dc.contributor.advisor | Beneš, Viktor | |
dc.creator | Haman, Jiří | |
dc.date.accessioned | 2021-03-23T21:12:35Z | |
dc.date.available | 2021-03-23T21:12:35Z | |
dc.date.issued | 2008 | |
dc.identifier.uri | http://hdl.handle.net/20.500.11956/17284 | |
dc.description.abstract | In this thesis we consider a stochastic volatility model based on non-Gaussian Ornstein-Uhlenbeck process (see also Barndor -Nielsen and Shephard [1]) where the logarithm of an asset price is the solution of a stochastic di erential equation without drift. The volatility component is modelled as a stationary, latent Ornstein-Uhlenbeck process, driven by a non-Gaussian Lévy process. We perform Bayesian inference for model parameters by means of Markov chain Monte Carlo algorithm based on data augmentation. The algorithm corresponds to a standard hierarchical parametrization of the model. The aim of this thesis is to express the unobserved stochastic volatility process for observed asset price. The algorithm is applied to the simulated and real asset price where real asset price is US dollar (USD) - Pound sterling (GBP) exchange rate. | en_US |
dc.language | Čeština | cs_CZ |
dc.language.iso | cs_CZ | |
dc.publisher | Univerzita Karlova, Matematicko-fyzikální fakulta | cs_CZ |
dc.title | Aplikace náhodných procesů ve financích | cs_CZ |
dc.type | diplomová práce | cs_CZ |
dcterms.created | 2008 | |
dcterms.dateAccepted | 2008-09-16 | |
dc.description.department | Department of Probability and Mathematical Statistics | en_US |
dc.description.department | Katedra pravděpodobnosti a matematické statistiky | cs_CZ |
dc.description.faculty | Faculty of Mathematics and Physics | en_US |
dc.description.faculty | Matematicko-fyzikální fakulta | cs_CZ |
dc.identifier.repId | 45982 | |
dc.title.translated | Applications of stochastic processes in finance | en_US |
dc.contributor.referee | Dostál, Petr | |
dc.identifier.aleph | 001001416 | |
thesis.degree.name | Mgr. | |
thesis.degree.level | navazující magisterské | cs_CZ |
thesis.degree.discipline | Probability, mathematical statistics and econometrics | en_US |
thesis.degree.discipline | Pravděpodobnost, matematická statistika a ekonometrie | cs_CZ |
thesis.degree.program | Mathematics | en_US |
thesis.degree.program | Matematika | cs_CZ |
uk.thesis.type | diplomová práce | cs_CZ |
uk.taxonomy.organization-cs | Matematicko-fyzikální fakulta::Katedra pravděpodobnosti a matematické statistiky | cs_CZ |
uk.taxonomy.organization-en | Faculty of Mathematics and Physics::Department of Probability and Mathematical Statistics | en_US |
uk.faculty-name.cs | Matematicko-fyzikální fakulta | cs_CZ |
uk.faculty-name.en | Faculty of Mathematics and Physics | en_US |
uk.faculty-abbr.cs | MFF | cs_CZ |
uk.degree-discipline.cs | Pravděpodobnost, matematická statistika a ekonometrie | cs_CZ |
uk.degree-discipline.en | Probability, mathematical statistics and econometrics | en_US |
uk.degree-program.cs | Matematika | cs_CZ |
uk.degree-program.en | Mathematics | en_US |
thesis.grade.cs | Výborně | cs_CZ |
thesis.grade.en | Excellent | en_US |
uk.abstract.en | In this thesis we consider a stochastic volatility model based on non-Gaussian Ornstein-Uhlenbeck process (see also Barndor -Nielsen and Shephard [1]) where the logarithm of an asset price is the solution of a stochastic di erential equation without drift. The volatility component is modelled as a stationary, latent Ornstein-Uhlenbeck process, driven by a non-Gaussian Lévy process. We perform Bayesian inference for model parameters by means of Markov chain Monte Carlo algorithm based on data augmentation. The algorithm corresponds to a standard hierarchical parametrization of the model. The aim of this thesis is to express the unobserved stochastic volatility process for observed asset price. The algorithm is applied to the simulated and real asset price where real asset price is US dollar (USD) - Pound sterling (GBP) exchange rate. | en_US |
uk.file-availability | V | |
uk.grantor | Univerzita Karlova, Matematicko-fyzikální fakulta, Katedra pravděpodobnosti a matematické statistiky | cs_CZ |
thesis.grade.code | 1 | |
dc.contributor.consultant | Karlova, Andrea | |
uk.publication-place | Praha | cs_CZ |
uk.thesis.defenceStatus | O | |
dc.identifier.lisID | 990010014160106986 | |