IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2404.03319.html
   My bibliography  Save this paper

Early warning systems for financial markets of emerging economies

Author

Listed:
  • Artem Kraevskiy
  • Artem Prokhorov
  • Evgeniy Sokolovskiy

Abstract

We develop and apply a new online early warning system (EWS) for what is known in machine learning as concept drift, in economics as a regime shift and in statistics as a change point. The system goes beyond linearity assumed in many conventional methods, and is robust to heavy tails and tail-dependence in the data, making it particularly suitable for emerging markets. The key component is an effective change-point detection mechanism for conditional entropy of the data, rather than for a particular indicator of interest. Combined with recent advances in machine learning methods for high-dimensional random forests, the mechanism is capable of finding significant shifts in information transfer between interdependent time series when traditional methods fail. We explore when this happens using simulations and we provide illustrations by applying the method to Uzbekistan's commodity and equity markets as well as to Russia's equity market in 2021-2023.

Suggested Citation

  • Artem Kraevskiy & Artem Prokhorov & Evgeniy Sokolovskiy, 2024. "Early warning systems for financial markets of emerging economies," Papers 2404.03319, arXiv.org.
    • Handle: RePEc:arx:papers:2404.03319
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2404.03319
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chaudhuri, Kausik & Wu, Yangru, 2003. "Random walk versus breaking trend in stock prices: Evidence from emerging markets," Journal of Banking & Finance, Elsevier, vol. 27(4), pages 575-592, April.
    2. Smimou, K. & Khallouli, W., 2015. "Does the Euro affect the dynamic relation between stock market liquidity and the business cycle?," Emerging Markets Review, Elsevier, vol. 25(C), pages 125-153.
    3. Ahmad, Wasim & Mishra, Anil V. & Daly, Kevin J., 2018. "Financial connectedness of BRICS and global sovereign bond markets," Emerging Markets Review, Elsevier, vol. 37(C), pages 1-16.
    4. Khalfaoui, Rabeh & Hammoudeh, Shawkat & Rehman, Mohd Ziaur, 2023. "Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: Evidence from the quantile vector autoregression network," Emerging Markets Review, Elsevier, vol. 54(C).
    5. Chen, Zhimin & Ibragimov, Rustam, 2019. "One country, two systems? The heavy-tailedness of Chinese A- and H- share markets," Emerging Markets Review, Elsevier, vol. 38(C), pages 115-141.
    6. Zhao, Hong & Li, Jiayi & Lei, Yiqing & Zhou, Mingming, 2022. "Risk spillover of banking across regions: Evidence from the belt and road countries," Emerging Markets Review, Elsevier, vol. 52(C).
    7. Neaime, Simon, 2016. "Financial crises and contagion vulnerability of MENA stock markets," Emerging Markets Review, Elsevier, vol. 27(C), pages 14-35.
    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. Su, Xianfang & Chen, Meixia, 2024. "Financial connectedness in BRICS: Quantile effects and BRICS SUMMIT impacts," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    2. Abduraimova, Kumushoy, 2022. "Contagion and tail risk in complex financial networks," Journal of Banking & Finance, Elsevier, vol. 143(C).
    3. Baoqiang Zhan & Shu Zhang & Helen S. Du & Xiaoguang Yang, 2022. "Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 861-882, October.
    4. Kanak Patel & Ricardo Pereira & Kirill Zavodov, 2009. "Mean-Reversion in REITs Discount to NAV & Risk Premium," The Journal of Real Estate Finance and Economics, Springer, vol. 39(3), pages 229-247, October.
    5. Shyh-Wei Chen, 2008. "Non-stationarity and Non-linearity in Stock Prices: Evidence from the OECD Countries," Economics Bulletin, AccessEcon, vol. 3(11), pages 1-11.
    6. Christian Urom & Gideon Ndubuisi & Jude Ozor, 2021. "Economic activity, and financial and commodity markets’ shocks: An analysis of implied volatility indexes," International Economics, CEPII research center, issue 165, pages 51-66.
    7. Costola, Michele & Lorusso, Marco, 2022. "Spillovers among energy commodities and the Russian stock market," Journal of Commodity Markets, Elsevier, vol. 28(C).
    8. João Frois Caldeira & Rangan Gupta & Muhammad Tahir Suleman & Hudson S. Torrent, 2021. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(15), pages 4312-4329, December.
    9. Noureddine Benlagha & Wael Hemrit, 2022. "Does economic policy uncertainty matter to explain connectedness within the international sovereign bond yields?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(1), pages 1-21, January.
    10. Chancharat,Surachai & Valadkhani, Abbas, 2007. "Testing for the Random Walk Hypothesis and Structural Breaks in International Stock Prices," Economics Working Papers wp07-15, School of Economics, University of Wollongong, NSW, Australia.
    11. Emmanouil Mavrakis & Christos Alexakis, 2018. "Statistical Arbitrage Strategies under Different Market Conditions: The Case of the Greek Banking Sector," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 17(2), pages 159-185, August.
    12. Paresh Narayan & Arti Prasad, 2007. "Mean Reversion in Stock Prices: New Evidence from Panel Unit Root Tests for Seventeen European Countries," Economics Bulletin, AccessEcon, vol. 3(34), pages 1-6.
    13. Wan, Yang & He, Shi, 2021. "Dynamic connectedness of currencies in G7 countries: A Bayesian time-varying approach," Finance Research Letters, Elsevier, vol. 41(C).
    14. Mahmoud Arayssi & Ali Fakih & Nathir Haimoun, 2019. "Did the Arab Spring reduce MENA countries’ growth?," Applied Economics Letters, Taylor & Francis Journals, vol. 26(19), pages 1579-1585, November.
    15. Mehmet Balcilar & Zeynel Abidin Ozdemir & Esin Cakan, 2015. "Structural Breaks, Long Memory, or Unit Roots in Stock Prices: Evidence from Emerging Markets," International Econometric Review (IER), Econometric Research Association, vol. 7(1), pages 13-33, April.
    16. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2014. "Multivariate Variance Ratio Statistics," Cambridge Working Papers in Economics 1459, Faculty of Economics, University of Cambridge.
    17. Ferry Syarifuddin, 2022. "Is Islamic Banks Better Than Conventional Banks In The Time Of Uncertainty?," Working Papers WP/06/2022, Bank Indonesia.
    18. Ahmed A. Elamer & Collins G. Ntim & Hussein A. Abdou & Alaa Mansour Zalata & Mohamed Elmagrhi, 2019. "The impact of multi-layer governance on bank risk disclosure in emerging markets: the case of Middle East and North Africa," Accounting Forum, Taylor & Francis Journals, vol. 43(2), pages 246-281, April.
    19. Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 245-264, October.
    20. Guo, Xu & McAleer, Michael & Wong, Wing-Keung & Zhu, Lixing, 2017. "A Bayesian approach to excess volatility, short-term underreaction and long-term overreaction during financial crises," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 346-358.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2404.03319. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.