37009 Risk Management
Warning: The information on this page is indicative. The subject outline for a
particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2025 is available in the Archives.
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): 25878 Risk Management
Description
Designed specifically for quantitative finance students, this subject provides disciplinary knowledge in the area of risk management by providing a comprehensive and rigorous exposition of the theory and practice of financial risk measurement and management. It focuses on the tools and techniques for identifying, measuring and managing risk. It covers the value-at-risk (VaR) measure and expected shortfall (ES) risk measures, which form the basis of risk measurement and management in current financial regulation such as Basel II, Basel III, and Solvency II. Parametric and non-parametric techniques, including historical and Monte Carlo simulation, to calculate or approximate risk measures are covered in depth.
This subject discusses the axiomatic theory of risk measures and the relevance of these axioms/properties to financial risk management. Furthermore, in view of emerging evidence of the intricate interplay of risk factors relevant to financial risk management, this subject also tackles more general notions of dependence beyond (linear) correlation. To more accurately model the dependence structure between risk factors, copulas are discussed in depth and applied to the calculation of risk measures.
The risk modelling and management concepts are then applied extensively to the analysis of market risk and operational risk among others.
The combination of theory, practical examples, and complex problems presented throughout the subject promote enquiry and critical thinking. Innovation and creativity are required to successfully solve the problems in the assessment tasks. Communication skills are also honed to enable students to clearly and concisely explain the methods used and the results obtained in the process.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Explain the regulatory environment for financial risk management as specified, for example, by the Basel Accords (Basel II and Basel III) and Solvency II. |
|---|---|
| 2. | Identify and model key risk events and risk factors facing financial institutions, especially with respect to complex portfolios of financial instruments, and the financial industry at large. |
| 3. | State and interpret the properties of risk measures and compare and contrast different risk measures, especially value-at-risk (VaR) and expected shortfall (ES), in terms of these properties |
| 4. | Quantify and analyze the market risk exposure of complex portfolio using various risk measures and calculation methods. |
| 5. | Characterize and analyze the dependence structure between and among risk factors and effectively utilize copulas to model these dependence structures |
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)
- 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)
- Develop and present complex ideas and justifications using appropriate communication approaches from a variety of methods (oral, written, visual) to communicate with mathematicians, data analysts, scientists, industry, and the general public. (5.1)
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 will provide a thorough understanding of the range of risks confronting financial institutions and the financial industry at large. Students will develop theoretical and practical knowledge on quantifying risk using various risk measures and methods for calculating these risk measures. Students will apply state-of-the-art techniques in risk modelling and management, including dependence modelling and simulation, to the analysis of market risk exposures of complex portfolios, among other practical scenarios.
This subject contributes to the development of the following graduate attributes:
GA 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. In this subject, students will acquire extensive knowledge in the theory, methods, and applications of quantitative risk management.
GA 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. This attribute shall be developed through the assessment tasks in the subject.
GA 3. Professional, Ethical and Social Responsibility - develop an enhanced capacity to work ethically and professionally using collaborative skills in the workplace. This attribute shall be developed by building the students' understanding of the importance of professional and ethical conduct in risk management.
GA 4. Reflection, Innovation, Creativity - develop the ability source and analyse multiple sources of data to develop innovative solutions to real world problems in quantitative finance. Students will be required to acquire data from various sources and synthesize relevant information to assess and manage different types of financial risks.
GA 5. Communication - develop effective professional communication skills for a range of technical and non-technical audiences. Students' communication skills will be honed through regular collaborative activities with their peers during problem solving sessions in the workshops.
Teaching and learning strategies
The subject is presented in the form of a lecture and practical workshop format. The theoretical concepts are presented in lectures and students work on theoretical and practical exercises in the workshops. Students have the opportunity to work collaboratively and will receive feedback on their solutions. The teaching and learning strategies in this subject enable students to experience a seamless integration of online and face-to-face learning.
The subject will be consistent with the UTS Learning Futures model where students will have access to online learning resources and will undertake preliminary learning tasks prior to coming to classes where they engage in further learning and practical workshops. The online learning resources focus on the theory and concepts, the learning of which is then reinforced via in-class exercises during the workshops. Students are able to access the online learning resources at their convenience.
The problems sets and exercises discussed during the workshops shall encourage critical thinking and innovation and will prepare students for the successful completion of the subject assessment tasks. Students will receive verbal feedback on their work during workshops. Students will receive summative feedback on their assignment solutions.
Content (topics)
- Regulatory frameworks for financial risk modelling and management
- Axioms for risk measures and key examples of risk measures: value-at-risk (VaR), expected shortfall, utility-based risk measures, spectral risk measures
- Methods for calculating risk measures: analytical, historical simulation, Monte Carlo simulation
- Dependence structures and copulas
- Market risk modelling and management, including risk-factor mapping and the analysis of portfolio risks
- Operational risk modelling and management
- Backtesting of risk measures
Assessment
Assessment task 1: Problem Set
| Intent: | This assignment task will contribute to the development of the following graduate attributes:
|
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 2, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): .1, .1 and 1.1 |
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 30% |
| Criteria: | This assignment will be assessed accuracy of answers, workings and clear explanations of solutions. |
Assessment task 2: Project
| Intent: | This assignment task will contribute to the development of the following graduate attributes:
|
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): .1, .1, 1.1, 3.1 and 5.1 |
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 30% |
| Criteria: | The assignment will be assessed accuracy of answers, workings and clear explanations of solutions. The clarity and integrity of the written report will also be assessed. Detailed criteria of the assignment will be provided in class. |
Assessment task 3: Exam
| Intent: | This final exam will contribute to the development of the following graduate attributes:
|
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): .1, .1, 1.1 and 5.1 |
| Type: | Examination |
| Groupwork: | Individual |
| Weight: | 40% |
| Criteria: | The final exam will assess disciplinary knowledge in terms of the integrity and correctness of the answers and the accuracy of explanations. Innovation, creativity and critical reflection in the answers to questions in the final exam are assessed and recognised. Critical thinking will be integral to successfully solving questions in the final exam. |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks to obtain credit for the subject.
Required texts
There are no required texts for this subject. Lecture notes and other materials shall be made available on the subject Canvas site.
Recommended texts
The following references may be useful to supplement the lectures and activities in the subject:
- Danielsson, J. (2011). Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk with Implementation in R and Matlab. John Wiley & Sons, Ltd.
- Deutsch, H.-P. & Beinker, M. W. (2019). Derivatives and Internal Models: Modern Risk Management. (5th edition). Palgrave Macmillan.
- Hull, J. C. (2022). Options, Futures, and Other Derivatives. (11th edition). Pearson Education, Inc.
- Hult, H., Lindskog, F., Hammarlid, O. & Rehn, C. J. (2012). Risk and Portfolio Analysis: Principles and Methods. Springer.
- Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk. (3rd edition). McGraw-Hill.
- McNeil, A. J., Frey, R. & Embrechts, P. (2015). Quantitative Risk Management: Concepts, Techniques, and Tools. (revised edition). Princeton University Press.
- Miller, M. B. (2019). Quantitative Financial Risk Management. John Wiley & Sons, Inc.
- Ruschendorf, L. (2013). Mathematical Risk Analysis: Dependence, Risk Bounds, Optimal Allocations and Portfolios. Springer.