IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v503y2018icp947-957.html
   My bibliography  Save this article

The bubble and anti-bubble risk resistance analysis on the metal futures in China

Author

Listed:
  • Zhou, Wei
  • Huang, Yang
  • Chen, Jin

Abstract

This paper investigates the risk resistance ability of China’s metal futures before and after the financial crises in 2008 and 2015. The log periodic power law singularity (LPPLS) model is utilized to measure the financial bubble and anti-bubble during these periods. In addition, the risk resistance ability is quantitatively measured by the two derived parameters in the model. They control the super-exponential growth of price and describe the log-periodic acceleration of volatility, respectively. Further, we select the Shanghai Zinc (SZ) futures and the Shanghai Aluminum (SA) futures in Shanghai Futures Exchange as the objectives to empirically study their risk resistance ability during the above mentioned periods. Then, some interesting findings are concluded as follows: (1) For the China’s metal futures, the bubble risks last longer than the anti-bubble risks before and after the two financial crises. (2) The China’s metal futures have risk resistance ability in anti-bubble, but the ability is relatively weaker in bubble before and after the two financial crises. (3) The LPPLS model can efficiently predict and measure the bubble and anti-bubble of the financial products. It also gives us an access to observe the risk resistance ability of financial products by the derived parameters.

Suggested Citation

  • Zhou, Wei & Huang, Yang & Chen, Jin, 2018. "The bubble and anti-bubble risk resistance analysis on the metal futures in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 947-957.
    • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:947-957
    DOI: 10.1016/j.physa.2018.08.120
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711831063X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.08.120?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. Wosnitza, Jan Henrik & Leker, Jens, 2014. "Can log-periodic power law structures arise from random fluctuations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 228-250.
    2. Fantazzini, Dean, 2016. "The oil price crash in 2014/15: Was there a (negative) financial bubble?," Energy Policy, Elsevier, vol. 96(C), pages 383-396.
    3. 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.
    4. Graf v. Bothmer, Hans-Christian & Meister, Christian, 2003. "Predicting critical crashes? A new restriction for the free variables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 320(C), pages 539-547.
    5. Zhou, Wei-Xing & Sornette, Didier, 2009. "A case study of speculative financial bubbles in the South African stock market 2003–2006," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(6), pages 869-880.
    6. Zhou, Wei-Xing & Sornette, Didier, 2005. "Testing the stability of the 2000 US stock market “antibubble”," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 428-452.
    7. Dimitrios Dimitriou & Theodore Simos, 2013. "Contagion channels of the USA subprime financial crisis," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 5(1), pages 61-71, April.
    8. Algieri, Bernardina & Leccadito, Arturo, 2017. "Assessing contagion risk from energy and non-energy commodity markets," Energy Economics, Elsevier, vol. 62(C), pages 312-322.
    9. Zhou, Wei-Xing & Sornette, Didier, 2006. "Fundamental factors versus herding in the 2000–2005 US stock market and prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(2), pages 459-482.
    10. Branger, Nicole & Kraft, Holger & Meinerding, Christoph, 2009. "What is the impact of stock market contagion on an investor's portfolio choice?," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 94-112, August.
    11. Zhou, Wei-Xing & Sornette, Didier, 2003. "Evidence of a worldwide stock market log-periodic anti-bubble since mid-2000," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 330(3), pages 543-583.
    12. Zhou, Wei-Xing & Sornette, Didier, 2004. "Antibubble and prediction of China's stock market and real-estate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(1), pages 243-268.
    13. A. Johansen & D. Sornette, 1999. "Financial ``Anti-Bubbles'': Log-Periodicity in Gold and Nikkei collapses," Papers cond-mat/9901268, arXiv.org.
    14. Anders Johansen, 2004. "Origin of Crashes in 3 US stock markets: Shocks and Bubbles," Papers cond-mat/0401210, arXiv.org.
    15. Zhou, Wei-Xing & Sornette, Didier, 2004. "Causal slaving of the US treasury bond yield antibubble by the stock market antibubble of August 2000," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(3), pages 586-608.
    16. De Santis, Giorgio & Gerard, Bruno, 1997. "International Asset Pricing and Portfolio Diversification with Time-Varying Risk," Journal of Finance, American Finance Association, vol. 52(5), pages 1881-1912, December.
    17. G. Demos & D. Sornette, 2017. "Birth or burst of financial bubbles: which one is easier to diagnose?," Quantitative Finance, Taylor & Francis Journals, vol. 17(5), pages 657-675, May.
    18. Brée, David S. & Joseph, Nathan Lael, 2013. "Testing for financial crashes using the Log Periodic Power Law model," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 287-297.
    19. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Pawe{l} O'swik{e}cimka, 2016. "World Financial 2014-2016 Market Bubbles: Oil Negative - US Dollar Positive," Papers 1606.01218, arXiv.org.
    20. Anders Johansen & Olivier Ledoit & Didier Sornette, 2000. "Crashes As Critical Points," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 219-255.
    21. Wanfeng Yan & Ryan Woodard & Didier Sornette, 2010. "Diagnosis and Prediction of Tipping Points in Financial Markets: Crashes and Rebounds," Papers 1001.0265, arXiv.org, revised Feb 2010.
    22. Piccotti, Louis R., 2017. "Financial contagion risk and the stochastic discount factor," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 230-248.
    23. Filimonov, V. & Sornette, D., 2013. "A stable and robust calibration scheme of the log-periodic power law model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3698-3707.
    24. M. Bartolozzi & S. Drożdż & D. B. Leinweber & J. Speth & A. W. Thomas, 2005. "Self-Similar Log-Periodic Structures In Western Stock Markets From 2000," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 16(09), pages 1347-1361.
    25. Dimitrios Dimitriou & Theodore Simos, 2013. "Contagion channels of the USA subprime financial crisis: Evidence from USA, EMU, China and Japan equity markets," Journal of Financial Economic Policy, Emerald Group Publishing, vol. 5(1), pages 61-71, February.
    26. Wei Zhou, 2017. "Dynamic and Asymmetric Contagion Reactions of Financial Markets During the Last Subprime Crisis," Computational Economics, Springer;Society for Computational Economics, vol. 50(2), pages 207-230, August.
    27. Sornette, Didier & Cauwels, Peter, 2015. "Financial Bubbles: Mechanisms and Diagnostics," Review of Behavioral Economics, now publishers, vol. 2(3), pages 279-305, October.
    28. Vladimir Filimonov & Didier Sornette, 2011. "A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model," Papers 1108.0099, arXiv.org, revised Jun 2013.
    29. A. Johansen & D. Sornette, 1999. "Financial "Anti-Bubbles": Log-Periodicity In Gold And Nikkei Collapses," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 563-575.
    30. M. Bartolozzi & S. Drozdz & D. B. Leinweber & J. Speth & A. W. Thomas, 2005. "Self-Similar Log-Periodic Structures in Western Stock Markets from 2000," Papers cond-mat/0501513, arXiv.org, revised Mar 2005.
    31. Johansen, Anders, 2004. "Origin of crashes in three US stock markets: shocks and bubbles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 135-142.
    32. David S. Br�e & Damien Challet & Pier Paolo Peirano, 2013. "Prediction accuracy and sloppiness of log-periodic functions," Quantitative Finance, Taylor & Francis Journals, vol. 13(2), pages 275-280, January.
    33. Wosnitza, Jan Henrik & Denz, Cornelia, 2013. "Liquidity crisis detection: An application of log-periodic power law structures to default prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3666-3681.
    34. Sornette, Didier & Woodard, Ryan & Zhou, Wei-Xing, 2009. "The 2006–2008 oil bubble: Evidence of speculation, and prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1571-1576.
    35. David S. Bree & Nathan Lael Joseph, 2010. "Testing for financial crashes using the Log Periodic Power Law mode," Papers 1002.1010, arXiv.org, revised Apr 2013.
    36. Drożdż, S. & Grümmer, F. & Ruf, F. & Speth, J., 2003. "Log-periodic self-similarity: an emerging financial law?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 174-182.
    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. Shu, Min & Zhu, Wei, 2020. "Detection of Chinese stock market bubbles with LPPLS confidence indicator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    2. Bikramaditya Ghosh & Spyros Papathanasiou & Nikita Ramchandani & Dimitrios Kenourgios, 2021. "Diagnosis and Prediction of IIGPS’ Countries Bubble Crashes during BREXIT," Mathematics, MDPI, vol. 9(9), pages 1-14, April.
    3. Bikramaditya Ghosh & Spyros Papathanasiou & Vandita Dar & Dimitrios Kenourgios, 2022. "Deconstruction of the Green Bubble during COVID-19 International Evidence," Sustainability, MDPI, vol. 14(6), pages 1-18, March.

    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. Sornette, Didier & Woodard, Ryan & Yan, Wanfeng & Zhou, Wei-Xing, 2013. "Clarifications to questions and criticisms on the Johansen–Ledoit–Sornette financial bubble model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4417-4428.
    2. Shu, Min & Zhu, Wei, 2020. "Detection of Chinese stock market bubbles with LPPLS confidence indicator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    3. Papastamatiou, Konstantinos & Karakasidis, Theodoros, 2022. "Bubble detection in Greek Stock Market: A DS-LPPLS model approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    4. Wosnitza, Jan Henrik & Denz, Cornelia, 2013. "Liquidity crisis detection: An application of log-periodic power law structures to default prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3666-3681.
    5. Lin, L. & Ren, R.E. & Sornette, D., 2014. "The volatility-confined LPPL model: A consistent model of ‘explosive’ financial bubbles with mean-reverting residuals," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 210-225.
    6. Wosnitza, Jan Henrik & Leker, Jens, 2014. "Can log-periodic power law structures arise from random fluctuations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 228-250.
    7. 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.
    8. Demos, G. & Sornette, D., 2019. "Comparing nested data sets and objectively determining financial bubbles’ inceptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 661-675.
    9. Gharib, Cheima & Mefteh-Wali, Salma & Serret, Vanessa & Ben Jabeur, Sami, 2021. "Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach," Resources Policy, Elsevier, vol. 74(C).
    10. Bikramaditya Ghosh & Spyros Papathanasiou & Nikita Ramchandani & Dimitrios Kenourgios, 2021. "Diagnosis and Prediction of IIGPS’ Countries Bubble Crashes during BREXIT," Mathematics, MDPI, vol. 9(9), pages 1-14, April.
    11. 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.
    12. Guilherme Demos & Didier Sornette, 2017. "Lagrange regularisation approach to compare nested data sets and determine objectively financial bubbles' inceptions," Papers 1707.07162, arXiv.org.
    13. 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.
    14. Fry, John & Cheah, Eng-Tuck, 2016. "Negative bubbles and shocks in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 343-352.
    15. Min Shu & Ruiqiang Song & Wei Zhu, 2021. "The 'COVID' Crash of the 2020 U.S. Stock Market," Papers 2101.03625, arXiv.org.
    16. Vakhtina, Elena & Wosnitza, Jan Henrik, 2015. "Capital market based warning indicators of bank runs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 304-320.
    17. Shu, Min & Song, Ruiqiang & Zhu, Wei, 2021. "The ‘COVID’ crash of the 2020 U.S. Stock market," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    18. 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.
    19. Li Lin & Didier Sornette, 2015. ""Speculative Influence Network" during financial bubbles: application to Chinese Stock Markets," Papers 1510.08162, arXiv.org.
    20. Hideyuki Takagi, 2021. "Exploring the Endogenous Nature of Meme Stocks Using the Log-Periodic Power Law Model and Confidence Indicator," Papers 2110.06190, arXiv.org.

    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:phsmap:v:503:y:2018:i:c:p:947-957. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.