401-4657-00L Numerical Analysis of Stochastic Ordinary Differential Equations
| Semester | Autumn Semester 2020 |
| Lecturers | D. Salimova |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods" |
Courses
| Number | Title | Hours | Lecturers | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 401-4657-00 V | Numerical Analysis of Stochastic ODEs (Comp. Meth. Quant. Fin.: Monte Carlo and Sampling Methods) The lecturers will communicate the exact lesson times of ONLINE courses | 3 hrs |
| D. Salimova | ||||||
| 401-4657-00 U | Numerical Analysis of Stochastic ODEs (Comp. Meth. Quant. Fin.: Monte Carlo and Sampling Methods) Groups are selected in myStudies. Responsible lecturer: Dr. D. Salimova The lecturers will communicate the exact lesson times of ONLINE courses. | 1 hrs |
| D. Salimova |
Catalogue data
| Abstract | Course on numerical approximations of stochastic ordinary differential equations driven by Wiener processes. These equations have several applications, for example in financial option valuation. This course also contains an introduction to random number generation and Monte Carlo methods for random variables. |
| Learning objective | The aim of this course is to enable the students to carry out simulations and their mathematical convergence analysis for stochastic models originating from applications such as mathematical finance. For this the course teaches a decent knowledge of the different numerical methods, their underlying ideas, convergence properties and implementation issues. |
| Content | Generation of random numbers Monte Carlo methods for the numerical integration of random variables Stochastic processes and Brownian motion Stochastic ordinary differential equations (SODEs) Numerical approximations of SODEs Applications to computational finance: Option valuation |
| Lecture notes | There will be English, typed lecture notes for registered participants in the course. |
| Literature | P. Glassermann: Monte Carlo Methods in Financial Engineering. Springer-Verlag, New York, 2004. P. E. Kloeden and E. Platen: Numerical Solution of Stochastic Differential Equations. Springer-Verlag, Berlin, 1992. |
| Prerequisites / Notice | Prerequisites: Mandatory: Probability and measure theory, basic numerical analysis and basics of MATLAB programming. a) mandatory courses: Elementary Probability, Probability Theory I. b) recommended courses: Stochastic Processes. Start of lectures: Wednesday, September 16, 2020. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 6 credits |
| Examiners | D. Salimova |
| Type | end-of-semester examination |
| Language of examination | English |
| Repetition | A repetition date will be offered in the first two weeks of the semester immediately consecutive. |
| Additional information on mode of examination | Learning tasks: Meaningful solutions to 70% of the weekly homework assignments can count as bonus of up to +0.25 of final grade. End-of-Semester examination will be *closed book*, 2hr in class, and will involve theoretical as well as MATLAB programming problems. Examination will take place on ETH-workstations running MATLAB. Own computer will NOT be allowed for the examination. |
| Digital exam | The exam takes place on devices provided by ETH Zurich. |
Learning materials
| Main link | Information |
| Only public learning materials are listed. | |
Groups
| 401-4657-00 U | Numerical Analysis of Stochastic ODEs (Comp. Meth. Quant. Fin.: Monte Carlo and Sampling Methods) | ||||||
| Groups | G-ON 01 |
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| G-01 |
| ||||||
Restrictions
| Priority | Registration for the course unit is only possible for the primary target group |
| Primary target group | Doctorate Architecture (064000)
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