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Markov Chains application to the financial-economic time series prediction

Author

Listed:
  • Vladimir Soloviev
  • Vladimir Saptsin
  • Dmitry Chabanenko

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.

Suggested Citation

  • Vladimir Soloviev & Vladimir Saptsin & Dmitry Chabanenko, 2011. "Markov Chains application to the financial-economic time series prediction," Papers 1111.5254, arXiv.org.
    • Handle: RePEc:arx:papers:1111.5254
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    Cited by:

    1. Stepchenko Arthur & Chizhov Jurij, 2015. "Applying Markov Chains for NDVI Time Series Forecasting of Latvian Regions," Information Technology and Management Science, Sciendo, vol. 18(1), pages 57-61, December.
    2. Olawale Awe O. & Adedayo Adepoju A., 2018. "Modified Recursive Bayesian Algorithm For Estimating Time-Varying Parameters In Dynamic Linear Models," Statistics in Transition New Series, Statistics Poland, vol. 19(2), pages 258-293, June.
    3. Kerolly Kedma Felix do Nascimento & Fábio Sandro dos Santos & Jader Silva Jale & Silvio Fernando Alves Xavier Júnior & Tiago A. E. Ferreira, 2023. "Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1095-1114, March.
    4. Alina-Petronela Haller & Ovidiu Gherasim & Mariana B?lan & Carmen Uzl?u, 2020. "Medium-term forecast of European economic sustainable growth using Markov chains," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 38(2), pages 585-618.
    5. O. Olawale Awe & A. Adedayo Adepoju, 2018. "Modified Recursive Bayesian Algorithm For Estimating Time-Varying Parameters In Dynamic Linear Models," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 239-258, June.
    6. Wu, Sheng-Jhih & Chu, Moody T., 2017. "Markov chains with memory, tensor formulation, and the dynamics of power iteration," Applied Mathematics and Computation, Elsevier, vol. 303(C), pages 226-239.

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