Research Article
Year 2017,
, 163 - 181, 31.05.2017
Abstract
Autoregressive conditional heteroskedasticity models are found in consequence of
heteroskedasticity problem in financial time series. In this context, symmetric and asymmetric models
are applied. In this study, the most appropriate autoregressive conditional heteroskedasticity model is
researched in Turkey’s gold market index. In the scope of study, daily closing prices data of gold
market index between the date of 07.27.1995 – 07.27.2016 are used. The most appropriate model for
gold market index volatility is EGARCH (1,1). There is no leverage effect in this model, but positive
shocks are the result of more volatility than negative shocks.
References
- Awartani, B. ve Corradi, V. (2005) “Predicting the Volatility of the S&P-500 Stock Index via GARCH models: the Role of Asymmetries”, International Journal of Forecasting, 21: 167-183.
- Bekaert, G. ve Wu, G. (2000) “Asymmetric Volatility and Risk in Equity”, The Review of Financial Studies, 13(1): 1-42.
- Black, F. (1976) “Studies of Stock Price Volatility Changes. Proceedings of the American Statistical Association”, Business and Economic Statistics Section, 177-181.
- Bollerslev, T. (1986) “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31: 307-327.
- Borsa İstanbul (2016a) http://www.borsaistanbul.com/urunler-ve-piyasalar/piyasalar/kiymetli-madenler-ve-kiymetli-taslar-piyasasi/kiymetli-madenler-piyasasi, (23.09.2016).
- Borsa İstanbul (2016b) http://www.borsaistanbul.com/urunler-ve-piyasalar/piyasalar/kiymetli-madenler-ve-kiymetli-taslar-piyasasi, (23.09.2016).
- Çağlayan, E. ve Dayıoğlu, T. (2009) “Döviz Kuru Getiri Volatilitesinin Koşullu Değişen Varyans Modelleri ile Öngörüsü”, Ekonometri ve İstatistik, 9: 1-16.
- Ding, Z., Granger, C.W.J. ve Engle, R.F. (1993) “A Long Memory Property Of Stock Market Returns And A New Model”, Journal of Empirical Finance,1(1): 83-106.
- Emenike, K. (2010) “Modelling Stock Returns Volatility In Nigeria Using GARCH Models”, Munich Personal RePEc Archive, 23432: 1-17.
- Engle, R. (1982) “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation”, Econometrica, 50(4): 987-1007.
- Engle, R. ve Ng, V. (1993) “Measuring and Testing the Impact of News of Volatility”, The Journal of Finance, 48(5): 1749-1778.
- Fabozzi, F. J., Tunaru, R. ve Wu, T. (2004) “Modeling Volatility for Chinese Equity Markets”, Annals of Economics and Finance, 5: 79-92.
- Karabacak, M., Meçik, O. ve Genç, E. (2014) “Koşullu Değişen Varyans Modelleri ile BİST 100 Endeks Getirisi ve Altın Getiri Serisi Volatilitesinin Tahmini”, Uluslararası Alanya İşletme Fakültesi Dergisi, 6(1): 79-90.
- Liu, H.C ve Hungi J.C. (2010) “Forecasting S&P-100 Stock Index Volatility: The Role of Volatility Asymmetry and Distributional Assumption in GARCH Models”, Expert Systems with Applications, 37: 4928-4934.
- Nelson, D.B. (1991) “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, 59: 347-370.
- Padungsaksawasdi, C., Daigler, R. (2014) “The Return-Implied Volatility Relation for Commodity ETFs”, Journal of Future Markets, 34(3): 261-281.
- Parvaresh, M. ve Bavaghar, M. (2012) “Forecasting Volatility in Tehran Stock Market with GARCH Models”, Journal of Basic and Applied Scientific Research, 2(1): 150-155.
- Sevüktekin, M. ve Nargeleçekenler, M. (2006) “İstanbul Menkul Kıymetler Borsasında Getiri Volatilitesinin Modellenmesi ve Önraporlanması”, Ankara Üniversitesi SBF Dergisi, 61(4): 243-265.
- Shamiri, A. ve Isa, Z. (2009) “Modeling and Forecasting Volatility of the Malaysian Stock Market”, Journal of Mathematics and Statistics, 5: 234-240.
- Zakoian, J.M. (1994) “Threshold Heteroskedasticity Models”, Journal of Economic Dynamics and Control, 15: 931-955.
KOŞULLU DEĞİŞEN VARYANS MODELLERİ İLE TÜRKİYE ALTIN PİYASASI ENDEKSİ VOLATİLİTELERİNİN TAHMİN EDİLMESİ
Abstract
Finansal zaman
serilerinde görülen değişen varyans sorununun sonucu olarak otoregresif koşullu
değişen varyans modelleri bulunmuştur. Bu kapsamda simetrik ve asimetrik
modeller uygulanmıştır. Bu çalışmada, Türkiye’de altın piyasası endeksi volatiliteleri
için en uygun koşullu değişen varyans modeli araştırılmıştır. Çalışma kapsamında 27.07.1995 -
27.07.2016 tarihleri arasında altın piyasası endeksinin günlük kapanış
verilerinden elde edilen getiriler kullanılmıştır. Altın piyasası endeksi
volatiliteleri için en uygun değişen varyans modeli olarak EGARCH (1,1) modeli
bulunmuştur. Söz konusu modelde kaldıraç etkisi bulunmamış, ancak pozitif
şokların negatif şoklara göre volatiliteyi daha fazla artırdığı sonucuna
ulaşılmıştır.
References
- Awartani, B. ve Corradi, V. (2005) “Predicting the Volatility of the S&P-500 Stock Index via GARCH models: the Role of Asymmetries”, International Journal of Forecasting, 21: 167-183.
- Bekaert, G. ve Wu, G. (2000) “Asymmetric Volatility and Risk in Equity”, The Review of Financial Studies, 13(1): 1-42.
- Black, F. (1976) “Studies of Stock Price Volatility Changes. Proceedings of the American Statistical Association”, Business and Economic Statistics Section, 177-181.
- Bollerslev, T. (1986) “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31: 307-327.
- Borsa İstanbul (2016a) http://www.borsaistanbul.com/urunler-ve-piyasalar/piyasalar/kiymetli-madenler-ve-kiymetli-taslar-piyasasi/kiymetli-madenler-piyasasi, (23.09.2016).
- Borsa İstanbul (2016b) http://www.borsaistanbul.com/urunler-ve-piyasalar/piyasalar/kiymetli-madenler-ve-kiymetli-taslar-piyasasi, (23.09.2016).
- Çağlayan, E. ve Dayıoğlu, T. (2009) “Döviz Kuru Getiri Volatilitesinin Koşullu Değişen Varyans Modelleri ile Öngörüsü”, Ekonometri ve İstatistik, 9: 1-16.
- Ding, Z., Granger, C.W.J. ve Engle, R.F. (1993) “A Long Memory Property Of Stock Market Returns And A New Model”, Journal of Empirical Finance,1(1): 83-106.
- Emenike, K. (2010) “Modelling Stock Returns Volatility In Nigeria Using GARCH Models”, Munich Personal RePEc Archive, 23432: 1-17.
- Engle, R. (1982) “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation”, Econometrica, 50(4): 987-1007.
- Engle, R. ve Ng, V. (1993) “Measuring and Testing the Impact of News of Volatility”, The Journal of Finance, 48(5): 1749-1778.
- Fabozzi, F. J., Tunaru, R. ve Wu, T. (2004) “Modeling Volatility for Chinese Equity Markets”, Annals of Economics and Finance, 5: 79-92.
- Karabacak, M., Meçik, O. ve Genç, E. (2014) “Koşullu Değişen Varyans Modelleri ile BİST 100 Endeks Getirisi ve Altın Getiri Serisi Volatilitesinin Tahmini”, Uluslararası Alanya İşletme Fakültesi Dergisi, 6(1): 79-90.
- Liu, H.C ve Hungi J.C. (2010) “Forecasting S&P-100 Stock Index Volatility: The Role of Volatility Asymmetry and Distributional Assumption in GARCH Models”, Expert Systems with Applications, 37: 4928-4934.
- Nelson, D.B. (1991) “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, 59: 347-370.
- Padungsaksawasdi, C., Daigler, R. (2014) “The Return-Implied Volatility Relation for Commodity ETFs”, Journal of Future Markets, 34(3): 261-281.
- Parvaresh, M. ve Bavaghar, M. (2012) “Forecasting Volatility in Tehran Stock Market with GARCH Models”, Journal of Basic and Applied Scientific Research, 2(1): 150-155.
- Sevüktekin, M. ve Nargeleçekenler, M. (2006) “İstanbul Menkul Kıymetler Borsasında Getiri Volatilitesinin Modellenmesi ve Önraporlanması”, Ankara Üniversitesi SBF Dergisi, 61(4): 243-265.
- Shamiri, A. ve Isa, Z. (2009) “Modeling and Forecasting Volatility of the Malaysian Stock Market”, Journal of Mathematics and Statistics, 5: 234-240.
- Zakoian, J.M. (1994) “Threshold Heteroskedasticity Models”, Journal of Economic Dynamics and Control, 15: 931-955.
There are 20 citations in total.
Details
| Journal Section | Articles |
|---|---|
| Authors | |
| Publication Date | May 31, 2017 |
| Published in Issue | Year 2017 |