Mustafa SEVÜKTEKİN, Mehmet NARGELEÇEKENLER

İstanbul Menkul Kıymetler Borsasında Getiri Volatilitesinin Modellenmesi ve Önraporlanması

Özellikle yüksek frekanslı günlük finansal verileri modelleme basanları nedeniyle birçok araştırmacı tarafından büyük ilgi gören ARCH ve GARCH modelleri değişen varyansın sadece yatay kesit verisi problemi olmadığını aynı zamanda bir zaman serisi verisi problemi olduğunu göstermektedir. Dolayısıyla finansal zaman serisi verileri ile yapılan çalışmalarda doğrusal zaman serisi modelleri yerine doğrusal olmayan koşullu değişen varyans modellerinin kullanılması gerekmektedir. Bu çalışmada, IMKBİOO günlük getiri serisinin volatilitesinin önraporlaması için alternatif modellerin performansları değerlendirilmektedir. Öncelikle birim kök testleri, doğrusal olmayan modellerin teorik yapısı ve koşullu değişen varyans önraporlama modelleri kısaca tartışıldıktan sonra uygulama amacıyla MKB100 günlük getirilen alınmıştır. Getiri serisinin zaman serisi özellikleri incelendikten sonra serinin durağan olduğu sonucuna ulaşılmıştır. Daha sonra alternatif modeller içerisinden getiri serisi için en uygun modelin ARMA(1,2) olduğu bulunmuştur. IMKBİOO günlük getiri serisi için elde edilen expost önraporlama sonuçlarına göre alternatif ARCH ve GARCH modelleri içerisinden en uygun koşullu değişen varyans modelinin GARCH(1,1) olduğu belirlenmiştir. Bulunan bu sonuç menkul kıymetler borsası üzerine yapılan daha önceki çalışmaların büyük bir kısmını destekler niteliktedir.

Modeling and Forecasting of Return Volatility at İstanbul Stock Exchange

Due to their success in modeling high frequency daily financial data many researchers are interested in ARCH and GARCH models, which show that changing variance not only is a cross sectional data problem but also is a time series data problem. Consequently, financial time series data must be used in nonlinear conditional heteroscedastic models rather than linear time series models. This study evaluates the performance of alternative model for forecasting volatility of daily returns series in Istanbul Stock Index (ISE). After we shortly discuss unit root tests, theorical background of nonlinear time series, and conditional heteroscedastic forecasting, ISE daily return data is handled case-study. After examining time series property of return series, we conclude return series is stationary. Later among alternative models we found that the most appropriate model is ARMA(1,2) for return series. According to expost forecasting results, GARCH (1,1) model is the best appropriate model among alternative ARCH and GARCH models for ISE daily return series. This result supports many of the former studies on stock exchange.

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