About: Financial modeling using Gaussian process models

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Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6072854
Description	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 predict and 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, it is 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 markets. For a comparison BVAR model and benchmark model that is commonly used in todays financial mathematics are chosen. The results indicate that GP models are applicable to financial data as well as BVAR models. 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 predict and 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, it is 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 markets. For a comparison BVAR model and benchmark model that is commonly used in todays financial mathematics are chosen. The results indicate that GP models are applicable to financial data as well as BVAR models. (en)
Title	Financial modeling using Gaussian process models Financial modeling using Gaussian process models (en)
skos:prefLabel	Financial modeling using Gaussian process models Financial modeling using Gaussian process models (en)
skos:notation	RIV/68407700:21260/11:00213092!RIV14-MSM-21260___
http://linked.open...avai/riv/aktivita	I P Z
http://linked.open...avai/riv/aktivity	I, P(1M0572), P(GA102/08/0567), P(MEB091015), P(TA01030603), Z(AV0Z10750506)
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Přikryl, Jan
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta dopravní
http://linked.open...dnocenehoVysledku	199702
http://linked.open...ai/riv/idVysledku	RIV/68407700:21260/11:00213092
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	gaussian process models; autoregression; financial; efficient markets (en)
http://linked.open.../riv/klicoveSlovo	autoregression efficient markets financial gaussian process models
http://linked.open...ontrolniKodProRIV	[88E628E1019E]
http://linked.open...v/mistoKonaniAkce	Prague
http://linked.open...i/riv/mistoVydani	Prague
http://linked.open...i/riv/nazevZdroje	IDAACS'2011 - Proceedings of the 6th IEEE International Cnonference on Intelligent Data Acquisition and Advanced Computing Systems
http://linked.open...in/vavai/riv/obor	BB
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...vavai/riv/projekt	New methods for urban traffic control in congested areas System Identification Based on Gaussian Process Model for Traffic Control Applications Data, algorithms, decision making Fully probabilistic design of dynamic decision strategies
http://linked.open...UplatneniVysledku	2011
http://linked.open...iv/tvurceVysledku	Přikryl, Jan Kocijan, Juš Petelin, Dejan Šindelář, Jan
http://linked.open...vavai/riv/typAkce	EUR - Evropská
http://linked.open.../riv/zahajeniAkce	2011-09-15 (xsd:date)
http://linked.open...n/vavai/riv/zamer	Pokročilé matematické metody získávání, zpracování a využití informací a znalostí ve složitých a nedeterministických systémech
number of pages	6 (xsd:int)
http://bibframe.org/vocab/doi	10.1109/IDAACS.2011.6072854
http://purl.org/ne...btex#hasPublisher	IEEE, THEY, CTU
https://schema.org/isbn	978-1-4577-1424-5
http://localhost/t...ganizacniJednotka	21260

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