MF006 Seminary on Financial Mathematics

Faculty of Science
Spring 2020
Extent and Intensity
0/2/0. 2 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: z (credit).
Teacher(s)
doc. RNDr. Martin Kolář, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Martin Kolář, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable of Seminar Groups
MF006/01: Fri 8:00–9:50 M2,01021, M. Kolář
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
Topics for the seminar will be selected from mathematical techniques and models used in financial institutions.
Learning outcomes
At the end of the course students should be able to: - explain mathematical foundations of the models - apply models to real data - interpret correctly the model predictions
Syllabus
  • Methods of data analysis,
  • Application of regression models,
  • Bayesian models,
  • Methods of stochastic analysis
  • Models for derivatives pricing
Literature
  • ANDERSON, Raymond. The credit scoring toolkit : theory and practice for retail credit risk management and decision automation. 1st pub. Oxford: Oxford University Press, 2007, lvi, 731. ISBN 9780199226405. info
  • SIDDIQI, Naeem. Credit risk scorecards : developing and implementing intelligent credit scoring. Hoboken, N.J.: Wiley, 2006, xi, 196. ISBN 047175451X. info
  • THOMAS, L. C., David B. EDELMAN and Jonathan N. CROOK. Credit scoring and its applications. Philadelphia, Pa.: Society for Industrial and Applied Mathematics, 2002, xiv, 248. ISBN 0898714834. URL info
  • WEST, Mike and Jeff HARRISON. Bayesian forecasting and dynamic models. 2nd ed. New York: Springer, 1997, xiv, 680. ISBN 0387947256. info
  • BISHOP, Christopher M. Neural networks for pattern recognition. 1st pub. Oxford: Oxford University Press, 1995, xvii, 482. ISBN 0198538499. info
Teaching methods
Student presentations of selected topics
Assessment methods
Successful presentation of the selected topic.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
The lessons are usually in Czech or in English as needed, and the relevant terminology is always given with English equivalents.

The target skills of the study include the ability to use the English language passively and actively in their own expertise and also in potential areas of application of mathematics.

Assessment in all cases may be in Czech and English, at the student's choice.

The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2020, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2020/MF006