VOLATİLİTE MODELLERİNİN ÖNGÖRÜ PERFORMANSLARI: ARCH, GARCH VE SWARCH KARŞILAŞTIRMASI

Bu çalışmada alternatif volatilite modellerinin öngörü performansları karşılaştırılmıştır. İstanbul Menkul Kıymetler Borsası İMKB100 endeksi haftalık kapanış verileri kullanılarak getiri volatilitesi ARCH, GARCH ve SWARCH yöntemleriyle tahmin edilmiş ve bu tahminlere dayalı olarak öngörüler yapılmıştır. Öngörü performansları gerçekleşen volatilite baz alınarak çeşitli hata istatistikleriyle değerlendirilmiştir. Çalışma sonuçları SWARCH modellerinin ARCH ve GARCH modellerine göre daha az ısrarcılığa sahip olduğunu göstermektedir. Elde edilen sonuçlar dinamik ve kayan pencere yaklaşımlarına göre yapılan öngörüler açısından SWARCH modellerinin daha iyi sonuçlar verdiğini ortaya koymaktadır.

Anahtar Kelimeler:

Volatilite Modelleri, Öngörü Performansı, SWARCH

VOLATİLİTE MODELLERİNİN ÖNGÖRÜ PERFORMANSLARI: ARCH, GARCH VE SWARCH KARŞILAŞTIRMASI

Keywords:

Volatilite Modelleri, Öngörü Performansı, SWARCH,

PDF

___

Akgiray, V. (1989). Conditional Heteroskedasticity in Time Series of Stock Returns: Evidence and Forecasts. Journal of Business, 62 (1): 55- 80.
Akgün, İ., & Sayan, H. (2007). İMKB-30 Hisse Senedi Getirilerinde Volatilitenin Kısa ve Uzun Hafızalı Asimetrik Koşullu Değişen Varyans Modelleri ile Öngörüsü. [Forecasting Volatility in ISE-30 Stock Returns with Short and Long Memory Asymmetric Conditional Heteroskedasticity Models]. İşletme-Finans Dergisi, Ocak: 127-137.
Andersen, T.G., Bollerslev, T. & Lange, S. (1999). Forecasting Financial Market Volatility: Sample Frequency vis-a-vis Forecast Horizon. Journal of Empirical Finance, 6: 457-477.
Balaban, E. (1999). Forecasting Stock Market Volatility: Evidence from Turkey. The ISE Finance Award Series Volume: 1, International Conference in Economics at the Middle East Technical University in 1999.
Balaban, E. (2004). Comparative Forecasting Performance of Symmetric and Asymmetric Conditional Volatility Models of an Exchange Rates. Economics Letters, 83: 99-105.
Bautista, C.C. (2003). Stock Market Volatility in the Philippines. Applied Economics Letters, 10: 315-318.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31: 307 -327.
Chen, S.W. & Lin, L.J. (2000). Switching ARCH models of stock market volatility in Taiwan. Advances in Pacific Basin Business, Economics, and Finance, 4: 1-21.
Day, T.E. & Lewis, C.M. (1992). Stock Market Volatility and the Infırmation Content of Stock Index Options. Journal of Econometrics, 52: 267-287.
Degiannakis, S. (2004). Volatility forecasting: Evidence from a fractional integrated asymmetric power ARCH skewed-t Model. Applied Financial Economics, 14: 1333-1342.
Engle, R.F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4): 987-1008.
Erlandsson, U. (2000). Forecasting Swedish Interest Rate Volatility-A Regime Switching Approach. Master Thesis in Economics, Lund University, Lund Macroeconomic Studies, 2000: 5.
Figlewski, S. (1997). ForecastingVolatility (monograph). Financial Markets, Institutions, and Instruments 6 (1), 1997.
Fong, W.M. (1997). Volatility Persistence and Switching ARCH in Japanese Markets. Financial Engineering and the Japanese Markets, 4: 37-57.
Forte, G. & Manera, M. (2002). Forecasting Volatility in European Stock Markets with Non-Linear GARCH Models. (November 2002). FEEM Working Paper, No. 98.
Franses, P.H. , & Van Dijk, D. (1996). Forecasting Stock Market Volatility Using Non-Linear GARCH Models. Journal of Forecasting, 15: 229- 235.
Güloğlu, B. & Akman, A. (2007). Türkiye’de Döviz Kuru Oynaklığının SWARCH Yöntemiyle Analizi, [Analysis of Exchange Rate Volatility with SWARCH Method in Turkey]. Finans Politik& Ekonomik Yorumlar, 44 (512): 43-51.
Hansen, P.R., Lunde, A. & Nason, J. (2003). Choosing the Best Volatility Models: The Model Confidence Set Approach. Brown Universitiy, Department of Economics, Working Paper, No: 2003-05.
Hamilton, J.D. & Susmel, R. (1994). Autoregressive Conditional Heteroscedasticity and Changes in Regime. Journal of Econometrics, 64: 307-333.
Koopman, S.J., Jungbacker, B. & Hol, E. (2004). Forecasting daily variability of the S&P Stock Index Using Historical, Realized and Implied, Volatility Measurements. Tinbergen Institute Discussion Paper, TI 2004 - 016/4.
Lamoreux, C.G. & Lastrapes, W.D. (1990). Persistence in Variance, Structural Change, and the GARCH Model. Journal of Business and Economic Statistics, 68: 225-234.
Li, M.Y.L. & Lin, H.W.W (2003). Examining the Volatility of Taiwan Stock Index Returns via a Three-Volatility-Regime Markow Switching ARCH Model. Review of Quantitative Finance and Accounting, 21: 123-139
Mapa, D. (2003). A Range-Based Generalized AutoRegressive Conditional Heteroskedasticity Model for Forecasting Financial Volatility. The Philippine Review of Economics. XL (2): 73-90.