10credit - ACTSC 445 Asset-Liability Management Department...

Info icon This preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
ACTSC 445: Asset-Liability Management Department of Statistics and Actuarial Science, University of Waterloo Unit 10 – Credit Risk References (recommended readings): Chap. 8 and 9 of Quantitative Risk Management Introduction Credit Risk is the risk that the value of a portfolio will change due to unexpected changes in the credit quality of issuers or trading partners Includes both losses due to defaults and losses caused by changes in credit quality of issuers Credit risk models are used for two main tasks: 1. Credit Risk Management: goal is to determine the loss distribution of a loan or bond portfolio over a fixed-time period, compute risk measures and make risk-capital allocations 2. Analysis (mostly pricing) of credit-risky securities, such as credit-default swaps Categories of models static vs dynamic: static models are typically for credit risk management, while dynamic models or for pricing credit-risky securities structural (or firm-value or threshold) vs reduced-form: structural models were initiated by Mer- ton in 1974; default occurs when a random variable (or process) falls below a threshold repre- senting the liabilities; in reduced-form models the precise mechanism leading to default is left unspecified; the default time of a firm is modelled as a non-negative random variable whose distribution depends on a set of economic variables. Challenges of credit risk management 1. Lack of public information and data : makes it very hard to calibrate models, and also results in asymmetric information 2. Skewed loss ditributions (frequent small profits and occasional large losses); makes it di ffi cult to model tail accurately; appropriate distributions tend to be harder to work with than, e.g., normal distribution 3. Dependence modelling : defaults events tend to happen simultaneously and this has a significant impact on the tail of the credit loss distribution. Needs to be modelled correctly. 1
Image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Plan for this unit We’ll first talk about structural models of default, starting with the well-known Merton’s model and some extensions, including models based on credit migration. Then we’ll discuss threshold models, which can be viewed as a generalization of Merton’s model. A useful class of models that are used to specify threshold models are copula-based models, so we’ll then introduce copulas and show how they can be used within threshold models. We’ll quickly discuss Li’s model and present some numerical results indicating the drawback of this model, and conclude with a short discussion of how some of the Basel II regulations are related to these models. Structural Models of Default 1. Merton’s model Consider a firm whose asset value V t at time t is a random variable { V t , t 0 } is a stochastic process The asset value V t comes from two components: the firm’s equity and firm’s debt, whose values at time t are S t and B t , respectively. Hence we have V t = S t + B t , 0 t T . For the
Image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern