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Forecasting stock market volatility: Further international evidence

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Author Info

  • 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.

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File URL: http://www.tandfonline.com/doi/abs/10.1080/13518470500146082
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

Volume (Year): 12 (2006)
Issue (Month): 2 ()
Pages: 171-188

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Handle: RePEc:taf:eurjfi:v:12:y:2006:i:2:p:171-188

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Related research

Keywords: Stock market volatility; forecasting; forecast evaluation;

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Citations

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. 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).
  5. 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, vol. 12(5), pages 449-453.
  6. 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, November.

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