IDEAS home Printed from https://ideas.repec.org/a/taf/apfiec/v8y1998i5p477-485.html
   My bibliography  Save this article

Forecasting index volatility: sampling interval and non-trading effects

Author

Listed:
  • David Walsh
  • Glenn Yu-Gen Tsou

Abstract

A detailed comparison is made of volatility forecasting techniques on Australian value-weighted indices. The techniques compared are the naive approach (historical volatility), an improved extreme-value method (IEV), the ARCH/GARCH class of models and an exponentially weighted moving average (EWMA) of volatility. The study suggests that the EWMA technique appears to be the best volatility forecasting technique, closely followed by the appropriate GARCH specification. Both the IEV and historical volatility approaches are poor by comparison. The diversification benefit that arises from indices with larger numbers of stocks appears to make forecasting the volatility of larger indices more accurate. However, as the sampling interval is reduced, the non-trading effects evident in the larger indices start to counteract this benefit.

Suggested Citation

  • David Walsh & Glenn Yu-Gen Tsou, 1998. "Forecasting index volatility: sampling interval and non-trading effects," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 477-485.
    • Handle: RePEc:taf:apfiec:v:8:y:1998:i:5:p:477-485
    DOI: 10.1080/096031098332772
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/096031098332772
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/096031098332772?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. Ray Ball & John Bowers, 1987. "A Corrected Statex-Actuaries Daily Accumulation Index," Australian Journal of Management, Australian School of Business, vol. 12(1), pages 1-8, June.
    2. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    3. Andrew L. Turner & Eric J. Weigel, 1992. "Daily Stock Market Volatility: 1928--1989," Management Science, INFORMS, vol. 38(11), pages 1586-1609, November.
    4. 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.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    7. Tse, Y. K., 1991. "Stock returns volatility in the Tokyo stock exchange," Japan and the World Economy, Elsevier, vol. 3(3), pages 285-298, November.
    8. Dimson, Elroy & Marsh, Paul, 1990. "Volatility forecasting without data-snooping," Journal of Banking & Finance, Elsevier, vol. 14(2-3), pages 399-421, August.
    9. Wiggins, James B, 1991. "Empirical Tests of the Bias and Efficiency of the Extreme-Value Variance Estimator for Common Stocks," The Journal of Business, University of Chicago Press, vol. 64(3), pages 417-432, July.
    10. Beckers, Stan, 1983. "Variances of Security Price Returns Based on High, Low, and Closing Prices," The Journal of Business, University of Chicago Press, vol. 56(1), pages 97-112, January.
    11. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
    12. Poon, Ser-Huang & Taylor, Stephen J., 1992. "Stock returns and volatility: An empirical study of the UK stock market," Journal of Banking & Finance, Elsevier, vol. 16(1), pages 37-59, February.
    13. 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.
    14. repec:bla:jfinan:v:43:y:1988:i:2:p:451-66 is not listed on IDEAS
    15. Akgiray, Vedat, 1989. "Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts," The Journal of Business, University of Chicago Press, vol. 62(1), pages 55-80, January.
    16. Marsh, Terry A. & Rosenfeld, Eric R., 1986. "Non-trading, market making, and estimates of stock price volatility," Journal of Financial Economics, Elsevier, vol. 15(3), pages 359-372, March.
    17. Geoffrey Booth, G. & Hatem, John & Virtanen, Ilkka & Yli-Olli, Paavo, 1992. "Stochastic modeling of security returns: Evidence from the Helsinki Stock Exchange," European Journal of Operational Research, Elsevier, vol. 56(1), pages 98-106, January.
    18. Kunitomo, Naoto, 1992. "Improving the Parkinson Method of Estimating Security Price Volatilities," The Journal of Business, University of Chicago Press, vol. 65(2), pages 295-302, April.
    19. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    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. Ercan Balaban & Asli Bayar & Robert Faff, 2006. "Forecasting stock market volatility: Further international evidence," The European Journal of Finance, Taylor & Francis Journals, vol. 12(2), pages 171-188.
    2. Degiannakis, Stavros & Floros, Christos, 2013. "Modeling CAC40 volatility using ultra-high frequency data," Research in International Business and Finance, Elsevier, vol. 28(C), pages 68-81.
    3. Amare Wubishet Ayele & Emmanuel Gabreyohannes & Yohannes Yebabe Tesfay, 2017. "Macroeconomic Determinants of Volatility for the Gold Price in Ethiopia: The Application of GARCH and EWMA Volatility Models," Global Business Review, International Management Institute, vol. 18(2), pages 308-326, April.
    4. Matei, Marius, 2010. "Risk analysis in the evaluation of the international investment opportunities. Advances in modelling and forecasting volatility for risk assessment purposes," Working Papers of Institute for Economic Forecasting 100201, Institute for Economic Forecasting.
    5. Stavros Degiannakis & Evdokia Xekalaki, 2007. "Assessing the performance of a prediction error criterion model selection algorithm in the context of ARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 17(2), pages 149-171.
    6. Eugenie Hol & Siem Jan Koopman, 2000. "Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility," Tinbergen Institute Discussion Papers 00-104/4, Tinbergen Institute.
    7. Kim-Leng Goh & Kim-Lian Kok, 2006. "Beating the Random Walk: Intraday Seasonality and Volatility in a Developing Stock Market," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 5(1), pages 41-59, April.
    8. Stephen L. Lee & Simon Stevenson, 2001. "Time Weighted Portfolio Optimisation," ERES eres2001_207, European Real Estate Society (ERES).

    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. Torben G. Andersen & Tim Bollerslev, 1997. "Answering the Critics: Yes, ARCH Models Do Provide Good Volatility Forecasts," NBER Working Papers 6023, National Bureau of Economic Research, Inc.
    2. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    3. Christensen, Kim & Podolski, Mark, 2005. "Asymptotic theory for range-based estimation of integrated variance of a continuous semi-martingale," Technical Reports 2005,18, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Neda Todorova, 2012. "Volatility estimators based on daily price ranges versus the realized range," Applied Financial Economics, Taylor & Francis Journals, vol. 22(3), pages 215-229, February.
    5. Ncube, Mthuli, 1996. "Modelling implied volatility with OLS and panel data models," Journal of Banking & Finance, Elsevier, vol. 20(1), pages 71-84, January.
    6. Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038, Elsevier.
    7. Dicle, Mehmet F. & Levendis, John, 2020. "Historic risk and implied volatility," Global Finance Journal, Elsevier, vol. 45(C).
    8. Caporin, Massimiliano & Ranaldo, Angelo & Santucci de Magistris, Paolo, 2013. "On the predictability of stock prices: A case for high and low prices," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5132-5146.
    9. Yadav, Pradeep K., 1992. "Event studies based on volatility of returns and trading volume: A review," The British Accounting Review, Elsevier, vol. 24(2), pages 157-184.
    10. David McMillan & Alan Speight & Owain Apgwilym, 2000. "Forecasting UK stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 10(4), pages 435-448.
    11. Ercan Balaban & Asli Bayar & Robert Faff, 2006. "Forecasting stock market volatility: Further international evidence," The European Journal of Finance, Taylor & Francis Journals, vol. 12(2), pages 171-188.
    12. Richard D. F. Harris & Murat Mazibas, 2022. "A component Markov regime‐switching autoregressive conditional range model," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 650-683, April.
    13. Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012. "Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range," International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
    14. Angelovska, Julijana & Ivanovski, Zoran, 2018. "Accuracy In Risk Estimation Based On Simple Sma And Ewma Models:Evidence From Macedonian Stock Market," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 9(1), pages 17-27.
    15. Bauer, Rob M M J & Nieuwland, Frederick G M C & Verschoor, Willem F C, 1994. "German Stock Market Dynamics," Empirical Economics, Springer, vol. 19(3), pages 397-418.
    16. Brooks, Robert D. & Davidson, Sinclair & Faff, Robert W., 1997. "An examination of the effects of major political change on stock market volatility: the South African experience," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 7(3), pages 255-275, October.
    17. Novkovska, Blagica & Serafimovic, Gordana, 2018. "Recognizing The Vulnerability Of Generation Z To Economic And Social Risks," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 9(1), pages 29-37.
    18. Pandey, Ajay, 2003. "Modeling and Forecasting Volatility in Indian Capital Markets," IIMA Working Papers WP2003-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    19. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    20. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.

    More about this item

    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:taf:apfiec:v:8:y:1998:i:5:p:477-485. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAFE20 .

    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.