Financial modeling using Gaussian process models

By Dejan Petelin, Jan Sindelár, Jan Prikryl, Jus Kocijan. 2011

8 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Autoregressive Convolutional Neural Networks for Asynchronous Time Series (2017, background): “ICML, NIPS, AISTATS, UAI), one can notice that time series are often forecast using Gaussian processes (Petelin et al., 2011; Tobar et al., 2015; Hwang et al., 2016), especially when time series are irregularly sampled (Cunningham et al.”