IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i4p587-d749349.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/4/587/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/4/587/
    Download Restriction: no
    ---><---

    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. Gilles Zumbach, 2021. "On the short term stability of financial ARCH price processes," Papers 2107.06758, arXiv.org.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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 & 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nikolay Hristov & Markus Roth, 2019. "Uncertainty Shocks and Financial Crisis Indicators," CESifo Working Paper Series 7839, CESifo.
    2. Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Marcin Wk{a}torek, 2023. "What is mature and what is still emerging in the cryptocurrency market?," Papers 2305.05751, arXiv.org.
    3. Mr. Jorge A Chan-Lau, 2020. "UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification," IMF Working Papers 2020/262, International Monetary Fund.
    4. repec:zbw:bofrdp:2019_014 is not listed on IDEAS
    5. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    6. Aslam, Faheem & Zil-e-huma, & Bibi, Rashida & Ferreira, Paulo, 2022. "Cross-correlations between economic policy uncertainty and precious and industrial metals: A multifractal cross-correlation analysis," Resources Policy, Elsevier, vol. 75(C).
    7. Barbara Jarmulska, 2022. "Random forest versus logit models: Which offers better early warning of fiscal stress?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 455-490, April.
    8. Solomon Y. Deku & Alper Kara & Artur Semeyutin, 2021. "The predictive strength of MBS yield spreads during asset bubbles," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 111-142, January.
    9. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2018. "An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions?," Discussion Papers 48/2018, Deutsche Bundesbank.
    10. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek, 2020. "Multiscale characteristics of the emerging global cryptocurrency market," Papers 2010.15403, arXiv.org, revised Mar 2021.
    11. Lu, Linna & Lei, Yalin & Yang, Yang & Zheng, Haoqi & Wang, Wen & Meng, Yan & Meng, Chunhong & Zha, Liqiang, 2023. "Assessing nickel sector index volatility based on quantile regression for Garch and Egarch models: Evidence from the Chinese stock market 2018–2022," Resources Policy, Elsevier, vol. 82(C).
    12. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    13. Seulki Chung, 2023. "Inside the black box: Neural network-based real-time prediction of US recessions," Papers 2310.17571, arXiv.org, revised May 2024.
    14. Kwon, Yujin & Park, Sung Y., 2023. "Modeling an early warning system for household debt risk in Korea: A simple deep learning approach," Journal of Asian Economics, Elsevier, vol. 84(C).
    15. Hyeongwoo Kim & Wen Shi, 2021. "Forecasting financial vulnerability in the USA: A factor model approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 439-457, April.
    16. Kurowski, Łukasz & Smaga, Paweł, 2023. "Analysing financial stability reports as crisis predictors with the use of text-mining," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    17. Li, Shuping & Li, Jianfeng & Lu, Xinsheng & Sun, Yihong, 2022. "Exploring the dynamic nonlinear relationship between crude oil price and implied volatility indices: A new perspective from MMV-MFDFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    18. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    19. Espinosa-Paredes, G. & Rodriguez, E. & Alvarez-Ramirez, J., 2022. "A singular value decomposition entropy approach to assess the impact of Covid-19 on the informational efficiency of the WTI crude oil market," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    20. Mr. Plamen K Iossifov, 2021. "Cyclical Patterns of Systemic Risk Metrics: Cross-Country Analysis," IMF Working Papers 2021/028, International Monetary Fund.
    21. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:587-:d:749349. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.