Syllabus
Registration via LPIS
Day | Date | Time | Room |
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Thursday | 11/29/18 | 09:00 AM - 12:30 PM | TC.5.03 |
Thursday | 12/06/18 | 09:00 AM - 12:30 PM | TC.5.03 |
Thursday | 12/13/18 | 09:00 AM - 12:30 PM | TC.5.03 |
Thursday | 12/20/18 | 09:00 AM - 12:30 PM | TC.5.03 |
Thursday | 01/10/19 | 09:00 AM - 12:30 PM | TC.5.03 |
Thursday | 01/17/19 | 09:00 AM - 12:30 PM | TC.5.03 |
Thursday | 01/24/19 | 09:00 AM - 12:30 PM | TC.5.03 |
Thursday | 01/31/19 | 09:00 AM - 11:00 AM | TC.0.01 ERSTE |
After completing this course the student will have the ability to:
- design and perform simulation experiments
- recall the basic tools for exploring univariate and multivariate data sets
- measure and model key characterics of financial data
Apart from that, the course will contribute to the students' ability to:
- demonstrate effective team skills in order to contribute appropriately to the production of a group output
- work, communicate and participate effectively in a team situation and group discussions and to function as a valuable and cooperative team member
Moreover, after completing this course the student will have the ability to:
- adequately communicate the results of exploring data
- discuss empirical findings in the light of domain knowledge
- use the web to access and extract financial data
In addition, the student will be able to:
- use R for simulation as well as manipulating and exploring data
Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.
- 40% home assignments and group discussions
- 30% course project
- 30% written final exam
The assessment of the homework assignments and course project will be based on the correctness of results, the clarity and persuasiveness of each bit of work and the recognizable effort made. This implies an ability to work in teams. For the written exam, the assessment will be based on the ability to describe and apply the key concepts discussed throughout the course and to choose the appropriate analytical techniques to obtain the relevant data.
To avoid the potential free-rider problem related to group work, the final exam will strongly be related to the problems already discussed in homework assignments and course projects.
Please note that there will be no opportunity to retake the written final exam.
- Successful completion of the "Mathematics I" course
- Successful completion of the "Financial Markets and Instruments" course
- Basic knowledge in probability and statistics (on an undergraduate level)
Unit | Date | Contents |
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1 | Numerical Linear Algebra After attending this session, students should recall key matrix operations and decompositions in theory and practice, and how the compositions can be employed for the numerical solution of linear systems. Reference: Braun and Murdoch, Chapter 6. |
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2 | Random Number Generation and Simulation After attending this session, students should know about the principles of random number generation and be able to perform Monte Carlo simulation and integration. Reference: Braun and Murdoch, Chapter 5. |
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3 | Data Management After attending this session, students should be able to use R to access and extract data from a variety of sources and formats, including plain text files with comma or tab separated values, spreadsheets as well as web sites or HTML tables embedded in these. The students should also be able to organize and transform data for subsequent analyses. |
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4 | Data Exploration After attending this session, students should be able to explore univariate and multivariate data, making use of traditional and modern visualization techniques, including histograms, Q-Q plots, and mosaic plot variants. Reference: Carmona, Chapters 1 and 2 |
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5 | Graphics and Quantmod After attending this session, students should have gained a working knowledge of the R graphics engines and systems and be able to customize high-level graphics via annotation. They should also be able to use R to obtain financial data from the web. Reference: Braun and Murdoch, Chapter 3. |
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6 | Heavy Tails and Copulas After attending this session, students should recall techniques for investigating and modeling the tails of distributions, classical measures of dependence, and how to use copulas for modeling dependence in financial data. Reference: Carmona, Chapters 1 and 2. |
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7 | Presentations and Review After attending this session students should recall developing, presenting and discussing the results of using R for scientific computing and analyzing financial data. They should also assess their efficiency for self and group organization and reflect upon the "big picture" of this course. |
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8 | Final exam |
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