IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v462y2016icp350-357.html
   My bibliography  Save this article

The roles of mean residence time on herd behavior in a financial market

Author

Listed:
  • Li, Jiang-Cheng
  • Li, Yun-Xian
  • Tang, Nian-Sheng
  • Mei, Dong-Cheng

Abstract

We investigate the herd behavior of stock prices in a finance system with the Heston model. Based on parameter estimation of the Heston model obtained by minimizing the mean square deviation between the theoretical and empirical return distributions, we simulate mean residence time of positive return (MRTPR). Plots of MRTPR against the amplitude or mean reversion of volatility demonstrate a phenomenon of herd behavior for a positive cross correlation between noise sources of the Heston model. Also, for a negative cross correlation, a phenomenon of herd behavior is observed in plots of MRTPR against the long-run variance by increasing amplitude or mean reversion of volatility.

Suggested Citation

  • Li, Jiang-Cheng & Li, Yun-Xian & Tang, Nian-Sheng & Mei, Dong-Cheng, 2016. "The roles of mean residence time on herd behavior in a financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 350-357.
    • Handle: RePEc:eee:phsmap:v:462:y:2016:i:c:p:350-357
    DOI: 10.1016/j.physa.2016.06.061
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116303296
    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.2016.06.061?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. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    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. Zhong, Guang-Yan & Li, Jiang-Cheng & Jiang, George J. & Li, Hai-Feng & Tao, Hui-Ming, 2018. "The time delay restraining the herd behavior with Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 335-346.
    2. Thiago Christiano Silva & Benjamin Miranda Tabak & Idamar Magalhães Ferreira, 2019. "Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies," Complexity, Hindawi, vol. 2019, pages 1-14, December.
    3. Ni, Yensen & Cheng, Yirung & Huang, Paoyu & Day, Min-Yuh, 2018. "Trading strategies in terms of continuous rising (falling) prices or continuous bullish (bearish) candlesticks emitted," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 188-204.
    4. Bianca Reichert & Adriano Mendon a Souza, 2022. "Can the Heston Model Forecast Energy Generation? A Systematic Literature Review," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 289-295.
    5. Haghani, Milad & Sarvi, Majid, 2017. "Social dynamics in emergency evacuations: Disentangling crowd’s attraction and repulsion effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 24-34.

    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. Dash, Saumya Ranjan & Maitra, Debasish, 2018. "Does sentiment matter for stock returns? Evidence from Indian stock market using wavelet approach," Finance Research Letters, Elsevier, vol. 26(C), pages 32-39.
    2. Lallouache, Mehdi & Abergel, Frédéric, 2014. "Tick size reduction and price clustering in a FX order book," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 488-498.
    3. Dufour, Jean-Marie & García, René & Taamouti, Abderrahim, 2008. "Measuring causality between volatility and returns with high-frequency data," UC3M Working papers. Economics we084422, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
    5. Ozcan Ceylan, 2015. "Limited information-processing capacity and asymmetric stock correlations," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 1031-1039, June.
    6. Scalas, Enrico & Kaizoji, Taisei & Kirchler, Michael & Huber, Jürgen & Tedeschi, Alessandra, 2006. "Waiting times between orders and trades in double-auction markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 463-471.
    7. Nour Meddahi, 2002. "A theoretical comparison between integrated and realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 479-508.
    8. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(1), pages 1-30.
    9. Monira Essa Aloud, 2016. "Time Series Analysis Indicators under Directional Changes: The Case of Saudi Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 55-64.
    10. Vassilios G. Papavassiliou, 2016. "Allowing For Jump Measurements In Volatility: A High-Frequency Financial Data Analysis Of Individual Stocks," Bulletin of Economic Research, Wiley Blackwell, vol. 68(2), pages 124-132, April.
    11. Monira Essa Aloud, 2016. "Profitability of Directional Change Based Trading Strategies: The Case of Saudi Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 87-95.
    12. Cotter, John & Dowd, Kevin, 2007. "The tail risks of FX return distributions: A comparison of the returns associated with limit orders and market orders," Finance Research Letters, Elsevier, vol. 4(3), pages 146-154, September.
    13. Selçuk, Faruk & Gençay, Ramazan, 2006. "Intraday dynamics of stock market returns and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 375-387.
    14. Stefanescu, Razvan & Dumitriu, Ramona, 2015. "Conţinutul analizei seriilor de timp financiare [The Essentials of the Analysis of Financial Time Series]," MPRA Paper 67175, University Library of Munich, Germany.
    15. Challet, Damien & Stinchcombe, Robin, 2003. "Limit order market analysis and modelling: on a universal cause for over-diffusive prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 141-145.
    16. Feng, Yuanhua, 2002. "Modelling Different Volatility Components," CoFE Discussion Papers 02/18, University of Konstanz, Center of Finance and Econometrics (CoFE).
    17. Caporin, Massimiliano & Chang, Chia-Lin & McAleer, Michael, 2019. "Are the S&P 500 index and crude oil, natural gas and ethanol futures related for intra-day data?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 50-70.
    18. Gencay, Ramazan & Selcuk, Faruk & Ulugulyagci, Abdurrahman, 2003. "High volatility, thick tails and extreme value theory in value-at-risk estimation," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 337-356, October.
    19. Julien Chevallier & Benoît Sévi, 2011. "On the realized volatility of the ECX CO 2 emissions 2008 futures contract: distribution, dynamics and forecasting," Annals of Finance, Springer, vol. 7(1), pages 1-29, February.
    20. Masanori Hirano & Kiyoshi Izumi & Hiroyasu Matsushima & Hiroki Sakaji, 2020. "Comparing Actual and Simulated HFT Traders' Behavior for Agent Design," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(3), pages 1-6.

    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:462:y:2016:i:c:p:350-357. 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.