Konferenční příspěvek

Petelin D., Šindelář J., Přikryl J., Kocijan J.

Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems : Technology and Application

IEEE

Piscataway

2011

978-1-4577-1424-5

Mgr. Jan Šindelář (ÚTIA - Oddělení adaptivních systémů)

gaussian process models, autoregression, financial, efficien

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.