Financial modeling using Gaussian process models
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F11%3A00366040" target="_blank" >RIV/67985556:_____/11:00366040 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/68407700:21260/11:00213092
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Financial modeling using Gaussian process models
Popis výsledku v původním jazyce
In the 1960s E. Fama developed the efficient market hypothesis (EMH) which asserts that the financial market is efficient if its prices are formed on the basis of all publicly available information. That means technical analysis cannot be used to predictand beat the market. Since then, it was widely examined and was mostly accepted by mathematicians and financial engineers. However, the predictability of financial-market returns remains an open problem and is discussed in many publications. Usually, itis concluded that a model able to predict financial returns should adapt to market changes quickly and catch local dependencies in price movements. The Bayesian vector autoregression (BVAR) models, support vector machines (SVM) and some other were already applied to financial data quite succesfully. Gaussian process (GP) models are emerging non-parametric Bayesian models and in this paper we test their applicability to financial data. GP model is fitted to daily data from U.S. commodity
Název v anglickém jazyce
Financial modeling using Gaussian process models
Popis výsledku anglicky
In the 1960s E. Fama developed the efficient market hypothesis (EMH) which asserts that the financial market is efficient if its prices are formed on the basis of all publicly available information. That means technical analysis cannot be used to predictand beat the market. Since then, it was widely examined and was mostly accepted by mathematicians and financial engineers. However, the predictability of financial-market returns remains an open problem and is discussed in many publications. Usually, itis concluded that a model able to predict financial returns should adapt to market changes quickly and catch local dependencies in price movements. The Bayesian vector autoregression (BVAR) models, support vector machines (SVM) and some other were already applied to financial data quite succesfully. Gaussian process (GP) models are emerging non-parametric Bayesian models and in this paper we test their applicability to financial data. GP model is fitted to daily data from U.S. commodity
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
BB - Aplikovaná statistika, operační výzkum
OECD FORD obor
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Návaznosti výsledku
Projekt
Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.
Návaznosti
Z - Vyzkumny zamer (s odkazem do CEZ)
Ostatní
Rok uplatnění
2011
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems : Technology and Application
ISBN
978-1-4577-1424-5
ISSN
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e-ISSN
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Počet stran výsledku
6
Strana od-do
672-677
Název nakladatele
IEEE
Místo vydání
Piscataway
Místo konání akce
Prague
Datum konání akce
15. 9. 2011
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
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