IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v74y2025ics1544612324017628.html
   My bibliography  Save this article

Rising bubbles by margin calls

Author

Listed:
  • Alaminos, David

Abstract

This paper examines price bubble formation in commodity markets driven by margin calls, highlighting mechanisms causing extreme price volatility. Analyzing Nickel, WTI Oil, Silver, Copper, Wheat, Corn, and Soybean, I test five hypotheses on leverage, liquidity reduction, and positive feedback loops using advanced detection methods like LPPLS and GSADF. Results show high leverage and margin calls amplify volatility through forced trades and speculation. Asymmetrical reactions and herding behavior further exacerbate bubbles, particularly under supply constraints. My findings stress the need for improved risk management and regulatory measures to curb leverage-driven volatility, enhancing market stability and resilience.

Suggested Citation

  • Alaminos, David, 2025. "Rising bubbles by margin calls," Finance Research Letters, Elsevier, vol. 74(C).
  • Handle: RePEc:eee:finlet:v:74:y:2025:i:c:s1544612324017628
    DOI: 10.1016/j.frl.2024.106733
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612324017628
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2024.106733?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(4), pages 1043-1078, November.
    2. Jarrow, Robert & Lamichhane, Sujan, 2022. "Risk premia, asset price bubbles, and monetary policy," Journal of Financial Stability, Elsevier, vol. 60(C).
    3. D. Sornette & A. Johansen, 2001. "Significance of log-periodic precursors to financial crashes," Quantitative Finance, Taylor & Francis Journals, vol. 1(4), pages 452-471.
    4. Xiaohang Ren & Wenting Jiang & Qiang Ji & Pengxiang Zhai, 2024. "Seeing is believing: Forecasting crude oil price trend from the perspective of images," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2809-2821, November.
    5. Zachary Feinstein, 2021. "Clearing prices under margin calls and the short squeeze," Papers 2102.02176, arXiv.org, revised Apr 2022.
    6. Caginalp, Carey & Caginalp, Gunduz, 2019. "Stochastic asset price dynamics and volatility using a symmetric supply and demand price equation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 807-824.
    7. Gao, Ya-Chun & Tang, Huai-Lin & Cai, Shi-Min & Gao, Jing-Jing & Stanley, H. Eugene, 2018. "The impact of margin trading on share price evolution: A cascading failure model investigation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 69-76.
    8. Yajun Wang, 2016. "Why Can Margin Requirements Increase Volatility and Benefit Margin Constrained Investors?," Review of Finance, European Finance Association, vol. 20(4), pages 1449-1485.
    9. D. Sornette & A. Johansen, 2001. "Significance of log-periodic precursors to financial crashes," Papers cond-mat/0106520, arXiv.org.
    10. Yintian Wang & Guofu Zhou & Yingzi Zhu, 2021. "The Chinese warrant bubble: A fundamental analysis," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 3-26, January.
    11. Brunnermeier, Markus K. & Oehmke, Martin, 2013. "Bubbles, Financial Crises, and Systemic Risk," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1221-1288, Elsevier.
    12. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 1043-1078, November.
    13. Jian Li & Jean‐Paul Chavas, 2023. "A dynamic analysis of the distribution of commodity futures and spot prices," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(1), pages 122-143, January.
    14. Ya-Chun Gao & Huai-Lin Tang & Shi-Min Cai & Jing-Jing Gao & H. Eugene Stanley, 2018. "The impact of margin trading on share price evolution: A cascading failure model investigation," Papers 1804.07352, arXiv.org.
    15. Gary Gorton & K. Geert Rouwenhorst, 2006. "Facts and Fantasies about Commodity Futures," Financial Analysts Journal, Taylor & Francis Journals, vol. 62(2), pages 47-68, March.
    16. Zhang, Xiaoming & Zhu, Mengqing & Tian, Yiming & Zedda, Stefano, 2024. "Detecting house price bubbles in G7 countries: New evidence and heterogeneous determinants," Finance Research Letters, Elsevier, vol. 69(PA).
    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. Adrian Fernández-Pérez & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2025. "El Clasico of Housing: Bubbles in Madrid and Barcelona’s Real Estate Markets," Documentos de Trabajo del ICAE 2025-03, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    2. Hirano, Tomohiro & Toda, Alexis Akira, 2024. "Bubble economics," Journal of Mathematical Economics, Elsevier, vol. 111(C).
    3. Christian Kubitza, 2021. "Tackling the Volatility Paradox: Spillover Persistence and Systemic Risk," ECONtribute Discussion Papers Series 079, University of Bonn and University of Cologne, Germany.
    4. V. Filimonov & G. Demos & D. Sornette, 2017. "Modified profile likelihood inference and interval forecast of the burst of financial bubbles," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1167-1186, August.
    5. Cheng, Fangzheng & Fan, Tijun & Fan, Dandan & Li, Shanling, 2018. "The prediction of oil price turning points with log-periodic power law and multi-population genetic algorithm," Energy Economics, Elsevier, vol. 72(C), pages 341-355.
    6. Riza Demirer & Guilherme Demos & Rangan Gupta & Didier Sornette, 2019. "On the predictability of stock market bubbles: evidence from LPPLS confidence multi-scale indicators," Quantitative Finance, Taylor & Francis Journals, vol. 19(5), pages 843-858, May.
    7. José Gabriel Astaíza-Gómez, 2025. "Uncertainty, Risk, and Opaque Stock Markets," IJFS, MDPI, vol. 13(1), pages 1-32, March.
    8. Yao, Can-Zhong & Li, Hong-Yu, 2021. "A study on the bursting point of Bitcoin based on the BSADF and LPPLS methods," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    9. Fan, John Hua & Fernandez-Perez, Adrian & Indriawan, Ivan & Todorova, Neda, 2024. "When Chinese mania meets global frenzy: Commodity price bubbles," Journal of Commodity Markets, Elsevier, vol. 36(C).
    10. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    11. Blot, Christophe & Hubert, Paul & Labondance, Fabien, 2024. "The asymmetric effects of monetary policy on stock price bubbles," European Economic Review, Elsevier, vol. 168(C).
    12. Zhang, Qunzhi & Sornette, Didier & Balcilar, Mehmet & Gupta, Rangan & Ozdemir, Zeynel Abidin & Yetkiner, Hakan, 2016. "LPPLS bubble indicators over two centuries of the S&P 500 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 126-139.
    13. Zhang, Xiaoming & Wei, Chunyan & Lee, Chien-Chiang & Tian, Yiming, 2023. "Systemic risk of Chinese financial institutions and asset price bubbles," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    14. Dean Fantazzini & Erik Nigmatullin & Vera Sukhanovskaya & Sergey Ivliev, 2017. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. Part 2," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 45, pages 5-28.
    15. Wang, Xiao-Qing & Wu, Tong & Zhong, Huaming & Su, Chi-Wei, 2023. "Bubble behaviors in nickel price: What roles do geopolitical risk and speculation play?," Resources Policy, Elsevier, vol. 83(C).
    16. Francisco Blasques & Siem Jan Koopman & Gabriele Mingoli, 2023. "Observation-Driven filters for Time- Series with Stochastic Trends and Mixed Causal Non-Causal Dynamics," Tinbergen Institute Discussion Papers 23-065/III, Tinbergen Institute, revised 01 Mar 2024.
    17. Hanna Halaburda & Guillaume Haeringer & Joshua Gans & Neil Gandal, 2022. "The Microeconomics of Cryptocurrencies," Journal of Economic Literature, American Economic Association, vol. 60(3), pages 971-1013, September.
    18. Bielskis, Karolis & Lastauskas, Povilas, 2024. "The role of housing market and credit on household consumption dynamics: Evidence from the OECD countries," Journal of Economic Behavior & Organization, Elsevier, vol. 228(C).
    19. Horváth, Lajos & Li, Hemei & Liu, Zhenya, 2022. "How to identify the different phases of stock market bubbles statistically?," Finance Research Letters, Elsevier, vol. 46(PA).
    20. Liu, Guangqiang & Zeng, Qing & Lei, Juan, 2022. "Dynamic risks from climate policy uncertainty: A case study for the natural gas market," Resources Policy, Elsevier, vol. 79(C).

    More about this item

    Keywords

    Bubbles; Crashes; Margin calls; Commodities; Speculation; GSADF; LPPLS;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G01 - Financial Economics - - General - - - Financial Crises
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

    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:eee:finlet:v:74:y:2025:i:c:s1544612324017628. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.