to the lending counterparty. Even without the extreme case of a borrower becoming insolvent, credit risk can be defined as the eventuality that an unexpected variation in a borrower’s creditworthiness may cause an unexpected variation in the value of the credit. It can be classified as follows:

• Credit default risk is the risk of an identifiable loss by a bank that believes it probable that a creditor will not fully honour their credit obligations or that they may encounter significant delays in the payment of a substantial obligation.
• Concentration risk is the risk associated with a single or a group of exposures that may cause significant losses that can undermine a bank’s own sustainability. This concentration may be towards a single obligation or an entire sector.
• Country risk is the risk of a loss arising from a state freezing its payments in foreign currency (transfer/conversion risk) or defaulting its payments (sovereign default risk).

1.2 Credit Risk

Credit risk is one of the most important factors in determining the price and yield of financial activities. Although the effects of credit risk on the value of bonds have long been examined, the development of analytical models for the study and quantification of these effects is relatively recent. Our aim here is to introduce fundamental concepts, such as a definition of credit risk and its components. Later, we will analyse various existing models for measuring credit risk.

Credit risk is one of the most analysed and difficult to quantify forms of market risk. The traditional approach to its study has been to apply actuarial methods of risk assessment based on historical data. However, the rapid growth of derivative activity on the financial market, particularly of over-the-counter and credit derivatives, and the high level of sophistication of certain financial instruments have brought to light the inadequacy of traditional methods in giving a correct estimation of real-world risks.

Ultimately, credit risk may be defined as the eventuality that one of the contractors is unable to honour its financial obligations, thus causing a loss to the lending party. This definition, however, only contemplates the extreme case in which the debtor becomes insolvent.

A loss of value of the credit position may also derive from a deterioration of the debtor’s financial situation without them necessarily becoming insolvent. A fuller definition of the term “credit risk”, therefore, should be as follows: the effect that an unexpected variation in a debtor’s creditworthiness can cause on the credit value. The ratings provided by agencies, such as Standard & Poor’s and Moody’s, are an estimation of the creditworthiness of companies and countries.

There are, therefore, two definitions of the term credit risk to differentiate between credit loss arising after a debtor’s insolvency (the default-mode paradigm) and variations in the value of exposure arising from deteriorations in the debtor’s creditworthiness, with insolvency being only an exceptional case (Mark-to-Market or Mark-to-Model paradigm).

1.3 Credit Risk Measure

Value at risk is, without doubt, the most widely used measure for the management of financial risk. This form of risk measure, however, is not necessarily suitable for measuring counterparty risk for various reasons.

Credit exposure has to be defined over more than one temporal horizon to shed light on the effect of passing time and the general underlying trends.

Counterparty risk should be measured both from the view of pricing as well as that of risk management; more than one measure is, therefore, required.

For a full evaluation of counterparty risk on a portfolio, thus taking into consideration every counterparty present, the effective exposure towards every single counterparty must be understood.

Other risk measures that exist apart from value at risk are as follows.

• Concerning expected exposure with regard to the management of risk, institutions are only interested in positions with positive Mark to Market because, as pointed out previously, counterparty risk is asymmetrical, and only these positions can generate exposure. The expected exposure on time frame t is only concerned with positive Mark to Market. It is defined as the arithmetical average of exposure distribution at a set future date.
• Concerning potential future exposure (PFE), we can state that at a set date t and taking the worst possible scenario into consideration, it indicates the value of exposure at a set future date towards a specific level of confidence. As future Mark-to-Market values cannot be known for certain, at a set future date, the PFE can only be described as some form of probability distribution.
• The expected positive exposure at a set date [0; T] is a methodological estimation of future exposure of transactions subject to counterparty risk based on a weighted average contemplated over a time frame defined by the expected values of exposures. This weighted average is defined by the relation between the time frame of each single expected exposure and the entire period of time being considered.

1.4 Monte Carlo

Monte Carlo is a class of computational algorithms that, starting from a series of randomly or pseudo-randomly generated numbers, permits us to obtain an average result that is similar to a theoretical behaviour. It allows us, therefore, to resolve complicated deterministic problems with a probabilistic method.

It is particularly useful in science when the direct observation of a phenomenon is not sufficient to provide a theoretical model. In these cases, it is advisable to conduct the same experiment repeatedly while changing the values attributed to the initial conditions and to the ensuing, if applicable, interaction between the elements under consideration through a pseudo-casual generation of numbers. The next step entails the unification of the results through some predetermined form of media. Generally, this method consists of four successive phases: first, some kind of domain, even a multidimensional one, has to be defined for the input data; second, input data have to be generated randomly through some variable defined by the domain; third, a deterministic computation of the various output for each input vector has to be made; and finally, aggregation of the results.

1.5 Interest Rate Swap

An interest rate swap is a form of contract in which two parties agree to exchange interest rate cash flows, based on a specified notional amount, for a predetermined period of time. In most cases, the payments for these contracts are annual or biannual. However, there are also cases in which payments are made quarterly or monthly. The capital on which the interests are calculated is purely notional and not exchanged between the contractors.

According to the simplest contractual formula, called plain vanilla swap, one party makes payments at a variable rate, calculated according to a reference rate (e.g. the Euribor rate for the interbank circuit), and receives payments at a fixed rate. The counterparty, meanwhile, will make payments at a fixed rate and receive them at a variable rate.

The speculative use of interest rate swaps is intuitive and operationally significant. If a rise in interest rates is expected, it would make sense to enter a swap contract, making payments at a fixed rate and receiving them at a variable one. Similarly, if the expectations are for a drop in interest ...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Author
• Introduction: Starting with Credit Risk
• Chapter 1 ▪ Background of Credit Risk and Java Visualization for Expected Exposure
• Chapter 2 ▪ Theoretical Phase of a Real-World Case Study
• Chapter 3 ▪ Real-World Case of the Practical Phase for Generating Exposure Regulatory Measures in a Specific Bank with an Internal Model Method
• Chapter 4 ▪ Theoretical Approach of the Real-World Case Phase Related to the Methodology of Scenario Simulation Used for Generating Exposure Regulatory Measures
• Chapter 5 ▪ Generation of a Simulation of a Real-World Case for Generating Exposure Regulatory Measures
• Chapter 6 ▪ Compute Exposure by Counterparty
• Chapter 7 ▪ First Quantitative Analysis of Portfolio Exposure Profiles
• Chapter 8 ▪ Further Analysis on Portfolio Exposure Profiles Using Zero Rate Vector 0.03
• Chapter 9 ▪ Further Analysis of Portfolio Exposure Profiles with Zero Rate Vector 0.06
• Chapter 10 ▪ Generalization of Analysis on Portfolio Exposure Profiles with Zero Rate Vectors 0.01, 0.03, and 0.06
• Chapter 11 ▪ Risk Perspective of Credit Valuation Adjustment
• Chapter 12 ▪ Further Work
• Chapter 13 ▪ MATLAB Source Code Strategy and Analysis for Generation of Time Step Set of Data
• Chapter 14 ▪ Expected Exposure Visualization List of Java Code Packages
• Chapter 15 ▪ Expected Exposure Visualization List of UML Diagram
• Chapter 16 ▪ Credit Models Using Google Cloud
• Chapter 17 ▪ Conclusion
• Index