Outline

Overview

Securities and derivatives are the foundation of modern financial markets. The fixed-income market, for example, is the dominant sector of the global financial market where various interest-rate linked securities are traded, such as zero-coupon and coupon bonds, interest rate swaps and swaptions. This unit will investigate short-term interest rate models, the Heath-Jarrow-Morton approach to instantaneous forward rates and recently developed models of forward London Interbank Offered Rates (LIBORs) and forward swap rates. You will learn about pricing and hedging of credit derivatives, another challenging and practically important problem and become familiar with stochastic models for credit events, dependent default times and credit ratings. You will learn how to value and hedge single-name and multi-name credit derivatives such as vulnerable options, corporate bonds, credit default swaps and collateralized debt obligations. You will also learn about the most recent developments in Financial Mathematics, such as robust pricing and nonlinear evaluations. By doing this unit, you will get a solid grasp of mathematical tools used in valuation and hedging of fixed income securities, develop a broad knowledge of advanced quantitative methods related to interest rates and credit risk and you will learn to use powerful mathematical tools to address important real-world quantitative problems in the finance industry.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Students are expected to have working knowledge of Stochastic Processes, Stochastic Calculus and mathematical methods used to price options and other financial derivatives, for example as in MATH4511 or equivalent
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Marek Rutkowski, marek.rutkowski@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final examination 4 problem based questions	60%	Formal exam period	3 hours
Outcomes assessed:
Assignment	Assignment 1 Problem based questions	20%	Week 07	2 weeks
Outcomes assessed:
Assignment	Assignment 2 Problem based questions	20%	Week 11	2 weeks
Outcomes assessed:
= Type D final exam ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100	Representing complete or close to complete mastery of the material.
Distinction	75 - 84	Representing excellence, but substantially less than complete mastery.
Credit	65 - 74	Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Standard penalties

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	An overview of the fixed-income market and interest rate derivatives. Market conventions and valuation principles for zero-coupon and coupon bonds.	Lecture and tutorial (4 hr)
Week 02	Models of the short term rate: Vasicek’s model, the Cox-Ingersoll-Ross (CIR) model, the Hull and White model and the Black-Karasinski model.	Lecture and tutorial (4 hr)
Week 03	Modelling of instantaneous forward rates through the Heath-Jarrow-Morton approach	Lecture and tutorial (4 hr)
Week 04	Valuation of bond options and other fixed income derivatives in Gaussian, lognormal and the CIR models through the change of a numeraire method.	Lecture and tutorial (4 hr)
Week 05	Modelling of forward LIBORs. Valuation and hedging of caps and floors in the LIBOR market model (LMM).	Lecture and tutorial (4 hr)
Week 06	Modelling of co-terminal and co-initial forward swap rates. Valuation and hedging of swaptions in Jamshidian’s swap market model (SMM).	Lecture and tutorial (4 hr)
Week 07	Vulnerable option and defaultable bonds. Structural approaches to the modelling of corporate credit risk: the Merton model of corporate debt and the Black and Cox approach.	Lecture and tutorial (4 hr)
Week 08	Properties of the Poisson and Cox processes and their stochastic exponentials	Lecture and tutorial (4 hr)
Week 09	Modelling of default times through hazard functions and hazard processes. Computations of conditional expectations and the study of the immersion property.	Lecture and tutorial (4 hr)
Week 10	Valuation of credit default swaps (CDSs) in the hazard function and hazard process approaches	Lecture and tutorial (4 hr)
Week 11	Models of conditionally independent default times and copula-based approaches to dependence of default events. Modelling of dependent default times: Jarrow and Yu model and Kusuoka’s approach.	Lecture and tutorial (4 hr)
Week 12	Analysis of basket credit derivatives: valuation of first-to-default swaps, CDS indices and collateralized debt obligations (CDOs).	Lecture and tutorial (4 hr)
Week 13	Exotic credit derivatives	Block teaching (4 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

MATH4513 Topics in Financial Mathematics (course notes on Canvas)

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Demonstrate familiarity with fundamental concepts in the area of financial markets with application to existing securities related to interest rates, such as: bonds, bond options and interest rate swaps and swaptions.
LO2. Develop stochastic models and solve qualitative and quantitative problems associated with the valuation and hedging of fixed income securities within the framework of short-term rate models.
LO3. Understand, explain and apply the principles of modelling of forward rates through different competing methods and analyse the relationships between alternative approaches.
LO4. Apply mathematical expertise to solve practical problems using various approaches and analyse the advantages and shortcomings of solutions obtained through different methods.
LO5. Examine the concept of credit risk and its impact on pricing problems for complex financial derivatives such as: corporate bonds, credit default swaps and collateralized debt obligations.
LO6. Analyse the issue of dependence between defaults of several credit-risky names and apply various probabilistic techniques for modelling of dependent defaults.
LO7. Identify, formulate and solve original practical problems that can be addressed using mathematical techniques learnt in this unit, interpret these solutions and evaluate their implementations.
LO8. Demonstrate capability for independent learning using sources such as journal articles and working papers and use this information to evaluate recently developed approaches and mathematical tools.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This is the first time this unit has been offered.

Additional information

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

MATH4513: Topics in Financial Mathematics

Semester 2, 2021 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect