Volatilite Değerleme ve Tahmini Için ARCH ve GARCH Modellerinin Kullanımı

Abstract

Bu çalışma, 9 yıllık günlük verilere dayanarak IMKB 100 endeksinin volatilitesini değerlendirmek ve tahmin etmek için, her biri dört ayrı dağılımla denenen, ARMA özellikleri eklenebilen 11 değişik ARCH modelinin performansını sunmaktadır. Elde edilen sonuçlara göre, aynı dağılım kullanılırsa, kısmi entegre edilmiş asimetrik modeller bu özelliğe sahip olmayan orjinal versiyonlarından daha iyi volatilite değerlemesi yapabilmektedir. Çarpık-t ve Student-t dağılımlarının kullanılması modelin veriye daha uyumlu olmasını sağlamaktadır. Sonuç olarak, belirli bir model veya dağılımın kullanılmasının volatilite tahmininde açık bir iyileşmeye yol açmadığı gözlenmiştir.

Keywords

• GARCH, EGARCH, GJR, APARCH, IGARCH, FIGARCH, FIAPARCH, FIEGARCH, HYGARCH, ARMA, GED, Skewed-t, Ox, G@RCH

The Use of ARCH and GARCH Models for Estimating and Forecasting Volatility

Abstract

This paper presents the performance of 11 ARCH-type models each with four different distributions combined with ARMA specifications in conditional mean in estimating and forecasting the volatility of IMKB 100 stock indices, using daily data over a 9 years period. The results suggest that fractionally integrated asymmetric models outperform the non-FI versions and, using skewed-t and student-t distributions provide better fit to the data for almost every model in estimating volatility. In forecasting volatility a clear improvement is not observed by altering a specific model component or distribution

Keywords

• GARCH, EGARCH, GJR, APARCH, IGARCH, FIGARCH, FIAPARCH, FIEGARCH, HYGARCH, ARMA, GED, Skewed-t, Ox, G@RCH

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