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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2015/2016

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Monte Carlo Methods (MATH11155)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits5 ECTS Credits2.5
SummaryThis 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Simulation (MATH10015)
Other requirements None
Information for Visiting Students
Pre-requisitesNone
Course Delivery Information
Academic year 2015/16, Available to all students (SV1) 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 )
Assessment (Further Info) Written Exam 80 %, Coursework 20 %, Practical Exam 0 %
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.
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
KeywordsMCM
Contacts
Course organiserDr Sotirios Sabanis
Tel: (0131 6)50 5084
Email:
Course secretaryMrs Kathryn Mcphail
Tel: (0131 6)51 4351
Email:
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