MF006 Seminary on Financial Mathematics

Faculty of Science
Spring 2027
Extent and Intensity
0/2/0. 2 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: z (credit).
In-person direct teaching
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
Course Enrolment Limitations
The course is offered to students of any study field.
Abstract
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
Key topics
  • Methods of data analysis
  • Game theory
  • Bayesian models
  • Methods of stochastic analysis
  • Models for derivatives pricing
Study resources and literature
  • TSE, Yiu Kuen. Nonlife actuarial models : theory, methods and evaluation. Second edition. Cambridge: Cambridge University Press, 2023, xiv, 535. ISBN 9781009315074. info
  • Introduction to Deep Learning Using R A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R. Edited by Taweh Beysolow II. Berkeley, CA: Imprint: Apress, 2017, XIX, 227. ISBN 9781484227343. URL info
  • DICKSON, D. C. M.; Mary HARDY and H. R. WATERS. Actuarial mathematics for life contingent risks. 2nd ed. Cambridge: Cambridge University Press, 2013, xxi, 597. ISBN 9781107044074. 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
Approaches, practices, and methods used in teaching
Student presentations of selected topics
Method of verifying learning outcomes and course completion requirements
Successful presentation of the selected topic.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught every week.
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 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026.
  • Enrolment Statistics (Spring 2027, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2027/MF006