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Bubble detection in Greek Stock Market: A DS-LPPLS model approach

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  • Papastamatiou, Konstantinos
  • Karakasidis, Theodoros

Abstract

Upfront bubble detection is one of the holy grails in Financial Markets. In the present paper, in order to archive this goal, we consider two different methods based on the Log Periodic Power Law. We implement this early detection algorithms in the Greek Stock Market, which is a relatively “shallow” and underdeveloped market. We have examined a period from 1997 until the end of 2019, an epoch before the rise of COVID-19 virus. Using this methodology, we managed to detect with a relatively good accuracy the formation and the critical time for both positive and negative financial bubbles that occurred during the examination period.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:587:y:2022:i:c:s0378437121008062
    DOI: 10.1016/j.physa.2021.126533
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    1. Didier Sornette & Ryan Woodard, & Wanfeng Yan & Wei-Xing Zhou, "undated". "Clarifications to Questions and Criticisms on the Johansen-Ledoit-Sornette bubble Model," Working Papers ETH-RC-11-004, ETH Zurich, Chair of Systems Design.
    2. Dimitrios I. Maditinos & Željko Šević & Nikolaos G. Theriou, 2007. "Investors' behaviour in the Athens Stock Exchange (ASE)," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 24(1), pages 32-50, March.
    3. George Chang & James Feigenbaum, 2006. "A Bayesian analysis of log-periodic precursors to financial crashes," Quantitative Finance, Taylor & Francis Journals, vol. 6(1), pages 15-36.
    4. 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.
    5. Vladimir Filimonov & Didier Sornette, "undated". "A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model," Working Papers ETH-RC-11-002, ETH Zurich, Chair of Systems Design.
    6. Petr Geraskin & Dean Fantazzini, 2013. "Everything you always wanted to know about log-periodic power laws for bubble modeling but were afraid to ask," The European Journal of Finance, Taylor & Francis Journals, vol. 19(5), pages 366-391, May.
    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. Dimitrios Gounopoulos & Kyriaki Kosmidou & Dimitrios Kousenidis & Victoria Patsika, 2019. "The investigation of the dynamic linkages between real estate market and stock market in Greece," The European Journal of Finance, Taylor & Francis Journals, vol. 25(7), pages 647-669, May.
    9. Sornette, Didier & Zhou, Wei-Xing, 2006. "Importance of positive feedbacks and overconfidence in a self-fulfilling Ising model of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 704-726.
    10. 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.
    11. Provopoulos, George A., 2014. "The Greek Economy and Banking System: Recent Developments and the Way Forward," Journal of Macroeconomics, Elsevier, vol. 39(PB), pages 240-249.
    12. Didier SORNETTE & Guilherme DEMOS & Zhang QUN & Peter CAUWELS & Vladimir FILIMONOV & Qunzhi ZHANG, 2015. "Real-Time Prediction and Post-Mortem Analysis of the Shanghai 2015 Stock Market Bubble and Crash," Swiss Finance Institute Research Paper Series 15-32, Swiss Finance Institute.
    13. Antonios Antoniou & Emilios C. Galariotis & Spyros I. Spyrou, 2005. "Contrarian Profits and the Overreaction Hypothesis: the Case of the Athens Stock Exchange," European Financial Management, European Financial Management Association, vol. 11(1), pages 71-98, January.
    14. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    15. John T. Barkoulas & Christopher F. Baum & Nickolaos Travlos, 1996. "Long Memory in the Greek Stock Market," Boston College Working Papers in Economics 356., Boston College Department of Economics.
    16. W.-X. Zhou & D. Sornette, 2007. "Self-organizing Ising model of financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 175-181, January.
    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. 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.
    20. Economou, Fotini & Katsikas, Epameinondas & Vickers, Gregory, 2016. "Testing for herding in the Athens Stock Exchange during the crisis period," Finance Research Letters, Elsevier, vol. 18(C), pages 334-341.
    21. Ioannis P. Antoniades & Giuseppe Brandi & L. G. Magafas & T. Di Matteo, 2020. "The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool," Papers 2010.08890, arXiv.org, revised Dec 2020.
    22. 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.
    23. Sornette, Didier & Cauwels, Peter, 2015. "Financial Bubbles: Mechanisms and Diagnostics," Review of Behavioral Economics, now publishers, vol. 2(3), pages 279-305, October.
    24. Zhou, Wei-Xing & Sornette, Didier, 2006. "Is there a real-estate bubble in the US?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 361(1), pages 297-308.
    25. 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.
    26. Yan, Wanfeng & Woodard, Ryan & Sornette, Didier, 2012. "Diagnosis and prediction of rebounds in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1361-1380.
    27. 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.
    28. Christos Pierros, 2020. "Income distribution, structural competitiveness and financial fragility of the Greek economy," International Review of Applied Economics, Taylor & Francis Journals, vol. 34(1), pages 50-74, January.
    29. 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.
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