Markov Chains application to the financial-economic time series prediction

Vladimir Soloviev, Vladimir Saptsin and Dmitry Chabanenko

Papers from arXiv.org

Abstract: In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of aftereffect or memory. The technology proposes prediction with the hierarchy of time discretization intervals and splicing procedure for the prediction results at the different frequency levels to the single prediction output time series. The hierarchy of time discretizations gives a possibility to use fractal properties of the given time series to make prediction on the different frequencies of the series. The prediction results for world's stock market indices is presented.

Date: 2011-11
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1111.5254

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