Course detail

Financial Risk Management

FP-EfrmPAcad. year: 2014/2015

The subject deals with the problem of financial risk management. The concepts such as risk are specified there, further credit, market, interest, unique and systematic risk. There are mentioned the possibilities of financial risk management by means of derivatives, such as forwards, futures, swaps and options. The advanced methods include description of possible use of fuzzy logic, artificial neural networks and genetic algorithms during the process of financial risk management.

Learning outcomes of the course unit

Analyze the credit, market, interest, unique and systematic risk.
Demonstrate the ability of derivatives, such as forwards, futures, swaps and options
Use fuzzy logic, artificial neural networks and genetic algorithm in financial risk management

Prerequisites

The knowledge in the area of math (linear algebra, arrays, analyses of functions, operations with matrixes) statistics (analysis of time series, regression analyses, the use of statistical methods in economy), operational analysis (linear programming), financial analyses and planning (the analyses of profits and costs, cash flow, value and bankruptcy model).

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Mishkin F.S., Eakins S.G., Financial markets and Institutions, 3rd Edition, Addison Wesley Longman 2000 (EN)
Dostál, P, Sojka, Z.: Financial Risk Management, TUB – FBM - Zlín 2008, ISBN 978-80-7318-772-9 (EN)
Steiner R., Mastering Financial Calculations. A Step-by-Step Guide to the Mathematics of Finacial Market Instruments, Finacial Times Management 1998
Soper J. Mathematic for Economics and Business, Blackwell Publishing,UK, 2004 ISBN 1-4051-1127-5 (EN)
DOSTÁL, P. Advanced Decision Making in Business and Public Services. Brno: CERM Akademické nakladatelství, 2013. 168 p. ISBN: 978-80-7204-747-5. (EN)

Planned learning activities and teaching methods

The course contains lectures that explain basic principles, problems and methodology of the discipline, and exercises that promote the practical knowledge of the subject presented in the lectures.

Assesment methods and criteria linked to learning outcomes

The credit will be granted in case of an active participation in trainings and handing in the final assignment. The work will be processed in Excel file concentrating on individual problem from practice leading to solution with the help of theory of fuzzy logic.
The exam will be classified according ECTS. The way of implementation is in the form of written form pointed within the range 0-20 points. A-20-19;B18-17;C16-15;D14-13;E12-;F10-0.

Language of instruction

English

Work placements

Not applicable.

Course curriculum

1.To be familiar with the basic notions such as risk, credit risk and market risk.
2.Classical methods of risk management such as interest risk, unique and systematic risk.
3.Basis of derivatives such as forwards, futures, swaps and options. The examples of computer support.
4.Advanced methods of risk management such as fuzzy logic, neural networks and genetic algorithms

Aims

The aim of the above mentioned subject is to be familiar with the problem of financial risk management. The concepts such as risk are specified there, further credit, market, interest, unique and systematic risk. There are mentioned the possibilities of financial risk management by means of derivatives, such as forwards, futures, swaps and options. The advanced methods include description of possible use of fuzzy logic, artificial neural networks and genetic algorithms during the process of financial risk management.

Specification of controlled education, way of implementation and compensation for absences

The participation in meetings, consultations. The check of results of individual written assignments.

Classification of course in study plans

  • Programme MGR-EBF Master's

    branch MGR-EBF , 1. year of study, summer semester, 6 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Exercise

13 hours, optionally

Teacher / Lecturer

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