IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/201668.html
   My bibliography  Save this paper

Differences of Opinion and Stock Market Volatility: Evidence from a Nonparametric Causality-in-Quantiles Approach

Author

Listed:
  • Mehmet Balcilar

    (Department of Economics, Eastern Mediterranean University and Department of Economics, University of Pretoria)

  • Riza Demirer

    (Department of Economics and Finance, Southern Illinois University Edwardsville, USA)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, South Africa and IPAG Business School, Paris, France)

  • Mark E. Wohar

    (Department of Economics, University of Nebraska at Omaha, USA and School of Business and Economics, Loughborough University, UK)

Abstract

This paper examines whether the differences of opinion across active money managers relates to stock market volatility via the recently proposed nonparametric causality-in-quantiles test. Using the dispersion in equity market exposures of active managers as a proxy for differences in opinion, we analyze the predictability of (realized) volatility of the S&P500 for the period July, 2006-August, 2016. Unlike the result of no predictability obtained under the misspecified linear set-up, our nonparametric causality-in-quantiles test indicates that dispersion in active managers’ risk exposures to the stock market can predict volatility over the range of quantiles that correspond to moderately high levels of market volatility. Our findings are in line with the previous literature that relates divergent beliefs across investors to subsequent stock returns and suggest that the effect on subsequent returns is likely to be transmitted via the volatility channel. Our results highlight the importance of detecting and modeling nonlinearity when analyzing the information content of divergent beliefs across market participants.

Suggested Citation

  • Mehmet Balcilar & Riza Demirer & Rangan Gupta & Mark E. Wohar, 2016. "Differences of Opinion and Stock Market Volatility: Evidence from a Nonparametric Causality-in-Quantiles Approach," Working Papers 201668, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201668
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Dimitri Vayanos & Paul Woolley, 2013. "An Institutional Theory of Momentum and Reversal," Review of Financial Studies, Society for Financial Studies, vol. 26(5), pages 1087-1145.
    2. Peter C. B. Phillips & Bruce E. Hansen, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," Review of Economic Studies, Oxford University Press, vol. 57(1), pages 99-125.
    3. Paul A. Gompers & Andrew Metrick, 2001. "Institutional Investors and Equity Prices," The Quarterly Journal of Economics, Oxford University Press, vol. 116(1), pages 229-259.
    4. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    5. Berkman, Henk & Dimitrov, Valentin & Jain, Prem C. & Koch, Paul D. & Tice, Sheri, 2009. "Sell on the news: Differences of opinion, short-sales constraints, and returns around earnings announcements," Journal of Financial Economics, Elsevier, vol. 92(3), pages 376-399, June.
    6. Zivot, Eric & Andrews, Donald W K, 2002. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 25-44, January.
    7. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    8. Chen, Joseph & Hong, Harrison & Stein, Jeremy C., 2002. "Breadth of ownership and stock returns," Journal of Financial Economics, Elsevier, vol. 66(2-3), pages 171-205.
    9. Robin L. Lumsdaine & David H. Papell, 1997. "Multiple Trend Breaks And The Unit-Root Hypothesis," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 212-218, May.
    10. Goetzmann, William N. & Massa, Massimo, 2005. "Dispersion of opinion and stock returns," Journal of Financial Markets, Elsevier, vol. 8(3), pages 324-349, August.
    11. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    12. Bekiros, Stelios & Gupta, Rangan & Majumdar, Anandamayee, 2016. "Incorporating economic policy uncertainty in US equity premium models: A nonlinear predictability analysis," Finance Research Letters, Elsevier, vol. 18(C), pages 291-296.
    13. Ron Kaniel & Péter Kondor, 2013. "The Delegated Lucas Tree," Review of Financial Studies, Society for Financial Studies, vol. 26(4), pages 929-984.
    14. Ajinkya, Bipin B & Gift, Michael J, 1985. "Dispersion of Financial Analysts' Earnings Forecasts and the (Option Model) Implied Standard Deviaitons of Stock Returns," Journal of Finance, American Finance Association, vol. 40(5), pages 1353-1365, December.
    15. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    16. Jiang, Hao & Sun, Zheng, 2014. "Dispersion in beliefs among active mutual funds and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 114(2), pages 341-365.
    17. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    18. Timothy C. Johnson, 2004. "Forecast Dispersion and the Cross Section of Expected Returns," Journal of Finance, American Finance Association, vol. 59(5), pages 1957-1978, October.
    19. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    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. Rangan Gupta, 2018. "Manager Sentiment and Stock Market Volatility," Working Papers 201853, University of Pretoria, Department of Economics.
    2. Konstantinos Gkillas & Rangan Gupta & Chi Keung Marco Lau & Muhammad Tahir Suleman, 2020. "Jumps beyond the realms of cricket: India's performance in One Day Internationals and stock market movements," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(6), pages 1109-1127, April.
    3. Rangan Gupta & Chi Keung Marco Lau & Wendy Nyakabawo, 2018. "Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment," Working Papers 201866, University of Pretoria, Department of Economics.

    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. Christou, Christina & Gupta, Rangan & Nyakabawo, Wendy & Wohar, Mark E., 2018. "Do house prices hedge inflation in the US? A quantile cointegration approach," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 15-26.
    2. Giorgio Canarella & Rangan Gupta & Stephen M. Miller & Stephen K. Pollard, 2019. "Unemployment rate hysteresis and the great recession: exploring the metropolitan evidence," Empirical Economics, Springer, vol. 56(1), pages 61-79, January.
    3. Adem Atmaz & Suleyman Basak, 2018. "Belief Dispersion in the Stock Market," Journal of Finance, American Finance Association, vol. 73(3), pages 1225-1279, June.
    4. Heidari, Hassan & Katircioglu, Salih Turan & Davoudi, Narmin, 2012. "Are current account deficits sustainable? New evidence from Iran using bounds test approach to level relationships," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW), vol. 6, pages 1-18.
    5. Jiang, Hao & Sun, Zheng, 2014. "Dispersion in beliefs among active mutual funds and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 114(2), pages 341-365.
    6. Kyriakos Emmanouilidis & Christos Karpetis, 2020. "The Defense–Growth Nexus: A Review of Time Series Methods and Empirical Results," Defence and Peace Economics, Taylor & Francis Journals, vol. 31(1), pages 86-104, January.
    7. Dakpogan, Arnaud & Smit, Eon, 2018. "The effect of electricity losses on GDP in Benin," MPRA Paper 89545, University Library of Munich, Germany.
    8. Nikolay Gospodinov & Ian Irvine, 2005. "A ‘long march’ perspective on tobacco use in Canada," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 38(2), pages 366-393, May.
    9. Fatih Kaplan & Ayşe E. Ünal, 2020. "Industrial Production Index - Crude Oil Price Nexus: Russia, Kazakhstan And Azerbaijan," Economic Annals, Faculty of Economics, University of Belgrade, vol. 65(227), pages 119-142, October –.
    10. Gupta, Rangan & Risse, Marian & Volkman, David A. & Wohar, Mark E., 2019. "The role of term spread and pattern changes in predicting stock returns and volatility of the United Kingdom: Evidence from a nonparametric causality-in-quantiles test using over 250 years of data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 391-405.
    11. Dimitrios Dadakas & Christos Karpetis & Athanasios Fassas & Erotokritos Varelas, 2016. "Sectoral Differences in the Choice of the Time Horizon during Estimation of the Unconditional Stock Beta," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 4(4), pages 1-13, December.
    12. Esteve, Vicente & Navarro-Ibáñez, Manuel & Prats, María A., 2013. "The Spanish term structure of interest rates revisited: Cointegration with multiple structural breaks, 1974–2010," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 24-34.
    13. Trofimov, Ivan D., 2018. "The secular decline in profit rates: time series analysis of a classical hypothesis," MPRA Paper 88248, University Library of Munich, Germany.
    14. Muhammad Shahbaz & Muhammad shahbaz Shabbir & Muhammad sabihuddin Butt, 2016. "Does Military Spending Explode External Debt in Pakistan?," Defence and Peace Economics, Taylor & Francis Journals, vol. 27(5), pages 718-741, September.
    15. Mehmet Balcilar & Elie Bouri & Rangan Gupta & David Roubaud, 2016. "Can Volume Predict Bitcoin Returns and Volatility? A Nonparametric Causality-in-Quantiles Approach," Working Papers 201662, University of Pretoria, Department of Economics.
    16. Caporale, Guglielmo Maria & Kontonikas, Alexandros, 2009. "The Euro and inflation uncertainty in the European Monetary Union," Journal of International Money and Finance, Elsevier, vol. 28(6), pages 954-971, October.
    17. Kellard, Neil & Mark E Wohar, 2003. "Trends and Persistence in Primary Commodity Prices," Royal Economic Society Annual Conference 2003 118, Royal Economic Society.
    18. J. Andrew Hansz & Ying Zhang & Tingyu Zhou, 2017. "An Investigation into the Substitutability of Equity and Mortgage REITs in Real Estate Portfolios," The Journal of Real Estate Finance and Economics, Springer, vol. 54(3), pages 338-364, April.
    19. Tarlok Singh, 2017. "Are Current Account Deficits in the OECD Countries Sustainable? Robust Evidence from Time-Series Estimators," The International Trade Journal, Taylor & Francis Journals, vol. 31(1), pages 29-64, January.
    20. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.

    More about this item

    Keywords

    Realized Volatility; Differences of opinion; Quantile Causality;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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:pre:wpaper:201668. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/decupza.html .

    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: Rangan Gupta (email available below). General contact details of provider: https://edirc.repec.org/data/decupza.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.