IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/68460.html
   My bibliography  Save this paper

Predictive Models for Disaggregate Stock Market Volatility

Author

Listed:
  • Chong, Terence Tai Leung
  • Lin, Shiyu

Abstract

This paper incorporates the macroeconomic determinants into the forecasting model of industry-level stock return volatility in order to detect whether different macroeconomic factors can forecast the volatility of various industries. To explain different fluctuation characteristics among industries, we identified a set of macroeconomic determinants to examine their effects. The Clark and West (2007) test is employed to verify whether the new forecasting models, which vary among industries based on the in-sample results, can have better predictions than the two benchmark models. Our results show that default return and default yield have significant impacts on stock return volatility.

Suggested Citation

  • Chong, Terence Tai Leung & Lin, Shiyu, 2015. "Predictive Models for Disaggregate Stock Market Volatility," MPRA Paper 68460, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:68460
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/68460/1/MPRA_paper_68460.pdf
    File Function: original version
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Peter Oertmann* & Christel Rendu & Heinz Zimmermann, 2000. "Interest Rate Risk of European Financial Corporations," European Financial Management, European Financial Management Association, vol. 6(4), pages 459-478, December.
    2. John Y. Campbell & Martin Lettau & Burton G. Malkiel & Yexiao Xu, 2001. "Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk," Journal of Finance, American Finance Association, vol. 56(1), pages 1-43, February.
    3. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    5. Boudoukh, Jacob & Richardson, Matthew & Whitelaw, Robert F, 1994. " Industry Returns and the Fisher Effect," Journal of Finance, American Finance Association, vol. 49(5), pages 1595-1615, December.
    6. Paye, Bradley S., 2012. "‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables," Journal of Financial Economics, Elsevier, vol. 106(3), pages 527-546.
    7. Whitelaw, Robert F, 1994. " Time Variations and Covariations in the Expectation and Volatility of Stock Market Returns," Journal of Finance, American Finance Association, vol. 49(2), pages 515-541, June.
    8. Tobias Adrian & Joshua Rosenberg, 2008. "Stock Returns and Volatility: Pricing the Short‐Run and Long‐Run Components of Market Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2997-3030, December.
    9. Fama, Eugene F. & French, Kenneth R., 1997. "Industry costs of equity," Journal of Financial Economics, Elsevier, vol. 43(2), pages 153-193, February.
    10. Hamilton, James D & Gang, Lin, 1996. "Stock Market Volatility and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 573-593, Sept.-Oct.
    11. Sadorsky, Perry & Henriques, Irene, 2001. "Multifactor risk and the stock returns of Canadian paper and forest products companies," Forest Policy and Economics, Elsevier, vol. 3(3-4), pages 199-208, November.
    12. Brennan, Michael J. & Xia, Yihong, 2001. "Stock price volatility and equity premium," Journal of Monetary Economics, Elsevier, vol. 47(2), pages 249-283, April.
    13. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    14. Sweeney, Richard J & Warga, Arthur D, 1986. " The Pricing of Interest-Rate Risk: Evidence from the Stock Market," Journal of Finance, American Finance Association, vol. 41(2), pages 393-410, June.
    15. Fabio Fornari & Antonio Mele, 2013. "Financial Volatility and Economic Activity," Journal of Financial Management, Markets and Institutions, Società editrice il Mulino, issue 2, pages 155-198, December.
    16. Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, vol. 44(5), pages 1115-1153, December.
    17. Vihang Errunza & Ked Hogan, 1998. "Macroeconomic Determinants of European Stock Market Volatility," European Financial Management, European Financial Management Association, vol. 4(3), pages 361-377, November.
    18. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
    19. Mele, Antonio, 2007. "Asymmetric stock market volatility and the cyclical behavior of expected returns," Journal of Financial Economics, Elsevier, vol. 86(2), pages 446-478, November.
    20. Beltratti, A. & Morana, C., 2006. "Breaks and persistency: macroeconomic causes of stock market volatility," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 151-177.
    21. Veronesi, Pietro, 1999. "Stock Market Overreaction to Bad News in Good Times: A Rational Expectations Equilibrium Model," Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 975-1007.
    22. Sadorsky, Perry, 2003. "The macroeconomic determinants of technology stock price volatility," Review of Financial Economics, Elsevier, vol. 12(2), pages 191-205.
    23. Corradi, Valentina & Distaso, Walter & Mele, Antonio, 2013. "Macroeconomic determinants of stock volatility and volatility premiums," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 203-220.
    24. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    25. Faff, Robert W. & Brailsford, Timothy J., 1999. "Oil price risk and the Australian stock market," Journal of Energy Finance & Development, Elsevier, vol. 4(1), pages 69-87, June.
    26. Officer, R R, 1973. "The Variability of the Market Factor of the New York Stock Exchange," The Journal of Business, University of Chicago Press, vol. 46(3), pages 434-453, July.
    27. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    28. E. Dinenis & S. K. Staikouras, 1998. "Interest rate changes and common stock returns of financial institutions: evidence from the UK," The European Journal of Finance, Taylor & Francis Journals, vol. 4(2), pages 113-127.
    29. Colm Kearney & Kevin Daly, 1998. "The causes of stock market volatility in Australia," Applied Financial Economics, Taylor & Francis Journals, vol. 8(6), pages 597-605.
    30. Sadorsky, Perry, 2001. "Risk factors in stock returns of Canadian oil and gas companies," Energy Economics, Elsevier, vol. 23(1), pages 17-28, January.
    31. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    32. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, vol. 8(4), pages 323-361, December.
    33. Brandt, Michael W. & Kang, Qiang, 2004. "On the relationship between the conditional mean and volatility of stock returns: A latent VAR approach," Journal of Financial Economics, Elsevier, vol. 72(2), pages 217-257, May.
    34. Andreas Humpe & Peter Macmillan, 2009. "Can macroeconomic variables explain long-term stock market movements? A comparison of the US and Japan," Applied Financial Economics, Taylor & Francis Journals, vol. 19(2), pages 111-119.
    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. Conrad, Christian & Glas, Alexander, 2018. "‘Déjà vol’ revisited: Survey forecasts of macroeconomic variables predict volatility in the cross-section of industry portfolios," Working Papers 0655, University of Heidelberg, Department of Economics.

    More about this item

    Keywords

    Industry level stock return volatility; Out-of-sample forecast; Granger Causality.;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:68460. 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: (Joachim Winter). General contact details of provider: http://edirc.repec.org/data/vfmunde.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 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.

    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.