Fixed Income Derivatives: Risk Management and Financial Institutions (F)
Course content
In the world of today, both public and private institutions rely heavily on bond issuance to raise capital, and fixed income markets have come to play a central role in the global economy. This development has led to a rapid increase in the use of ever more sophisticated derivatives playing a dual role on one hand as means to insure against losses and on the other as tools of risky speculation. Interest rate derivatives have often played a central role in times of financial distress highlighting the need for financial actors to have a solid framework for pricing, hedging and risk management of these instruments.
Throughout this course, students will develop a thorough understanding of how fixed income markets can be modeled and how pricing and hedging of the most commonly traded fixed income derivatives can be performed within these models. Much of the course will be set in continuous time, and we will begin by covering the basics of stochastic calculus including Brownian motion, stochastic differential equations, Ito's formula, etc. This portion of the course will be somewhat technical, however emphasis will be on application of the methods and results we introduce. Once we have laid the mathematical foundation for the course, we will proceed to study dynamic models for the short rate and how the term structure of interest rates evolves in these types of models. As part of our discussion, we will learn how to fit term structure models to market data and how forward rate agreements, interest rate swaps and exchange options can be priced in the context of these models. Next, we will study the pricing, and hedging of more complicated interest rate options such as caps, floors, digital options and swaptions as well as how the “greeks” can be used for hedging and risk management of such contracts. Finally, we will cover more exotic financial derivatives including currency contracts such as FX forwards, FX swaps and cross currency swaps and credit derivatives such as asset swaps and credit default swaps.
The course will be somewhat technical and quantitative in nature, but emphasis will be placed on developing results that have applications in practice. The many methods and tools, we will develop, will be implemented using Python and hence, experience with a scripting language such as Python, Matlab, Julia or R will be helpful. By the end of this course, we will have developed a substantial library in Python containing some of the methods and algorithms most commonly used by financial practitioners.
MSc programme in Economics – elective course
The course is part of the Financial line at the MSc programme in Economics, symbolized by ‘F’.
The course is open to:
- Exchange and Guest students from abroad
- Credit students from Danish Universities
- Open University students
After completing the course the student is expected to be able to:
Knowledge:
- Develop an intuition for the mathematical framework underlying continuous time models.
- Know some of the most widely used dynamic models of the term structure of interest rates.
- Understand the properties of a wide range of interest rate derivatives.
- Deduce the risks associated with a wide range of derivatives
commonly traded in financial markets.
Skills:
- Choose an appropriate model to price and/or hedge commonly traded interest rate derivatives.
- Critically asses a financial model including its limitations and applicability in practice.
- Determine methods to price interest rate derivatives within the context of a dynamic model.
- Identify why a given model might not fit market data and suggest how to improve the model.
Competences:
- Implement and fit a given dynamic term structure model to market data using Python.
- Calculate prices of a wide range of commonly traded interest rate derivatives.
- Dynamically compute a replicating strategy to hedge an interest rate derivative in practice.
The course, will consist of lectures, exercise classes and assignments. Students are not required to hand in the assignments posted throughout the course but are strongly encouraged to work on these to better understand the material and as preparation for the exam.
Syllabus:
- Arbitrage Theory in Continuous Time (4th edition), Thomas Bjôrk, Oxford University Press, December 5. 2019, Chapters 4-5 and 20-25, Online ISBN: 9780191886218, Print ISBN: 9780198851615, https://doi.org/10.1093/oso/9780198851615.001.0001
- Fixed Income Derivatives Lecture Notes, Martin Linderstrøm, University of Copenhagen, February 3. 2013 Interpolation Methods for Curve Construction, Patrick S. Hagan and Graeme West, Applied Mathematical Finance, June 2006, Vol 13, No 2., pages 89-129, https://doi.org/10.1080/13504860500396032
- Managing Smile Risk, Patrick S. Hagan, Deep Kumar, Andrew S. Lesniewski, Diana E. Woodward, Wilmott Magazine, January 2002, Vol 1, pages 84-108
- Pricing Derivatives on Financial Securities Subject to Credit Risk, Robert Jarrow and Stuart M. Turnbull, Journal of Finance, March 1995, https://doi.org/10.1111/j.1540-6261.1995.tb05167.x
- Valuation of Credit Default Swaps, Dominic O'Kane and Stuart Turnbull, Fixed Income Quantitative
- Research, Lehman Brothers, April 2003
- Lecture notes and slides
Supplementary reading:
- Arbitrage Theory in Continuous Time (4th edition), Thomas Bjôrk, Oxford University Press, December 5. 2019, Chapters 1-3 and 6-8, Online ISBN: 9780191886218, Print ISBN: 9780198851615,
- https://doi.org/10.1093/oso/9780198851615.001.0001
- Stochastic Calculus for Finance II: Continuous-Time Models, Steven Shreve, Springer Finance, June 28. 2005, Chapters 1-6, ISBN-10: 0387249680, ISBN-13: 978-0387249681
References:
- Pricing Derivatives on Financial Securities Subject to Credit Risk, Robert Jarrow and Stuart M. Turnbull,
- Journal of Finance, March 1995, https://doi.org/10.1111/j.1540-6261.1995.tb05167.x
- Valuation of Credit Default Swaps, Dominic O'Kane and Stuart Turnbull, Fixed Income Quantitative
- Research, Lehman Brothers, April 2003
This course is not an introductory course, and students are
required to have a basic knowledge of derivatives pricing including
Black-Scholes formula and fixed income markets. It is therefore
expected that students have already followed the course
'Financial Decision Making'(previously 'Corporate
Finance and Incentives') offered at the Economics program at
University of Copenhagen or a similar course. It is possible to
take the two courses 'Financial Decision Making' and
'Fixed Income Derivatives' concurrently, but students doing
so are advised that an extra effort on their part will be
necessary.
The material we cover is fairly advanced and in particular, some of
the models used in this course are set in continuous time. Though
students are not assumed to have experience with continuous time
finance from a previous course, it is most helpful if they do.
Brownian motion and Ito calculus will be introduced early on in
this course and students with no prior experience with continuous
time finance must expect to spend some time and effort learning
these concepts.
Finally, it is important to stress that an integral part of this
course will involve programming in Python. Though no prior
knowledge of Python is assumed, students are expected to have some
basic programming experience.
Schedule:
There will be a total of 4 hours of teaching a week from week 36 to
50 (except week 42).
Individual feedback can be received at the exercise classes.
for enrolled students: Rules etc: Master(UK) and Master(DK)
When registered you will be signed up for exam.
- Full-degree students – sign up at Selfservice on KUnet
- Exchange and guest students from abroad – sign up through Mobility Online and Selfservice- read more through this website.
- Credit students from Danish universities - sign up through this website.
- Open University students - sign up through this website.
The dates for the exams are found here Exams – Faculty of Social Sciences - University of Copenhagen (ku.dk)
Please note that it is your own responsibility to check for overlapping exam dates.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Home assignment, 12 hours
- Type of assessment details
- Individual. Max 20 standard pages.
- Examination prerequisites
-
There are no requirements during the course that the student has to fulfill to be able to sit the exam.
- Aid
- All aids allowed
Use of AI tools is permitted. You must explain how you have used the tools. When text is solely or mainly generated by an AI tool, the tool used must be quoted as a source.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Exam period
-
Exam information:
The examination date can be found in the exam schedule here
More information is available in Digital Exam from the middle of the semester.
More information about examination, rules, aids etc. at Master (UK) and Master (DK)
- Re-exam
-
Same as the ordinary exam.
Reexam information:
The reexamination date/period can be found in the reexam schedule here
More information in Digital Exam in February.
More info at Master(UK), and Master(DK)
Criteria for exam assessment
Students are assessed on the extent to which they master the learning outcome for the course.
In order to obtain the top grade "12", the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.
In order to obtain the passing grade “02”, the student must in a satisfactory way be able to demonstrate a minimal acceptable level of the knowledge, skills and competencies listed in the learning outcomes.
Single subject courses (day)
- Category
- Hours
- Lectures
- 42
- Class Instruction
- 30
- Preparation
- 122
- Exam
- 12
- English
- 206
Kursusinformation
- Language
- English
- Course number
- AØKA08204U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 semester
- Placement
- Autumn
- Price
-
Information about admission and tuition fee: Master and Exchange Programme, credit students and guest students (Open University)
- Studyboard
- Department of Economics, Study Council
Contracting department
- Department of Economics
Contracting faculty
- Faculty of Social Sciences
Course Coordinator
- Jacob Lundbeck Serup (20-4d64667265314f7871676568666e3156687578734368667271316e7831676e)
Teacher
See "Course Coordinators".
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