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Financial modeling using Gaussian process models
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SYSNO ASEP 0366040 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Financial modeling using Gaussian process models Author(s) Petelin, D. (SI)
Šindelář, Jan (UTIA-B)
Přikryl, Jan (UTIA-B) RID
Kocijan, J. (SI)Number of authors 4 Source Title Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems : Technology and Application. - Piscataway : IEEE, 2011 - ISBN 978-1-4577-1424-5 Pages s. 672-677 Number of pages 6 s. Action 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications Event date 15.09.2011-17.09.2011 VEvent location Prague Country CZ - Czech Republic Event type WRD Language eng - English Country US - United States Keywords gaussian process models ; autoregression ; financial ; efficient markets Subject RIV BB - Applied Statistics, Operational Research R&D Projects 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) TA01030603 GA TA ČR - Technology Agency of the Czech Republic (TA ČR) GA102/08/0567 GA ČR - Czech Science Foundation (CSF) MEB091015 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
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