IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/34160.html
   My bibliography  Save this paper

The instability of the correlation structure of the S&P 500

Author

Listed:
  • Lyócsa, Štefan
  • Výrost, Tomáš
  • Baumöhl, Eduard

Abstract

Using weekly returns of S&P 500 constituents, we study the time-varying correlation structure during the period of 2006 to mid-2011. Contrary to most of the previous correlation studies of many assets, we do not use rolling correlations but the DCC MV-GARCH model with the MacGyver strategy proposed by Engle (2009). We find empirical evidence that the correlation structure tends to change significantly during the periods of high volatility and market downturns.

Suggested Citation

  • Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2011. "The instability of the correlation structure of the S&P 500," MPRA Paper 34160, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:34160
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/34160/2/MPRA_paper_34160.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    2. Junsoo Lee & Mark C. Strazicich, 2013. "Minimum LM unit root test with one structural break," Economics Bulletin, AccessEcon, vol. 33(4), pages 2483-2492.
    3. Liang Ding & Hiroyoki Miyake & Hao Zou, 2011. "Asymmetric correlations in equity returns: a fundamental-based explanation," Applied Financial Economics, Taylor & Francis Journals, vol. 21(6), pages 389-399.
    4. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    5. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    6. Cook, Steven & Manning, Neil, 2004. "Lag optimisation and finite-sample size distortion of unit root tests," Economics Letters, Elsevier, vol. 84(2), pages 267-274, August.
    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. Vyrost, Tomas & Baumöhl, Eduard & Lyocsa, Stefan, 2013. "What Drives the Stock Market Integration in the CEE-3?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 61(1), pages 67-81.
    2. Gustavo Peralta, 2016. "The Nature of Volatility Spillovers across the International Capital Markets," CNMV Working Papers CNMV Working Papers no. 6, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
    3. Baumöhl, Eduard & Lyócsa, Štefan, 2012. "Constructing weekly returns based on daily stock market data: A puzzle for empirical research?," MPRA Paper 43431, University Library of Munich, Germany.
    4. Erick Treviño Aguilar, 2020. "The interdependency structure in the Mexican stock exchange: A network approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-31, October.
    5. Claudiu Tiberiu Albulescu & Daniel Goyeau & Aviral Kumar Tiwari, 2017. "Co-movements and contagion between international stock index futures markets," Empirical Economics, Springer, vol. 52(4), pages 1529-1568, June.
    6. Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Similarity of emerging market returns under changing market conditions: Markets in the ASEAN-4, Latin America, Middle East, and BRICs," Economic Systems, Elsevier, vol. 39(2), pages 253-268.
    7. Chou, Ray Yeutien & Liu, Nathan, 2010. "The economic value of volatility timing using a range-based volatility model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2288-2301, November.
    8. De Nard, Gianluca & Engle, Robert F. & Ledoit, Olivier & Wolf, Michael, 2022. "Large dynamic covariance matrices: Enhancements based on intraday data," Journal of Banking & Finance, Elsevier, vol. 138(C).
    9. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    10. Baruník, Jozef & Kočenda, Evžen & Vácha, Lukáš, 2016. "Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers," Journal of Financial Markets, Elsevier, vol. 27(C), pages 55-78.
    11. Ray Chou & Chun-Chou Wu & Nathan Liu, 2009. "Forecasting time-varying covariance with a range-based dynamic conditional correlation model," Review of Quantitative Finance and Accounting, Springer, vol. 33(4), pages 327-345, November.
    12. Mohamed Beraich & Karim Amzile & Jaouad Laamire & Omar Zirari & Mohamed Amine Fadali, 2022. "Volatility Spillover Effects of the US, European and Chinese Financial Markets in the Context of the Russia–Ukraine Conflict," IJFS, MDPI, vol. 10(4), pages 1-18, October.
    13. Çelik, İsmail & Sak, Ahmet Furkan & Höl, Arife Özdemir & Vergili, Gizem, 2022. "The dynamic connectedness and hedging opportunities of implied and realized volatility: Evidence from clean energy ETFs," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    14. Mehmet Sahiner, 2024. "Volatility Spillovers and Contagion During Major Crises: An Early Warning Approach Based on a Deep Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2435-2499, June.
    15. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2019. "Range-based DCC models for covariance and value-at-risk forecasting," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 58-76.
    16. Sharma, Gagan Deep & Sarker, Tapan & Rao, Amar & Talan, Gaurav & Jain, Mansi, 2022. "Revisiting conventional and green finance spillover in post-COVID world: Evidence from robust econometric models," Global Finance Journal, Elsevier, vol. 51(C).
    17. Haiming Long & Ji Zhang & Nengyu Tang, 2017. "Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    18. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang & Uryasev, Stan, 2016. "A financial network perspective of financial institutions’ systemic risk contributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 183-196.
    19. Weibo Li & Wei Liu & Lei Wu & Xue Guo, 2021. "Risk spillover networks in financial system based on information theory," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    20. Ying-Ying Shen & Zhi-Qiang Jiang & Jun-Chao Ma & Gang-Jin Wang & Wei-Xing Zhou, 2022. "Sector connectedness in the Chinese stock markets," Empirical Economics, Springer, vol. 62(2), pages 825-852, February.

    More about this item

    Keywords

    correlation structure; dynamic conditional correlations; range-based volatility; conditional volatility; MacGyver strategy;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G1 - Financial Economics - - General Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:34160. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.