Syllabus for |
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TMS088 - Financial time series
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| Finansiella tidsserier |
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| Syllabus adopted 2019-02-22 by Head of Programme (or corresponding) |
| Owner: MPENM |
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7,5 Credits
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| Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail |
| Education cycle: Second-cycle |
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Major subject: Mathematics |
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Department: 11 - MATHEMATICAL SCIENCES
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Teaching language: English
Application code: 20126
Open for exchange students: Yes
| Module |
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0117 |
Examination |
7,5c |
Grading: TH |
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7,5c
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04 Jun 2021 pm J, |
02 Jan 2021 am J
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23 Aug 2021 am J
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In programs
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory elective)
TKIEK INDUSTRIAL ENGINEERING AND MANAGEMENT - Financial mathematics, Year 3 (compulsory)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory elective)
Examiner:
Annika Lang
Go to Course Homepage
Eligibility
General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
Good knowledge of calculus and linear algebra, knowledge of basic probability and statistics. Some knowledge of stochastic processes and programming in matlab is highly desirable.
Aim
Students will gain an understanding of the classical time-series theory
and practice with an emphasis on the modeling of financial time series.
They will develop an appreciation of the issues, goals and approaches of
this theory through being exposed to basic probabilistic models, tools,
and statistical estimation methods specific to this field. In the frame
of the general time-series set-up they will develop an appreciation of the specific issues
related to the analysis and forecasting of financial returns.
Learning outcomes (after completion of the course the student should be able to)
- Compute and interpret marginal distributions and autocorrelation functions in time series
- Derive the properties of ARIMA and GARCH models
- Choose an appropriate ARIMA/GARCH model for a given set of data and fit the model using an appropriate package
- Compute forecasts for a variety of linear and non-linear methods and models
Content
This course introduces time series techniques and their application to the analysis and forecasting of financial time series. Topics covered include:
ARIMA models - probabilistic properties and estimation- Stationary processes
- The autocovariance and the autocorrelation functions
- Basic properties of ARMA processes
- Linear process representation
- Estimation of ARMA processes
ARCH and GARCH processes - theory and practice of volatility modeling
- The ARCH family, definition and relation with ARMA processes
- Extensions of GARCH processes
Nonlinear models
- Bilinear models and Markov switch autoregressive models
- Model fitting using kernel regression, bandwidth selection, and local linear regression
- Non-parametric and parametric tests for non-linearity
- Forecasting and prediction performance measures
Organisation
The theoretical discourse is supplemented by hands-on data analysis. Familiarity with a statistical software analysis tool (like Matlab, Splus, R) is assumed.
Literature
The course literature is given on a separate list.
Examination including compulsory elements
Written exam. Programming exercises to gain bonus points for the final exam.