| Title |
Markovo grandinių Monte Karlo metodo taikymas stochastinėms sistemoms modeliuoti / |
| Translation of Title |
The application of Markov chain Monte Carlo method to modelling of stochastic systems. |
| Authors |
Landauskas, Mantas |
| Full Text |
|
| Pages |
107 |
| Keywords [eng] |
Markov chain Monte Carlo method ; kernel density estimate ; queuing system ; stock prices |
| Abstract [eng] |
The application of Markov chain Monte Carlo (MCMC) technique for two stochastic systems was investigated in this paper. A simple single channel system with a queue was modelled considering the service time distribution is of unknown form. Analysis showed that MCMC is possible to apply and gives adequate results. The custom approach of constructing a proposal density for MCMC was proposed and applied to find the value of a European call option as the classical techniques can not be applied in all the cases. This custom approach leads the enhanced MCMC to be applied to any empirical data having no assumptions about its probability density. Both applications were programmed in C++. This useful tool can be used for pricing the European option of a real stock having its historical prices. Modelling a theoretical queuing system with the distribution of the service time of an unknown form is also implemented. A MATLAB approach for modelling the dynamics of a stock prices with enhanced MCMC can be found in appendix 2 and 3. |
| Type |
Master thesis |
| Language |
Lithuanian |
| Publication date |
2011 |
