Borsa İstanbul’da Volatilite Öngörümlemesi: Katılım ve Konvansiyonel Endekslerin Karşılaştırmalı Analizi
Yıl 2025,
Cilt: 43 Sayı: 3, 449 - 468, 26.09.2025
Erdi Bayram
,
Rabia Aktaş
,
Koray Kayalıdere
Öz
Volatilite yatırım kararları ve portföy yönetimi için temel girdilerden biridir. Volatilite modellemesi ve öngörümlemesi finansal piyasalarda risk analizi ve yönetimi açısından oldukça önemlidir. Bu çalışmada, Borsa İstanbul’daki dört farklı endeksin volatilite özelikleri GARCH tipi modellerle incelenmiştir. Araştırmanın temel motivasyonu katılım ve konvansiyonel endekslerin volatilitesini modellemek ve ilgili endekslerde öngörümleme yapmaktır. Öncelikle 809 günlük getiri verisiyle GARCH modellerinin parametreleri tahmin edilmiş ve endeks getiri serilerinin volatilite özelikleri incelenmiştir. Daha sonra örneklem dışı 57 günlük tahmin serileri elde edilmiş ve günlük bazda gerçekleşmiş volatilite hesaplanmıştır. Son olarak modellerin tahmin serilerinin gün içi getirilerle hesaplanan gerçekleşmiş volatiliteyi öngörümleme gücü analiz edilmiştir. GARCH modellerinin öngörümleme gücünü incelemek üzere RMSE (Root Mean Squared Error), QLIKE (Quasi-Likelihood Loss) ve R-squared (R2) ölçütleri hesaplanmıştır. Sonuçlar katılım endekslerinin, özelikle Katılım 30 endeksinin geleneksel endekslere kıyasla volatilite karakteristiğinin farklı olduğunu göstermektedir. Ayrıca EGARCH ve TGARCH asimetrik modellerin kullanılan diğer modellere göre gerçekleşen volatiliteyi öngörümleme açısından daha başarılı tahminler ürettiği izlenmektedir.
Kaynakça
-
Abdalla, S. Z. S., & Winker, P. (2012). Modelling stock market volatility using univariate GARCH models: Evidence from Sudan and Egypt. International Journal of Economics and Finance, 4(8), 161-176. https://doi.org/10.5539/ijef.v4n8p161
-
Ahmed, F., Awais, I., & Pervaiz, A. (2016). Modeling volatility for conventional and Islamic stock market indices. Journal of Independent Studies and Research Management and Social Sciences & Economics, 14(1), 1-12. https://doi.org/10.31384/jisrmsse/2016.14.1.1
-
Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905. https://doi.org/10.2307/2527343
-
Asutay, M., Wang, Y., & Avdukic, A. (2022). Examining the performance of Islamic and conventional stock indices: A comparative analysis. Asia-Pacific Financial Markets, 29(2), 327-355. https://doi.org/10.1007/s10690-021-09351-7
-
Bildirici, M., & Ersin, Ö. Ö. (2009). Improving forecasts of GARCH family models with the artificial neural networks: an application to the daily returns in Istanbul Stock Exchange. Expert Systems with Applications, 36(4), 7355–7362. https://doi.org/10.1016/j.eswa.2008.09.051
-
Bolgün, E., & Akçay, B. (2009). Risk yönetimi (3rd ed.). Scala Publishing.
-
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
-
Borsa Istanbul. (2024). BIST Stock Indices-Participation. Retrieved 10 June 2024 from https://borsaistanbul.com/en/index/1/3/participation
-
Chai, F., Li, Y., Zhang, X., & Chen, Z. (2023). Daily semiparametric GARCH model estimation using intraday high-frequency data. Symmetry, 15(4), 908. https://doi.org/10.3390/sym15040908
-
Christensen, K., Siggaard, M., & Veliyev, B. (2023). A machine learning approach to volatility forecasting. Journal of Financial Econometrics, 21(5), 1680–1727. https://doi.org/10.1093/jjfinec/nbac020
-
Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83–106. https://doi.org/10.1016/0927-5398(93)90006-D
-
Engle, R. F., Lilien, D. M., & Robins, R. P. (1987). Estimating time varying risk premia in the term structure: the ARCH-M model. Econometrica, 55(2), 391-407. https://doi.org/10.2307/1913242
-
Hajizadeh, E., Seifi, A., Fazel Zarandi, M. H., & Turksen, I. B. (2012). A hybrid modeling approach for forecasting the volatility of S&P 500 index return. Expert Systems with Applications, 39(1), 431–436. https://doi.org/10.1016/j.eswa.2011.07.033
-
Hattori, T. (2020). A forecast comparison of volatility models using realized volatility: Evidence from the bitcoin market. Applied Economics Letters, 27(7), 591–595. https://doi.org/10.1080/13504851.2019.1644421
Herrera, A. M., Hu, L., & Pastor, D. (2018). Forecasting crude oil price volatility. International Journal of Forecasting, 34(4), 622–635. https://doi.org/10.1016/j.ijforecast.2018.04.007
-
Kashyap, S. (2023). Review on volatility and return analysis including emerging developments: Evidence from stock market empirics. Journal of Modelling in Management, 18(3), 756–816. https://doi.org/10.1108/JM2-10-2021-0249
-
Katsiampa, P. (2017). Volatility estimation for bitcoin: A comparison of GARCH models. Economics Letters, 158, 3–6. https://doi.org/10.1016/j.econlet.2017.06.023
-
Kayalıdere, K. (2013). Volatilite tahmin modelleri ve performanslarının ölçümü. Gazi Publishing.
-
Kristjanpoller, W., & Minutolo, M. C. (2015). Gold price volatility: A forecasting approach using the artificial neural network–GARCH model. Expert Systems with Applications, 42(20), 7245–7251. https://doi.org/10.1016/j.eswa.2015.04.058
-
Liu, M., Lee, C.-C., & Choo, W.-C. (2021). The role of high-frequency data in volatility forecasting: Evidence from the China stock market. Applied Economics, 53(22), 2500–2526. https://doi.org/10.1080/00036846.2020.1862747
-
Lyocsa, S., Molnar, P., & Vyrost, T. (2021). Stock market volatility forecasting: Do we need high-frequency data? International Journal of Forecasting, 37(3), 1092–1110. https://doi.org/10.1016/j.ijforecast.2020.12.001
-
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370. https://doi.org/10.2307/2938260
-
Nilsson, C. (2017). Forecasting Swedish stock market volatility and value-at-risk: a comparison of EWMA and GARCH models. Lund University School of Economics and Management. Master Essay II.
-
Ou, P., & Wang, H. (2010). Financial volatility forecasting by least square support vector machine based on GARCH, EGARCH and GJR Models: Evidence from ASEAN stock markets. International Journal of Economics and Finance, 2(1), 51-64. https://doi.org/10.5539/ijef.v2n1p51
-
Pilbeam, K., & Langeland, K. N. (2015). Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts. International Economics and Economic Policy, 12(1), 127–142. https://doi.org/10.1007/s10368-014-0289-4
-
Poon, S.-H. (2005). A practical guide to forecasting financial market volatility. John Wiley & Sons, Ltd.
Rahimikia, E., & Poon, S.-H. (2020). Machine learning for realised volatility forecasting. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3707796
-
Sharma, P., & Vipul. (2015). Forecasting stock index volatility with GARCH models: International evidence. Studies in Economics and Finance, 32(4), 445–463. https://doi.org/10.1108/SEF-11-2014-0212
-
Xiao L., Boasson V., Shishlenin S., & Makushina V. (2018). Volatility forecasting: Combinations of realized volatility measures and forecasting models. Applied Economics, 50(13), 1428-1441. https://doi.org/10.1080/00036846.2017.1363863
-
Yıldız, S. B. (2020). Performance analysis of Turkey’s participation and conventional indices using TOPSIS method. Journal of Islamic Accounting and Business Research. 11(7), 1403-1416. http://doi.org/10.1108/JIABR-08-2018-0123
-
Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. https://doi.org/10.1016/0165-1889(94)90039-6
-
Zhang, C., Zhang, Y., Cucuringu, M., & Qian, Z. (2024). Volatility forecasting with machine learning and intraday commonality. Journal of Financial Econometrics, 22(2), 492-530. https://doi.org/10.1093/jjfinec/nbad005
Volatility Forecasting in Borsa Istanbul: A Comparative Analysis of Participation and Conventional Indices
Yıl 2025,
Cilt: 43 Sayı: 3, 449 - 468, 26.09.2025
Erdi Bayram
,
Rabia Aktaş
,
Koray Kayalıdere
Öz
Volatility is a key input for investment decisions and portfolio management. Volatility modeling and forecasting are essential for risk analysis and risk management in financial markets. In this study, we investigate volatility characteristics of the four indices in Borsa Istanbul (Istanbul Stock Exchange) using GARCH family models. The primary motivation of this research is to model and forecast the volatility of participation and conventional indices. First, we estimate the model parameters using 809 days of data and explain the volatility features of the index return series. Second, we generate 57 days of forecast series and calculate realized volatility in the out-of-sample period. Finally, we analyze the predictive power of the forecast series of the models in relation to realized volatility, calculated using intraday returns. We use measures such as RMSE (Root Mean Squared Error), QLIKE (Quasi-Likelihood Loss), and R-squared (R²) to evaluate the predictability of GARCH models. The results reveal that participation indices exhibit distinct volatility characteristics and that asymmetric models—especially EGARCH and TGARCH—produce satisfactory predictions.
Kaynakça
-
Abdalla, S. Z. S., & Winker, P. (2012). Modelling stock market volatility using univariate GARCH models: Evidence from Sudan and Egypt. International Journal of Economics and Finance, 4(8), 161-176. https://doi.org/10.5539/ijef.v4n8p161
-
Ahmed, F., Awais, I., & Pervaiz, A. (2016). Modeling volatility for conventional and Islamic stock market indices. Journal of Independent Studies and Research Management and Social Sciences & Economics, 14(1), 1-12. https://doi.org/10.31384/jisrmsse/2016.14.1.1
-
Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905. https://doi.org/10.2307/2527343
-
Asutay, M., Wang, Y., & Avdukic, A. (2022). Examining the performance of Islamic and conventional stock indices: A comparative analysis. Asia-Pacific Financial Markets, 29(2), 327-355. https://doi.org/10.1007/s10690-021-09351-7
-
Bildirici, M., & Ersin, Ö. Ö. (2009). Improving forecasts of GARCH family models with the artificial neural networks: an application to the daily returns in Istanbul Stock Exchange. Expert Systems with Applications, 36(4), 7355–7362. https://doi.org/10.1016/j.eswa.2008.09.051
-
Bolgün, E., & Akçay, B. (2009). Risk yönetimi (3rd ed.). Scala Publishing.
-
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
-
Borsa Istanbul. (2024). BIST Stock Indices-Participation. Retrieved 10 June 2024 from https://borsaistanbul.com/en/index/1/3/participation
-
Chai, F., Li, Y., Zhang, X., & Chen, Z. (2023). Daily semiparametric GARCH model estimation using intraday high-frequency data. Symmetry, 15(4), 908. https://doi.org/10.3390/sym15040908
-
Christensen, K., Siggaard, M., & Veliyev, B. (2023). A machine learning approach to volatility forecasting. Journal of Financial Econometrics, 21(5), 1680–1727. https://doi.org/10.1093/jjfinec/nbac020
-
Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83–106. https://doi.org/10.1016/0927-5398(93)90006-D
-
Engle, R. F., Lilien, D. M., & Robins, R. P. (1987). Estimating time varying risk premia in the term structure: the ARCH-M model. Econometrica, 55(2), 391-407. https://doi.org/10.2307/1913242
-
Hajizadeh, E., Seifi, A., Fazel Zarandi, M. H., & Turksen, I. B. (2012). A hybrid modeling approach for forecasting the volatility of S&P 500 index return. Expert Systems with Applications, 39(1), 431–436. https://doi.org/10.1016/j.eswa.2011.07.033
-
Hattori, T. (2020). A forecast comparison of volatility models using realized volatility: Evidence from the bitcoin market. Applied Economics Letters, 27(7), 591–595. https://doi.org/10.1080/13504851.2019.1644421
Herrera, A. M., Hu, L., & Pastor, D. (2018). Forecasting crude oil price volatility. International Journal of Forecasting, 34(4), 622–635. https://doi.org/10.1016/j.ijforecast.2018.04.007
-
Kashyap, S. (2023). Review on volatility and return analysis including emerging developments: Evidence from stock market empirics. Journal of Modelling in Management, 18(3), 756–816. https://doi.org/10.1108/JM2-10-2021-0249
-
Katsiampa, P. (2017). Volatility estimation for bitcoin: A comparison of GARCH models. Economics Letters, 158, 3–6. https://doi.org/10.1016/j.econlet.2017.06.023
-
Kayalıdere, K. (2013). Volatilite tahmin modelleri ve performanslarının ölçümü. Gazi Publishing.
-
Kristjanpoller, W., & Minutolo, M. C. (2015). Gold price volatility: A forecasting approach using the artificial neural network–GARCH model. Expert Systems with Applications, 42(20), 7245–7251. https://doi.org/10.1016/j.eswa.2015.04.058
-
Liu, M., Lee, C.-C., & Choo, W.-C. (2021). The role of high-frequency data in volatility forecasting: Evidence from the China stock market. Applied Economics, 53(22), 2500–2526. https://doi.org/10.1080/00036846.2020.1862747
-
Lyocsa, S., Molnar, P., & Vyrost, T. (2021). Stock market volatility forecasting: Do we need high-frequency data? International Journal of Forecasting, 37(3), 1092–1110. https://doi.org/10.1016/j.ijforecast.2020.12.001
-
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370. https://doi.org/10.2307/2938260
-
Nilsson, C. (2017). Forecasting Swedish stock market volatility and value-at-risk: a comparison of EWMA and GARCH models. Lund University School of Economics and Management. Master Essay II.
-
Ou, P., & Wang, H. (2010). Financial volatility forecasting by least square support vector machine based on GARCH, EGARCH and GJR Models: Evidence from ASEAN stock markets. International Journal of Economics and Finance, 2(1), 51-64. https://doi.org/10.5539/ijef.v2n1p51
-
Pilbeam, K., & Langeland, K. N. (2015). Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts. International Economics and Economic Policy, 12(1), 127–142. https://doi.org/10.1007/s10368-014-0289-4
-
Poon, S.-H. (2005). A practical guide to forecasting financial market volatility. John Wiley & Sons, Ltd.
Rahimikia, E., & Poon, S.-H. (2020). Machine learning for realised volatility forecasting. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3707796
-
Sharma, P., & Vipul. (2015). Forecasting stock index volatility with GARCH models: International evidence. Studies in Economics and Finance, 32(4), 445–463. https://doi.org/10.1108/SEF-11-2014-0212
-
Xiao L., Boasson V., Shishlenin S., & Makushina V. (2018). Volatility forecasting: Combinations of realized volatility measures and forecasting models. Applied Economics, 50(13), 1428-1441. https://doi.org/10.1080/00036846.2017.1363863
-
Yıldız, S. B. (2020). Performance analysis of Turkey’s participation and conventional indices using TOPSIS method. Journal of Islamic Accounting and Business Research. 11(7), 1403-1416. http://doi.org/10.1108/JIABR-08-2018-0123
-
Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. https://doi.org/10.1016/0165-1889(94)90039-6
-
Zhang, C., Zhang, Y., Cucuringu, M., & Qian, Z. (2024). Volatility forecasting with machine learning and intraday commonality. Journal of Financial Econometrics, 22(2), 492-530. https://doi.org/10.1093/jjfinec/nbad005