IDEAS home Printed from https://ideas.repec.org/a/nea/journl/y2021i50p12-34.html
   My bibliography  Save this article

Confidence set for connected stocks of stock market

Author

Listed:
  • Koldanov, A.

    (National Research University Higher School of Economics, Nizhnii Novgorod, Russia)

  • Koldanov, P.

    (National Research University Higher School of Economics, Nizhnii Novgorod, Russia)

  • Semenov, D.

    (National Research University Higher School of Economics, Nizhnii Novgorod, Russia)

Abstract

The problem of analysis of pairwise connections between stocks of financial market by observations on stock returns is considered. Such problem arise in stock market network analysis. It is assumed that joint distribution of stock returns belongs to the wide class of elliptical distributions. Classical Pearson correlation, Fechner correlation and Kendall correlation are used as measure of dependence. The construction problems of sets of stocks with strong connections between its returns are investigated. The construction problems of sets of stocks with strong connections between its returns are investigated. To construct such sets the multiple hypotheses testing procedures on values of correlations are used. The properties of these statistical procedures are investigated by simulations. The simulation results show that procedures based on individual Fechner and Kendall tests lead to such sets of stocks with given confidence probability unlike procedure based on Pearson individual tests which do not control the confidence probability. At the same time it is emphasized that for Student distribution the constructed set is nearly the same to the confidence set. The procedure of consistency testing with elliptical model is proposed and exemplified. The peculiarities of the model are discussed.

Suggested Citation

  • Koldanov, A. & Koldanov, P. & Semenov, D., 2021. "Confidence set for connected stocks of stock market," Journal of the New Economic Association, New Economic Association, vol. 50(2), pages 12-34.
    • Handle: RePEc:nea:journl:y:2021:i:50:p:12-34
    DOI: 10.31737/2221-2264-2021-50-2-1
    as

    Download full text from publisher

    File URL: http://www.econorus.org/repec/journl/2021-50-12-34r.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.31737/2221-2264-2021-50-2-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Vizgunov, A. & Goldengorin, B. & Zamaraev, V. & Kalyagin, V. & Koldanov, A. & Koldanov, P. & Pardalos, P., 2012. "Applying Market Graphs for Russian Stock Market Analysis," Journal of the New Economic Association, New Economic Association, vol. 15(3), pages 66-81.
    2. Kalyagin, V. & Koldanov, A. & Koldanov, P. & Pardalos, P., 2017. "Statistical Procedures for Stock Markets Network Structures Identification," Journal of the New Economic Association, New Economic Association, vol. 35(3), pages 33-52.
    3. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    4. Petr Koldanov & Nina Lozgacheva, 2016. "Multiple Testing Of Sign Symmetry For Stock Return Distributions," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(08), pages 1-14, December.
    5. Namaki, A. & Shirazi, A.H. & Raei, R. & Jafari, G.R., 2011. "Network analysis of a financial market based on genuine correlation and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3835-3841.
    6. Petr Koldanov, 2019. "Testing new property of elliptical model for stock returns distribution," Papers 1907.10306, arXiv.org.
    7. Boginski, Vladimir & Butenko, Sergiy & Pardalos, Panos M., 2005. "Statistical analysis of financial networks," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 431-443, February.
    8. Rémy Chicheportiche & Jean-Philippe Bouchaud, 2012. "The Joint Distribution Of Stock Returns Is Not Elliptical," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 1-23.
    9. Garas, Antonios & Argyrakis, Panos, 2007. "Correlation study of the Athens Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 399-410.
    10. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang, 2009. "A network analysis of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2956-2964.
    11. Rémy Chicheportiche & Jean-Philippe Bouchaud, 2012. "The joint distribution of stock returns is not elliptical," Post-Print hal-00703720, HAL.
    12. Oleg Shirokikh & Grigory Pastukhov & Vladimir Boginski & Sergiy Butenko, 2013. "Computational study of the US stock market evolution: a rank correlation-based network model," Computational Management Science, Springer, vol. 10(2), pages 81-103, June.
    13. R'emy Chicheportiche & Jean-Philippe Bouchaud, 2010. "The joint distribution of stock returns is not elliptical," Papers 1009.1100, arXiv.org, revised Jun 2012.
    14. V. A. Kalyagin & A. P. Koldanov & P. A. Koldanov & P. M. Pardalos, 2018. "Optimal decision for the market graph identification problem in a sign similarity network," Annals of Operations Research, Springer, vol. 266(1), pages 313-327, July.
    Full references (including those not matched with items on IDEAS)

    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. V. A. Kalyagin & A. P. Koldanov & P. A. Koldanov & P. M. Pardalos, 2018. "Optimal decision for the market graph identification problem in a sign similarity network," Annals of Operations Research, Springer, vol. 266(1), pages 313-327, July.
    2. V. A. Kalyagin & P. A. Koldanov & P. M. Pardalos, 2015. "Optimal decision for the market graph identification problem in sign similarity network," Papers 1512.06449, arXiv.org.
    3. Nie, Chun-Xiao, 2017. "Correlation dimension of financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 632-639.
    4. Seyed Soheil Hosseini & Nick Wormald & Tianhai Tian, 2019. "A Weight-based Information Filtration Algorithm for Stock-Correlation Networks," Papers 1904.06007, arXiv.org.
    5. Nie, Chun-Xiao & Song, Fu-Tie, 2018. "Constructing financial network based on PMFG and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 104-113.
    6. Hosseini, Seyed Soheil & Wormald, Nick & Tian, Tianhai, 2021. "A Weight-based Information Filtration Algorithm for Stock-correlation Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    7. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    8. Wang, Gang-Jin & Xie, Chi & Han, Feng & Sun, Bo, 2012. "Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4136-4146.
    9. Kalyagin, V. & Koldanov, A. & Koldanov, P. & Pardalos, P., 2017. "Statistical Procedures for Stock Markets Network Structures Identification," Journal of the New Economic Association, New Economic Association, vol. 35(3), pages 33-52.
    10. Nie, Chun-Xiao & Song, Fu-Tie, 2018. "Analyzing the stock market based on the structure of kNN network," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 148-159.
    11. Kartikay Gupta & Niladri Chatterjee, 2020. "Examining Lead-Lag Relationships In-Depth, With Focus On FX Market As Covid-19 Crises Unfolds," Papers 2004.10560, arXiv.org, revised May 2020.
    12. Peralta, Gustavo & Zareei, Abalfazl, 2016. "A network approach to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 157-180.
    13. Jonathan Raimana Chan & Thomas Huckle & Antoine Jacquier & Aitor Muguruza, 2021. "Portfolio optimisation with options," Papers 2111.12658, arXiv.org, revised Sep 2024.
    14. Neto, José de Paula Neves & Figueiredo, Daniel Ratton, 2023. "Ranking influential and influenced stocks over time using transfer entropy networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    15. Fung, Thomas & Seneta, Eugene, 2014. "Convergence rate to a lower tail dependence coefficient of a skew-t distribution," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 62-72.
    16. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    17. Gustavo Peralta, 2015. "Network-based Measures as Leading Indicators of Market Instability: The case of the Spanish Stock," CNMV Working Papers CNMV Working Papers no 59, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
    18. Li, Jianxuan & Shi, Yingying & Cao, Guangxi, 2018. "Topology structure based on detrended cross-correlation coefficient of exchange rate network of the belt and road countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1140-1151.
    19. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang & Uryasev, Stan, 2016. "A financial network perspective of financial institutions’ systemic risk contributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 183-196.
    20. Yong Tang & Jason Jie Xiong & Zi-Yang Jia & Yi-Cheng Zhang, 2018. "Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets," Complexity, Hindawi, vol. 2018, pages 1-31, November.

    More about this item

    Keywords

    Network model of stock market; threshold graph; Pearson correlation; Kendall correlation; Fechner correlation; sufficient set; multiple hypotheses testing procedures;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics

    Statistics

    Access and download statistics

    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:nea:journl:y:2021:i:50:p:12-34. 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: Alexey Tcharykov (email available below). General contact details of provider: https://edirc.repec.org/data/nearuea.html .

    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.