BIST Sektör Endekslerinin Zamanla Değişen Beta Katsayıları

Year 2022, Issue: 96, 135 - 150, 24.10.2022

Erkan Ustaoğlu

https://doi.org/10.25095/mufad.1143257

Abstract

Çalışmanın amacı BIST’te yer alan 21 adet sektör endeksinin zamanla değişen beta katsayılarının
hesaplanmasıdır. Analizler sonucunda 21 adet sektör endeksinin beta katsayılarının zamanla değişen bir yapıda
olduğu gözlenmiştir. Aynı zamanda zamanla değişen tüm sektör betalarının ortalamaya dönme eğilimde olduğu
belirlenmiştir. Çalışmada en oynak beta katsayı XFINK endeksine ait iken, en az oynak beta katsayısı XSPOR
endeksine ait olduğu tespit edilmiştir. Ayrıca çalışmada ortalama olarak en düşük beta katsayısı 0.490 ile
XSPOR endeksine ait iken en yüksek beta katsayısı ise 1.248 ile XBANK endeksine ait olduğu saptanmıştır.
Çalışmada, sektör endekslerinin betası zamanla benzer değişimlere sahip olduğu tespit edilmiştir.

Keywords

Zamanla Değişen Beta, Beta Katsayısı, DCC-GARCH, Sistematik Risk, BIST.

Time-Varying Beta Coefficients of BIST Sector Indices

Abstract

The aim of the study is to calculate the time-varying beta coefficients of 21 sector indices in BIST. As a result of the analysis, it has been determined that the beta coefficients of 21 sector indices have a structure that changes over time. At the same time, it has been found that all sector time-varying betas tend to mean reversion. In the study determined that the most volatile beta coefficient belongs to the XBANK index, while the least volatile beta coefficient belongs to the XILTM index. In addition, in the study determined that the lowest beta coefficient with 0.441 belonged to the XTCRT index, while the highest beta coefficient was found to belong to the XBANK index with 0.868. In the study, it has been determined that the beta of other sector indices has similar changes over time, except for the XELKT, XILTM, and XSPOR indices.

Keywords

Time-Varying Beta, Beta Coefficient, DCC-GARCH, Systematic Risk, BIST

References

• Abiyev, Vasif (2015), “Time-Varying Beta Risk and Its Modeling Techniques for Turkish Industry Portfolios”, İktisat İsletme ve Finans, 30(352), pp. 79-108. https://doi.org/10.3848/iif.2015.349.4370.
• Aksoy, Esra – Akçakanat, Özen – Çelik, İsmail (2019), “The Long Memory of Time-Varying Beta, An Application in Turkish Banking Sector”, 23rd International Finance Symposium, pp. 816-826
• Alp, Murat – İskenderoğlu, Ömer – Evci, Samet (2013), “Estimation of The Stock Returns: An Emprical Analysis in Istanbul Stock Exchange”, Finans Politik ve Ekonomik Yorumlar Dergisi, 50(581), pp. 27-36.
• Altınsoy, Gözde – Erol, Işıl – Yıldırak, S. Kasırga (2010), “Time-Varying Beta Risk of Turkish Real Estate Investment Trusts”, METU Studies in Development 37.2, pp. 83- 1147.
• Andersen, Torben G. – Bollerslev, Tim (1998), “Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts”, International Economic Review, 39(4), pp. 885-905. https://doi.org/10.2307/2527343.
• Andersen, Torben. G – Bollerslev, Tim – Diebold, Francis X. – Wu, Ginger (2006), “Realized Beta: Persistence and Predictability, Advances in Econometrics”, 2(05), pp. 1–39. https://doi.org/10.1016/S0731-9053(05)20020-8.
• Ayub, Uzman – Kausar, Samaila – Noreen, Umara – Zakaria, Muhammad – Jadoon, Imran Abbas (2020), “Downside Risk-Based Six-Factor Capital Asset Pricing Model (CAPM): A New Paradigm in Asset Pricing”, Sustainability, 12(17), 6756. https://doi.org/10.3390/su12176756.
• Bai, Jushab – Perron, Pierre (2003), “Computation and Analysis of Multiple Structural Change Models”, Journal of Applied Econometrics, 18(1), pp. 1-22. https://doi.org/10.1002/jae.659.
• Bayrakdaroğlu, Ali (2018), Finansın Temel Teorileri, Editör Aysel Gündoğdu, Beta Basım Yayım, Istanbul.
• Bodie, Zvi - Kane Alex - Marcus, Alan J. (2008), Investments, 8th Edition, McGraw-Hill, New York.
• Bolak, Mehmet (2016), Risk Yönetimi, 2th Edition, Birsen Yayınevi, Istanbul.
• Bollerslev, Tim - Engle, Robert F. – Wooldridge, Jeffrey M. (1988), “A Capital Asset Pricing Model With Time-Varying Covariances” Journal of Political Economy, 96(1), pp. 116-131.
• Brooks, Robert D. – Faff, Robert W. – McKenzie, Michael D. (1998), “Time-varying Beta Risk of Australian Industry Portfolios: A Comparison of Modeling Techniques”, Australian Journal of Management, 23(1), pp. 1–22. https://doi.org/10.1177/031289629802300101.
• Büberkökü, Önder – Şahmaranoğlu, Simge Tüzün (2016), “Examining the Role of Trading Volume on Beta Coefficients: Evidence from Turkish Banks”, The Journal of Business Science, 4(1), pp. 1-28. https://doi.org/10.22139/ibd.73036.
• Choudhry, Taufiq – Lu, Lin – Peng, Ke (2010), “Time-Varying Beta and the Asian Financial Crisis: Evidence from the Asian Industrial Sectors”, Japan and the World Economy, 22(4), pp. 228-234. https://doi.org/10.1016/j.japwor.2010.06.003.
• Choudhry, Taufiq – Wu, Hao (2008), “Forecasting Ability of GARCH vs Kalman Filter Method: Evidence from Daily UK Time-Varying Beta”, Journal of Forecasting 27(8), pp. 670-689. https:// doi.org/10.1002/for.1096.
• Choudhry, Taufiq – Wu, Hao (2009), “Forecasting the Weekly Time-Varying Beta of UK Firms: Comparison between GARCH Models vs Kalman Filter Method”, The European Journal of Finance 15(4), pp. 437-44. https://doi.org/10.1080/13518470802604499.
• Ciner, Cetin (2015), “Time Variation in Systematic Risk, Returns and Trading Volume: Evidence from Precious Metals Mining Stocks”, International Review of Financial Analysis, 41, pp. 277-283. https://doi.org/10.1016/j.irfa.2015.01.019.
• Darolles, Serge – Francq, Christian – Laurent, Sébastien (2018), “Asymptotics of Cholesky GARCH Models and Time-Varying Conditional Betas”, Journal of Econometrics, 204(2), pp. 223-247. https://doi.org/10.1016/j.jeconom.2018.02.003.
• Engle, Charles – Rodrigues, Anthony P. (1989), “Tests of International CAPM with Time-Varying Covariances”, Journal of Applied Econometrics, 4, pp. 119–138. https://doi.org/10.1002/jae.3950040203.
• Engle, Robert (2002), “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models” Journal of Business & Economic Statistics, 20(3), pp. 339-350. https://doi.org/10.1198/073500102288618487.
• Güçlü, Fatih (2019), “Time-Varying Beta of the Participation 30 Index”, International Journal of Economics and Administrative Studies, pp. 115-126. https://doi.org/10.18092/ulikidince.515150.
• Gümrah, Ümit – Konuk, Serhat (2018), “Time Varying Beta: Application on Istanbul Stock Exchange Banking Sector”, The International Journal of Economic and Social Research, 14(1), pp. 51-66.
• Hajizadeh, Ehsan – Seifi, Abbas – Fazel-Zarandi Mohhamd Hossein – Turksen, Ismail Burhan (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.
• İskenderoğlu, Ömer (2012), “Estimation of the Beta Coeicients: An Empirical Analysis in Istanbul Stock Exchange”, Ege Academic Review, 12(1), pp. 67-76. Karan, Mehmet Baha (2013), Yatırım Analizi ve Portföy Yönetimi, 4th Edition, Gazi Kitabevi, Ankara.
• Köseoğlu, Sinem Derindere – Gökbulut, Rasim İlker (2012), “Market Risk of Turkish Sectors between 2001 and 2011: A Bivariate GARCH Approach”, African Journal of Business Management, 6(23), pp. 6948-6957. https://doi.org/10.5897/AJBM12.168.
• Ng, Lilian (1991), “Tests of the CAPM with Time-Varying Covariances: A Multivariate GARCH Approach”, Journal of Finance, 46, pp. 1507–1521. https://doi.org/10.1111/j.1540-6261.1991.tb04628.x.
• Tuna, Gülfen – Tuna, Vedat Ender (2013), “Systematic Risk on Istanbul Stock Exchange: Traditional Beta Coefficient Versus Downside Beta Coefficient”, Journal of Business Research - Turk, 5(1), pp. 189-205.
• Zhang, Yuanyuan – Choudhry, Taufiq (2017), “Forecasting the Daily Time‐Varying Beta of European Banks during the Crisis Period: Comparison between GARCH Models and the Kalman Filter”, Journal of Forecasting, 36(8), pp. 956-973. https://doi.org/10.1002/for.2442.