GRADUATE SCHOOL
Financial Economics (With Thesis)
FM 551 | Course Introduction and Application Information
| Course Name |
Scientific Computation and Simulation in Finance
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
FM 551
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
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| Course Language |
English
|
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| Course Type |
Elective
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| Course Level |
Second Cycle
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| Course Coordinator | - | |||||
| Course Lecturer(s) | - | |||||
| Assistant(s) | - | |||||
| Course Objectives | Scientific computation and simulation in finance is a crossdisciplinary field which relies on mathematical finance, numerical methods and computer simulations to make trading, hedging and investment decisions, as well as facilitating the risk management of those decisions. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | Scientific computation and simulation in finance is a crossdisciplinary field which relies on mathematical finance, numerical methods and computer simulations to make trading, hedging and investment decisions, as well as facilitating the risk management of those decisions. |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Errors, Condition Numbers, Norms | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 2 | Solving Linear Systems (Application: Markov Chains) | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 3 | Best fit and least squares (Application: CAPM) | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 4 | Nonlinear Equations (Application: Implied Volatility) | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 5 | Optimization (Application: Optimal Portfolios) | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 6 | Interpolation (Application) | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 7 | Quadrature (Application: Pricing European Claims) | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 8 | Numerical MEthods for ODEs | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 9 | BlackScholes PDE and Heat Equation | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 10 | Explicit Finite Differences for PDEs | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 11 | Backward Finite Differences & CrankNicolson Scheme | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 12 | Pricing European Claims | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 13 | CRR Model and Binomial trees | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 14 | Numerical Methods for American Options | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 15 | Special methods for interestrate models | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| 16 | Review of the Semester |
| Course Notes/Textbooks | Seydel, R. Tools for Computational Finance (latest edition).Siegman and Davis. Matlab Primer, Chapman/Hall. |
| Suggested Readings/Materials | Implementing derivative models. Authors: L. Clewlow, Ch. Strickland. John Wiley and Sons, Ltd., 1998.Statistical Analysis of Financial Data in SPlus. Authors: Ren A. Carmona. Springer Texts in Statistics, January 2004. Introduction to Stochastic Calculus Applied to Finance. Authors: D. Lamberton and B. Lapeyre. Chapman and Hall/CRC, 1996. |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project |
1
|
20
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
30
|
| Final Exam |
1
|
50
|
| Total |
| Weighting of Semester Activities on the Final Grade |
50
|
|
| Weighting of End-of-Semester Activities on the Final Grade |
50
|
|
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
15
|
5
|
75
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
0
|
||
| Project |
1
|
30
|
30
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
32
|
32
|
| Final Exam |
1
|
40
|
40
|
| Total |
225
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
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| 1 | To improve and deepen expertise in economics and finance. |
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| 2 | To be able to comprehend the interaction between economics, finance and related fields. |
X | ||||
| 3 | To be able to apply the advanced level knowledge acquired in economics and finance. |
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| 4 | To be able to create new knowledge by combining the knowledge of finance and economics with the knowledge coming from other disciplines and be able to solve problems which requires expert knowledge by applying scientific methods. |
X | ||||
| 5 | To be able to use computer programs needed in the fields of economics and finance as well as information and communication technologies in advanced levels. |
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| 6 | To be able to think analytically to identify problems in finance and economics and to be able to make policy recommendations in economics and finance based on scientific analysis of issues and problems. |
X | ||||
| 7 | To be able to develop new strategic approaches for unexpected, complicated situations in finance and economics and take responsibility in solving it. |
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| 8 | To protect the social, scientific and ethical values at the data collection, interpretation and dissemination stages and to be able to institute and observe these values. |
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| 9 | To be able to critically evaluate the knowledge in finance and economics, to lead learning and carry out advanced level research independently. |
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| 10 | To be able to use a foreign language for both following scientific progress and for written and oral communication. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest