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Forecasting stock market volatility: Further international evidence

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dc.contributor.author Balaban, Ercan
dc.contributor.author Bayar, Aslı
dc.contributor.author Faff, Robert W.
dc.date.accessioned 2023-02-16T12:49:14Z
dc.date.available 2023-02-16T12:49:14Z
dc.date.issued 2006-02
dc.identifier.citation Balaban, Ercan; Bayar, Aslı; Faff, Robert W. (2006). "Forecasting stock market volatility: Further international evidence", European Journal of Finance, Vol. 12, no. 2, pp. 171-188. tr_TR
dc.identifier.issn 1351-847X
dc.identifier.uri http://hdl.handle.net/20.500.12416/6255
dc.description.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. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1080/13518470500146082 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Forecast Evaluation tr_TR
dc.subject Forecasting tr_TR
dc.subject Stock Market Volatility tr_TR
dc.title Forecasting stock market volatility: Further international evidence tr_TR
dc.type article tr_TR
dc.relation.journal European Journal of Finance tr_TR
dc.identifier.volume 12 tr_TR
dc.identifier.issue 2 tr_TR
dc.identifier.startpage 171 tr_TR
dc.identifier.endpage 188 tr_TR
dc.contributor.department Çankaya Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, İşletme Bölümü tr_TR


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