37004 Interest Rates and Credit Risk Models
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Credit points: 8 cp
Result type: Grade and marks
Requisite(s): 24 credit points of completed study in spk(s): STM91543 Core Subjects (Mathematics)
These requisites may not apply to students in certain courses. See access conditions.
Anti-requisite(s): 25872 Interest Rates and Credit Risk Models
Description
This subject focuses on topics of interest rate theory and credit risk modelling and emphasises their analogies. The aim of this subject is for students to obtain a thorough working knowledge of models for interest rate and credit risk, their practical implementation and calibration to market data, their underlying assumptions and limitations, as well as applying the mathematical techniques underpinning the models.The material covers the following major topics: products of fixed-income markets, short-rate models, forward-rate and LIBOR market models, financial instruments in credit risk management. In the framework of Models of default, the advantages and shortcomings of synthetic credit-linked instruments are discussed. Furthermore, the dependence structure of default events and default contagion and its role in the the global financial crisis, is treated.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Apply and calibrate to market data standard stochastic models of interest rate and credit risk |
|---|---|
| 2. | Derive closed-form and near closed-form solutions to derivative pricing problems involving interest rate and credit risk |
| 3. | Understand the ethical issues and potential conflicts of interest that can arise in the design, valuation and issuance of structured financial products |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Appraise advanced knowledge and critically evaluate the information's source and relevance, with a focus on applications of mathematical methodologies to quantitative finance problem solving. (1.1)
- Investigate complex and challenging real-world problems in the areas of quantitative finance by critically evaluating information and solutions and conducting appropriate approaches to independent research. (2.1)
- Practice professionally adhering to confidentiality requirements, ethical conduct, data management, and organisation and collaborative skills in the context of applying mathematical and statistical modelling to quantitative finance problems. (3.1)
- Reflect and evaluate the value, integrity, and relevance of multiple sources of information to derive responsive, innovative solutions, show creativity, innovation and application of technologies in complex quantitative finance problems. (4.1)
Contribution to the development of graduate attributes
The Faculty of Science has determined that our courses will aim to develop the following attributes in students at the completion of their course of study. Each subject will contribute to the development of these attributes in ways appropriate to the subject, thus not all attributes are expected to be addressed in all subjects.
This subject contributes to the development of the following graduate atributes:
1. Disciplinary Knowledge - acquire detailed specialised quantitative finance knowledge and the professional competency required to work as a quantitative finance analyst in the modern finance industry.
2. Research, inquiry and critical thinking - develop the ability to apply and demonstrate critical and analytical skills to developing solution to complex real world problems.
3. Professional, Ethical and Social Responsibility - develop an enhanced capacity to work ethically and professionally using collaborative skills in the workplace.
4. Reflection, Innovation and Creativity – develop the ability to source and analyse multiple sources of data to develop innovative solutions to real world problems in quantitative finance
Teaching and learning strategies
This subject will use the ‘blended learning’ model where students will have access to online learning resources and will undertake learning tasks prior to coming to in-person tutorials. Essential principles are presented and analysed in the online lecture component and this is complemented by students going through practical application exercises under the guidance of the lecturer in the tutorials. In this way, the subject will enable students to experience an effective integration of online and face-to-face on-campus learning.
Lecture Program
The outlined schedule indicates topics to be discussed in lectures. The aim of the lectures is to provide a solid theoretical grounding in contemporary mathematical modelling of interest rates and credit risk, the use of these models for risk management and for the pricing of interest rate and credit derivatives. Students will then practice and apply this knowledge to solve realistic problems, both mathematically (in the tutorials) and computationally (in the computer exercises).
Computer ExercisesYou will need access to a computer (running MS Windows, Linux or macOS), since programming in Python is an important part of this subject. In keeping with authentic assessment, some of these exercises will be conducted on real-world financial data. In order to provide early feedback, these exercises will be assigned in several parts over the teaching period and feedback will be provided on each completed part before the next part is due.
Tutorials
Online recorded lectures in this subject will be complemented by in-person tutorials. In these tutorials, you will work in small groups to collaboratively solve mathematical problems in derivative security pricing on a whiteboard ("whiteboard tutorials"). The lecturer will provide formative feedback to each group as they develop their solutions during the tutorials. Before each tutorial, you should make sure that you have watched all recorded lectures of the previous weeks.
Content (topics)
- Basic concepts of stochastic interest rate modelling
- Multifactor Gauss/Markov HJM: interest rate, exchange rate and equity risk
- The lognormal Market Model and its extensions
- The new riskfree rate (RFR) benchmarks
- Basic concepts of stochastic credit risk modelling
- Black/Scholes/Merton credit risk modelling and extensions
- Spread-based modelling of credit risk
- Counterparty credit risk and other valuation adjustments
- Modelling default dependence
- Case study: Structured credit products in the Financial Crisis
Assessment
Assessment task 1: Assignment
| Intent: | This assessment task contributes to the development of the following graduate attributes: |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2 and 3 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1 and 4.1 |
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 50% |
| Criteria: | This assessment task contributes to the development of course intended learning outcome(s): 1.1, 1.3, 2.2, and 4.1 |
Assessment task 2: Final examination
| Intent: | This assessment task contributes to the development of the following graduate attributes: |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2 and 3 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1, 3.1 and 4.1 |
| Type: | Examination |
| Groupwork: | Individual |
| Weight: | 50% |
| Criteria: | This assessment task contributes to the development of course intended learning outcome(s): |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.