|
Lecturer(s)
|
-
Pavlačka Ondřej, RNDr. Ph.D.
-
Bebčáková Iveta, Mgr. Ph.D.
|
|
Course content
|
1. Risk and uncertainty. 2. Risk-management. 3. Main tools for modelling of risk - Monte Carlo simulation, scenarios, probabilistic trees. 4. Monte Carlo simulation - mathematical background, building a model, risk measuring 5. Fuzzy sets. 6. Operation with fuzzy sets. 7. Fuzzy numbers. Extension principle. Defuzzification. 8. Linguistic modelling. 9. Fuzzy inference system - Mamdani and Takagi-Sugeno approach. Fuzzy regulation.
|
|
Learning activities and teaching methods
|
|
Lecture, Demonstration
|
|
Learning outcomes
|
Meet the mathematical tools for modelling risk and uncertainty - Monte Carlo simulation, fuzzy sets theory.
Capability to appropriately model and measure risk and model uncertainty.
|
|
Prerequisites
|
calculus, probability theory, statistics
|
|
Assessment methods and criteria
|
Student performance, Seminar Work
Solving the given problems (Monte Carlo simulation, building fuzzy inference system). Active participation.
|
|
Recommended literature
|
-
Artzner, P., Delbaen, F., Eber, J.-M., Heath, D. (1999). Coherent measures of risk. Mathematical Finance 9.
-
D. Dubois, H. Prade (Eds). (2000). Fundamentals of fuzzy sets. Kluwer Academic Publishers, Boston, London, Dordrecht.
-
D. W. Hubbard. (2020). The Failure of Risk Management: Why It's Broken and How to Fix It (2nd Ed.). Wiley.
-
G. J. Klir, B. Yuan. (1996). Fuzzy sets and Fuzzy logic: Theory and Applications. Prentice Hall, New Jersey.
-
Hnilica, J., Fotr, J. (2014). Aplikovaná analýza rizika ve finančním managementu a investičním rozhodování. (2. vydání). Grada Publishing.
-
J. Talašová. (2003). Fuzzy metody vícekriteriálního hodnocení a rozhodování. VUP, Olomouc.
-
R. Bělohlávek, J.W. Dauben, G.J. Klir. (2017). Fuzzy Logic and Mathematics: A Historical Perspective. Oxford University Press.
-
V. Novák. (1990). Fuzzy množiny a jejich aplikace. SNTL, Praha.
-
Vose, D. (2008). Risk Analysis: a Quantitative Guide (3rd Ed.). New York.
|