Financial Model Implementation and Practice
Course ID
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Mandatory prerequisites
Recommended prerequisites
Aim and purpose
Content
The course is constructed to cover a wide variety of topics in financial modelling:
For example:
- Corporate valuation in terms of Discounted Cash Flow models
- Portfolio optimization problem in finance
- Valuation of options
- Fixed income analytics
- Financial Risk modelling
Learning goals
Description of outcome - Knowledge
Demonstrate knowledge about the course’s focus areas enabling the student to
- Explain the model choice for selected research topics.
- Explain the parameters, characteristics of selected empirical models.
- Understand simulation techniques, e.g. Monte Carlo simulation, that can be used to compute values of complicated functions that often have no analytical solution.
Description of outcome - Skills
Demonstrate skills, such that the student is able to:
- Implement theoretical models and numerical computation for enterprise valuation.
- Calculate the variance-covariance matrix and Find optimal portfolios.
- Back-test portfolio performance.
- Apply option pricing models, e.g. the binomial model and alternative models.
- Analyze fixed income attributions, e.g. term structure, duration, with selected models.
- Measure financial risk with Value-at-risk and alternative models.
- Apply numerical methods e.g. Monte Carlo simulation, to the pricing of derivatives, portfolio optimization, and financial risk management.
Description of outcome - Competences
Demonstrate competences, such that the student is able to:
- Use software (for example, Excel/VBA, or alternative software agreed between the student and instructor) to skillfully implement financial models introduced in the course in order to make empirical financial analysis.
- Discuss the meaning of empirical results and the plausible values of its parameters.
- Compare the advantages and disadvantages of different models and argue his/her model choice for specific dataset.
Literature
Examples
- Benninga, Simon: “Financial modelling” The MIT press, newest edition.
- Reading package and lecture notes.
Teaching Method
To enable the student to achieve the learning goals of the subject, the instruction is planned as following:
- The preparation package will be given two weeks before lecture starts to help the student to get familiar with the theories and models which will be used during lectures
- In-class lectures provide practical contents of the topics
- In-class exercises help students consolidate the knowledge and skills taught in the lectures
- Two consultation sessions provide students opportunities of discussing unclear topics and assist them to better apply the models with different settings.
Workload
- 6 hours of lectures per day (6x5) for 2 consecutive weeks
- Each six-hour teaching session mixes the lecture and in-class exercises
- The course will be conducted from the second week of August
Workload:
The students' workload is expected to be distributed as follows:
Lectures: 60 hours
Preparation, lectures: 136 hours
Preparation, exam: 62 hours
Workload in connection to the exam: 12 hours
Total: 270 hours
Examination regulations
Exam
Name
Timing
Take-home assignment (part 1):
Exam: August
Reexam: September
Tests
Take-home assignment (part 1)
Name
Form of examination
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Identification
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Assignment handover
Assignment handin
ECTS value
Additional information
The assignment will be handed over at noon (12:00) on the Friday of the first teaching week.
Reexam in same exam term. Form of examination can be changed with short notice.
EKA
Written exam (part 2)
Name
Form of examination
Censorship
Grading
Identification
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Assignment handover
Assignment handin
ECTS value
Additional information
The exam will be on the first Monday after two-week lectures.
The exam tests the achievement of the goals for all the goals mentioned (cf. the goal description) by random check.
Reexam in same exam term. Form of examination can be changed with short notice.