Forecasting stock market volatility: Further international evidence

Balaban, Ercan, Bayar, Asli and Faff, Robert W. (2006) Forecasting stock market volatility: Further international evidence. European Journal of Finance, 12 2: 171-188. doi:10.1080/13518470500146082

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Author Balaban, Ercan
Bayar, Asli
Faff, Robert W.
Title Forecasting stock market volatility: Further international evidence
Journal name European Journal of Finance   Check publisher's open access policy
ISSN 1466-4364
Publication date 2006-02
Sub-type Article (original research)
DOI 10.1080/13518470500146082
Volume 12
Issue 2
Start page 171
End page 188
Total pages 18
Place of publication Abingdon, United Kingdom
Publisher Routledge
Language eng
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.
Keyword Stock market volatility
Forecast evaluation
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: ERA 2012 Admin Only
UQ Business School Publications
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Created: Mon, 07 Mar 2011, 10:25:41 EST by Karen Morgan on behalf of UQ Business School