IDEAS home Printed from https://ideas.repec.org/a/taf/eurjfi/v12y2006i2p171-188.html
   My bibliography  Save this article

Forecasting stock market volatility: Further international evidence

Author

Listed:
  • Ercan Balaban
  • Asli Bayar
  • Robert Faff

Abstract

This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. First, standard (symmetric) loss functions are used to evaluate the performance of the competing models: mean absolute error, root mean squared error, and mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. Asymmetric loss functions are employed to penalize under-/over-prediction. When under-predictions are penalized more heavily, ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily, the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:eurjfi:v:12:y:2006:i:2:p:171-188 DOI: 10.1080/13518470500146082
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/13518470500146082
    Download Restriction: Access to full text is restricted to subscribers.

    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. Hendrik Bessembinder & Kalok Chan, 1998. "Market Efficiency and the Returns to Technical Analysis," Financial Management, Financial Management Association, vol. 27(2), Summer.
    2. Bessembinder, Hendrik & Chan, Kalok, 1995. "The profitability of technical trading rules in the Asian stock markets," Pacific-Basin Finance Journal, Elsevier, pages 257-284.
    3. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    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. Spyros Missiakoulis & Dimitrios Vasiliou & Nikolaos Eriotis, 2010. "Arithmetic mean: a bellwether for unbiased forecasting of portfolio performance," Managerial Finance, Emerald Group Publishing, vol. 36(11), pages 958-968, September.
    2. Ercan Balaban & Charalambos Th. Constantinou, 2006. "Volatility clustering and event-induced volatility: Evidence from UK mergers and acquisitions," The European Journal of Finance, Taylor & Francis Journals, pages 449-453.
    3. Missiakoulis, Spyros & Vasiliou, Dimitrios & Eriotis, Nikolaos, 2012. "Forecasting Performance with the Harmonic Mean: Long-Term Investment Horizons in Shanghai Stock Exchange," Review of Applied Economics, Review of Applied Economics, vol. 8(1).
    4. Ercan Balaban & Aslı Bayar, 2005. "Stock returns and volatility: empirical evidence from fourteen countries," Applied Economics Letters, Taylor & Francis Journals, vol. 12(10), pages 603-611.
    5. Balaban, Ercan & Lu, Shan, 2016. "Forecasting the term structure of volatility of crude oil price changes," Economics Letters, Elsevier, vol. 141(C), pages 116-118.
    6. Gozgor, Giray & Nokay, Pinar, 2011. "Comparing forecast performances among volatility estimation methods in the pricing of european type currency options of USD-TL and Euro-TL," MPRA Paper 34369, University Library of Munich, Germany.
    7. 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.

    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:eurjfi:v:12:y:2006:i:2:p:171-188. 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: (Chris Longhurst). General contact details of provider: http://www.tandfonline.com/REJF20 .

    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.

    We have no references for this item. You can help adding them by using 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.