IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v12y2024i2p36-d1336227.html
   My bibliography  Save this article

Quantitative Modeling of Financial Contagion: Unraveling Market Dynamics and Bubble Detection Mechanisms

Author

Listed:
  • Ionuț Nica

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 0105552 Bucharest, Romania)

  • Ștefan Ionescu

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 0105552 Bucharest, Romania)

  • Camelia Delcea

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 0105552 Bucharest, Romania)

  • Nora Chiriță

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 0105552 Bucharest, Romania)

Abstract

This study explored the complex interplay and potential risk of financial contagion across major financial indices, focusing on the Bucharest Exchange Trading Investment Funds Index (BET-FI), along with global indices like the S&P 500, Nasdaq Composite (IXIC), and Dow Jones Industrial Average (DJIA). Our analysis covered an extensive period from 2012 to 2023, with a particular emphasis on Romania’s financial market. We employed Autoregressive Distributed Lag (ARDL) modeling to examine the interrelations among these indices, treating the BET-FI index as our primary variable. Our research also integrated Exponential Curve Fitting (EXCF) and Generalized Supremum Augmented Dickey–Fuller (GSADF) models to identify and scrutinize potential price bubbles in these indices. We analyzed moments of high volatility and deviations from typical market trends, influenced by diverse factors like government policies, presidential elections, tech sector performance, the COVID-19 pandemic, and geopolitical tensions, specifically the Russia–Ukraine conflict. The ARDL model revealed a stable long-term relationship among the variables, indicating their interconnectedness. Our study also highlights the significance of short-term market shifts leading to long-term equilibrium, as shown in the Error Correction Model (ECM). This suggests the existence of contagion effects, where small, short-term incidents can trigger long-term, domino-like impacts on the financial markets. Furthermore, our variance decomposition examined the evolving contributions of different factors over time, shedding light on their changing interactions and impact. The Cholesky factors demonstrated the interdependence between indices, essential for understanding financial contagion effects. Our research thus uncovered the nuanced dynamics of financial contagion, offering insights into market variations, the effectiveness of our models, and strategies for detecting financial bubbles. This study contributes valuable knowledge to the academic field and offers practical insights for investors in turbulent financial environments.

Suggested Citation

  • Ionuț Nica & Ștefan Ionescu & Camelia Delcea & Nora Chiriță, 2024. "Quantitative Modeling of Financial Contagion: Unraveling Market Dynamics and Bubble Detection Mechanisms," Risks, MDPI, vol. 12(2), pages 1-42, February.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:2:p:36-:d:1336227
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/12/2/36/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/12/2/36/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Javier Cabello Sánchez & Juan Antonio Fernández Torvisco & Mariano R. Arias, 2021. "TAC Method for Fitting Exponential Autoregressive Models and Others: Applications in Economy and Finance," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    2. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    3. Armenia Androniceanu & Irina Georgescu & Ionuț Nica & Nora Chiriță, 2023. "A Comprehensive Analysis of Renewable Energy Based on Integrating Economic Cybernetics and the Autoregressive Distributed Lag Model—The Case of Romania," Energies, MDPI, vol. 16(16), pages 1-28, August.
    4. Didier SORNETTE & Peter CAUWELS, 2014. "Financial Bubbles: Mechanisms and Diagnostics," Swiss Finance Institute Research Paper Series 14-28, Swiss Finance Institute.
    5. Didier Sornette & Peter Cauwels, 2014. "Financial bubbles: mechanisms and diagnostics," Papers 1404.2140, arXiv.org.
    6. Mohamed Fakhfekh & Ahmed Jeribi & Marwa Ben Salem, 2023. "Volatility dynamics of the Tunisian stock market before and during the COVID‐19 outbreak: Evidence from the GARCH family models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1653-1666, April.
    7. Bai, Lan & Wei, Yu & Wei, Guiwu & Li, Xiafei & Zhang, Songyun, 2021. "Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective," Finance Research Letters, Elsevier, vol. 40(C).
    8. Elena Valentina Țilică, 2021. "Financial Contagion Patterns in Individual Economic Sectors. The Day-of-the-Week Effect from the Polish, Russian and Romanian Markets," JRFM, MDPI, vol. 14(9), pages 1-28, September.
    9. Marwa Ben Salem & Mohamed Fakhfekh & Ahmed Jeribi, 2023. "Volatility dynamics of Tunisian stock market before and during COVID-19 outbreak and diversification benefits of Bitcoin," Afro-Asian Journal of Finance and Accounting, Inderscience Enterprises Ltd, vol. 13(5), pages 651-672.
    10. Omar F. Saqib, 2001. "The East Asian Crisis in Kindleberger-Minsky’s Framework," Brazilian Journal of Political Economy, Center of Political Economy, vol. 21(2), pages 277-285.
    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. Keller-Ressel, Martin, 2015. "Simple examples of pure-jump strict local martingales," Stochastic Processes and their Applications, Elsevier, vol. 125(11), pages 4142-4153.
    2. Zhang, Yue-Jun & Yao, Ting, 2016. "Interpreting the movement of oil prices: Driven by fundamentals or bubbles?," Economic Modelling, Elsevier, vol. 55(C), pages 226-240.
    3. Zura Kakushadze, 2016. "On Origins of Bubbles," Papers 1610.03769, arXiv.org, revised Jul 2017.
    4. Ozkan Haykir & Ibrahim Yagli, 2022. "Speculative bubbles and herding in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-33, December.
    5. von der Becke Susanne & Sornette Didier, 2019. "An Asset-Based Framework of Credit Creation (applied to the Global Financial Crisis)," Accounting, Economics, and Law: A Convivium, De Gruyter, vol. 9(2), pages 1-21, July.
    6. Geuder, Julian & Kinateder, Harald & Wagner, Niklas F., 2019. "Cryptocurrencies as financial bubbles: The case of Bitcoin," Finance Research Letters, Elsevier, vol. 31(C).
    7. Iliyasu, Jamilu & Rafindadi Sanusi, Aliyu & Suleiman, Dahiru, 2019. "Testing For Multiple Bubble Episodes In Nigerian Stock Exchange Market," Ilorin Journal of Economic Policy, Department of Economics, University of Ilorin, vol. 6(6), pages 13-26, June.
    8. Cerruti, Gianluca & Lombardini, Simone, 2022. "Financial bubbles as a recursive process lead by short-term strategies," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 555-568.
    9. Martin Keller-Ressel, 2014. "Simple examples of pure-jump strict local martingales," Papers 1405.2669, arXiv.org, revised Jun 2015.
    10. Herwartz, Helmut & Reimers, Hans-Eggert, 2006. "Modelling the Fisher hypothesis: World wide evidence," Economics Working Papers 2006-04, Christian-Albrechts-University of Kiel, Department of Economics.
    11. Bilal Mehmood & Syed Hassan Raza & Mahwish Rana & Huma Sohaib & Muhammad Azhar Khan, 2014. "Triangular Relationship between Energy Consumption, Price Index and National Income in Asian Countries: A Pooled Mean Group Approach in Presence of Structural Breaks," International Journal of Energy Economics and Policy, Econjournals, vol. 4(4), pages 610-620.
    12. Tyrväinen, Timo, 1991. "Unions, wages and employment: evidence from Finland," Bank of Finland Research Discussion Papers 16/1991, Bank of Finland.
    13. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    14. Santos, João & Domingos, Tiago & Sousa, Tânia & St. Aubyn, Miguel, 2016. "Does a small cost share reflect a negligible role for energy in economic production? Testing for aggregate production functions including capital, labor, and useful exergy through a cointegration-base," MPRA Paper 70850, University Library of Munich, Germany.
    15. Law, Siong Hook & Tan, Hui & baharumshah, ahmad, 1999. "Financial Liberalization in ASEAN and the Fisher Hypothesis," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 33, pages 65-86.
    16. Cho, Guedae & Kim, MinKyoung & Koo, Won W., 2003. "Relative Agricultural Price Changes In Different Time Horizons," 2003 Annual meeting, July 27-30, Montreal, Canada 22249, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    17. Shahbaz, Muhammad & Hoang, Thi Hong Van & Mahalik, Mantu Kumar & Roubaud, David, 2017. "Energy consumption, financial development and economic growth in India: New evidence from a nonlinear and asymmetric analysis," Energy Economics, Elsevier, vol. 63(C), pages 199-212.
    18. Levent, Korap, 2007. "Modeling purchasing power parity using co-integration: evidence from Turkey," MPRA Paper 19584, University Library of Munich, Germany.
    19. Georgios Bertsatos & Plutarchos Sakellaris & Mike G. Tsionas, 2022. "Extensions of the Pesaran, Shin and Smith (2001) bounds testing procedure," Empirical Economics, Springer, vol. 62(2), pages 605-634, February.
    20. Ranjan Aneja & Umer J. Banday & Tanzeem Hasnat & Mustafa Koçoglu, 2017. "Renewable and Non-renewable Energy Consumption and Economic Growth: Empirical Evidence from Panel Error Correction Model," Jindal Journal of Business Research, , vol. 6(1), pages 76-85, June.

    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:jrisks:v:12:y:2024:i:2:p:36-:d:1336227. 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.