|
Lecturer(s)
|
-
Heckenbergerová Jana, Mgr. Ph.D.
-
Boháčová Hana, Mgr. Ph.D.
|
|
Course content
|
Valuation of options. Binomial tree model. Replication portfolio, self-financing equation. Put-call parity. American option valuation. Black-Scholes model of option pricing. Optional characteristics - Greeks. Markov chains. Classification of Markov chain states. Bonus-malus. Poisson process. Markov process. Non-homogeneous Markov process. Brownian motion. Wiener process. Black-Scholes partial differential equation.
|
|
Learning activities and teaching methods
|
- Home preparation for classes
- 30 hours per semester
- Practical training
- 26 hours per semester
- Contact teaching
- 26 hours per semester
- Contact teaching
- 14 hours per semester
- Preparation for a credit (assessment)
- 25 hours per semester
- Preparation for an exam
- 25 hours per semester
- Preparation for a partial test
- 18 hours per semester
- Term paper
- 30 hours per semester
- Preparation of a presentation (report)
- 26 hours per semester
|
|
Learning outcomes
|
The aim of the course is to develop the skills necessary for the construction of asset and liability models and for the valuation of financial derivatives for the needs of investors using stochastic processes and stochastic modeling, which can achieve a substantial reduction of risks in investing funds.
A student who has successfully completed the course can: explain the sources of randomness in the construction of the model and note the limitations of the model; explain the importance of stochastic approach to modeling;describe the use of stochastic models in different fields; to characterize short-term and long-term factors affecting the design of the model; mutually compare market prices and theoretical values of models; explain the importance of asset valuation models to characterize their limits. A student who has successfully completed the course can: use some of the industry's basic techniques to the extent necessary to address the industry's practical tasks; distinguish the need for a stochastic model in continuous or discrete time. The student who has successfully completed the course is able to: to include in their problem solving a consideration of their potential impacts; to communicate in a clear and convincing way to professionals and lay people information on the nature of professional issues and their own opinion on their solution.
|
|
Prerequisites
|
Assumption of knowledge of the theory of probability to the extent of bachelor degree.
|
|
Assessment methods and criteria
|
unspecified
Credit - written form using MS Excel software Exam: written form - content of 20% theory and 80% computational task. To successfully complete the course, it is necessary to obtain at least 50% of points.
|
|
Recommended literature
|
-
NELSON, B. L. Stochastic Modeling: Analysis and Simulation. New York, 1995.
-
ROLSKI T. et al. Stochastic Processes for Insurance and Finance. New York, 2001.
|