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Statistical Analysis of Current Financial Instrument Quotes in the Conditions of Market Chaos

Author

Listed:
  • Alexander Musaev

    (Saint-Petersburg State Institute of Technology, Technical University, St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 190013 St. Petersburg, Russia)

  • Andrey Makshanov

    (Department of Computing Systems and Computer Science, Admiral Makarov State University of Maritime and Inland Shipping, 198035 St. Petersburg, Russia)

  • Dmitry Grigoriev

    (Center of Econometrics and Business Analytics (CEBA), St. Petersburg State University, 199034 St. Petersburg, Russia)

Abstract

In this paper, the problem of estimating the current value of financial instruments using multidimensional statistical analysis is considered. The research considers various approaches to constructing regression computational schemes using quotes of financial instruments correlated to the data as regressors. An essential feature of the problem is the chaotic nature of its observation series, which is due to the instability of the probabilistic structure of the initial data. These conditions invalidate the constraints under which traditional statistical estimates remain non-biased and effective. Violation of experiment repeatability requirements obstructs the use of the conventional data averaging approach. In this case, numeric experiments become the main method for investigating the efficiency of forecasting and analysis algorithms of observation series. The empirical approach does not provide guaranteed results. However, it can be used to build sufficiently effective rational strategies for managing trading operations.

Suggested Citation

  • Alexander Musaev & Andrey Makshanov & Dmitry Grigoriev, 2022. "Statistical Analysis of Current Financial Instrument Quotes in the Conditions of Market Chaos," Mathematics, MDPI, vol. 10(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:587-:d:749349
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    References listed on IDEAS

    as
    1. Xing, Dun-Zhong & Li, Hai-Feng & Li, Jiang-Cheng & Long, Chao, 2021. "Forecasting price of financial market crash via a new nonlinear potential GARCH model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    2. Wa̧torek, Marcin & Drożdż, Stanisław & Oświȩcimka, Paweł & Stanuszek, Marek, 2019. "Multifractal cross-correlations between the world oil and other financial markets in 2012–2017," Energy Economics, Elsevier, vol. 81(C), pages 874-885.
    3. Markus Holopainen & Peter Sarlin, 2017. "Toward robust early-warning models: a horse race, ensembles and model uncertainty," Quantitative Finance, Taylor & Francis Journals, vol. 17(12), pages 1933-1963, December.
    4. Gilles Zumbach, 2021. "On the short term stability of financial ARCH price processes," Papers 2107.06758, arXiv.org.
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    Cited by:

    1. Eva Kaslik & Mihaela Neamţu & Anca Rădulescu, 2022. "Preface to the Special Issue on “Advances in Differential Dynamical Systems with Applications to Economics and Biology”," Mathematics, MDPI, vol. 10(19), pages 1-3, September.
    2. Alexander Musaev & Dmitry Grigoriev, 2025. "The Stability of Trend Management Strategies in Chaotic Market Conditions," JRFM, MDPI, vol. 18(1), pages 1-21, January.
    3. Alexander Musaev & Andrey Makshanov & Dmitry Grigoriev, 2024. "Multi-regression Forecast in Stochastic Chaos," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 137-160, July.

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