Postgraduate Course: Monte Carlo Methods (MATH11155)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 5 |
ECTS Credits | 2.5 |
| Summary | This course aims to provide a good introduction to Monte Carlo methods with applications to finance. Topics that will be covered are: Random number generation, basic Monte Carlo, variance reduction techniques such as: importance sampling, control variates and antithetic random variable, Financial options price sensitivities (Greeks). Students are expected to implement above techniques in programming language such as Matlab. |
| Course description |
Random number generation, pseudorandom numbers, inversion method, acceptance/rejection method, Box-Muller method, basic Monte Carlo, quasi Monte Carlo.
Variance reduction techniques such as: importance sampling, control variates and antithetic random variable,
Option price sensitivities (Greeks): pathwise, likelihood and finite difference approaches.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Simulation (MATH10015)
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Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
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| Academic year 2015/16, Available to all students (SV1)
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Quota: None |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
50
(
Lecture Hours 10,
Supervised Practical/Workshop/Studio Hours 3,
Programme Level Learning and Teaching Hours 1,
Directed Learning and Independent Learning Hours
36 )
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| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Coursework 20%
Examination 80% |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
1. Demonstrate conceptual understanding of Monte Carlo methods by answering relevant exam questions.
2. Demonstrate the ability to simulate pseudo random numbers from standard distributions by constructing relevant algorithms in reports and/or exams.
3. Demonstrate the ability to numerically price some basic financial options by constructing relevant algorithms in reports and/or exams.
4. Demonstrate conceptual understanding of variance-reduction techniques and its importance for Monte Carlo simulations by answering relevant exam questions.
5. Demonstrate conceptual understanding of various methods of calculating sensitivities for financial applications (Greeks) by answering relevant exam questions.
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Reading List
Ross, S. M. (2002). Simulation (3rd ed.). Academic Press.
Boyle P, Broadie M, and Glasserman P (1997). Monte Carlo methods for security pricing, Journal of Economic Dynamics and Control, 4, 1267-1321. .
Hull, J. C. (2002). Options, Futures and Other Derivatives, 5th edition. Prentice Hall.
Glasserman, P. (2004). Monte Carlo methods in Financial Engineering. Springer.
Asmussen, S., Glynn, P. W., (2007) Stochastic Simulation: Algorithms and Analysis, Springer. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | MCM |
Contacts
| Course organiser | Dr Sotirios Sabanis
Tel: (0131 6)50 5084
Email: |
Course secretary | Mrs Kathryn Mcphail
Tel: (0131 6)51 4351
Email: |
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