Estimating Value at Risk Using Garch Models: Evidence From the Turkish Banks
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Date
2008
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İzmir Ekonomi Üniversitesi
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Abstract
Bu çalışma, Türkiye'deki menkul kıymet borsasının davranışını ve karakteristiğini banka hisselerini odak noktası alarak incelemektedir. Yapılan analizler GARCH modelini, dört farklı periyod için, finansal zaman serilerine uygulayabilmek üzerine kurulmuştur. Model parametreleri Normal dağılım ve Student-t dağılımı olmak üzere iki farklı dağılım varsayımı altında tespit edilmiştir. Temin edilen parametreler vasıtasıyla bugünün ve bir adım sonrasının Riske Maruz Değer rakamları tahmin edilmiştir. Elde edilen sonuçlar GARCH (1, 1) modelinin banka ve endeks getiri serilerini modellemede uygun olduğunu göstermektedir. Dolayısıyla, buradan hareketle hesaplanan RMD değerlerinin fiyat hareketlerini yakalamada son derece başarılı olduğu izlenmiştir.
This thesis investigates the behaviour and characteristics of Turkish stock markets with a special focus on listed bank equities. The analysis is based on the fitting of GARCH model to financial return series for four different time period. The estimation of the parameters in the model is examined with two distributional assumptions for the innovations; Gaussian distribution and Student-t distribution. Furthermore, today?s Value at Risk figures are obtained via GARCH specifications, and also one-step ahead VaR figures are forecasted. The results indicate that GARCH (1, 1) model is suitable for modelling bank and index return series, hence, the VaR captures well stocks? price movements.
This thesis investigates the behaviour and characteristics of Turkish stock markets with a special focus on listed bank equities. The analysis is based on the fitting of GARCH model to financial return series for four different time period. The estimation of the parameters in the model is examined with two distributional assumptions for the innovations; Gaussian distribution and Student-t distribution. Furthermore, today?s Value at Risk figures are obtained via GARCH specifications, and also one-step ahead VaR figures are forecasted. The results indicate that GARCH (1, 1) model is suitable for modelling bank and index return series, hence, the VaR captures well stocks? price movements.
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Ekonometri, Econometrics, Fiyat hareketi, Price movement, GARCH model, GARCH model, Riske maruz değer, Value at risk
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1
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131
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