Journals / Ekonomik ve Sosyal Araştırmalar Dergisi / 2018 / Cilt: 14 - Sayı: 1

TIME VARYING BETA: APPLICATION ON ISTANBUL STOCK EXCHANGE BANKING SECTOR

ZAMANLA DEĞİŞEN BETA: BORSA İSTANBUL BANKACILIK SEKTÖRÜ UYGULAMASI

Pages
51–66
Publication Date
2022-10-11
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Multivariate Generalized Autoregressive Conditional Heteroscedasitcity Model (GARCH-BEKK) is employed to observe time varying betas of 12 banks traded on Borsa Istanbul for the period between December 2001 and February 2017 and results show that beta has time varying aspect.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study we aimed to observe time varying aspects and factors may affect the time variation of banking firms which are traded on Borsa İstanbul.
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Key Findings & Data:Multivariate Generalized Autoregressive Conditional Heteroscedasitcity Model (GARCH-BEKK) is employed to observe time varying betas of 12 banks traded on Borsa Istanbul for the period between December 2001 and February 2017 and results show that beta has time varying aspect.
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Conclusion & Impact:According to the results, there is a relationship between benchmark treasury bond yields and beta is closing to 1 in time.
🔬Design:Modeling & Simulation
📍Scope:2001 Verileri
⚙️Variables:Domain Parameters

Abstract

In this study we aimed to observe time varying aspects and factors may affect the time variation of banking firms which are traded on Borsa İstanbul. Multivariate Generalized Autoregressive Conditional Heteroscedasitcity Model (GARCH-BEKK) is employed to observe time varying betas of 12 banks traded on Borsa Istanbul for the period between December 2001 and February 2017 and results show that beta has time varying aspect. In the second step of the study, in order to detect the systematic risk factors that may effect the time varying beta and to present the trend, regression analysis is employed. According to the results, there is a relationship between benchmark treasury bond yields and beta is closing to 1 in time.

Özet

Çalışmada Borsa İstanbul’da işlem gören bankaların betalarının zamanla değişiminin gözlenmesi ve değişimi açıklayan faktörlerin belirlenmesi amaçlanmıştır. Aralık 2001 ve Şubat 2017 arasında Borsa İstanbul 100 (BİST100) endeksinde işlem gören 12 adet bankanın zamanla değişen betaları, günlük veriler kullanılarak Çok Değişkenli Genelleştirilmiş Otoregresif Koşullu Değişen Varyans-BEKK (BEKKGARCH) analizi yardımıyla hesaplanmış ve betanın zamanla değişimi gözlenmiştir. Çalışmanın ikinci aşamasında betanın değişimini etkileyebilecek sistematik risk faktörlerin ve eğilimin belirlenmesi amacıyla çoklu regresyon analizi uygulanmıştır. Sonuçlara göre bankacılık sektöründe beta zamanla 1’e yaklaşmakta ve gösterge tahvil faiz oranı zamanla değişimi açıklayan faktör olarak ortaya çıkmaktadır.

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Cite This Article

GÜMRAH, Ü., KONUK, S. (2022). TIME VARYING BETA: APPLICATION ON ISTANBUL STOCK EXCHANGE BANKING SECTOR. Ekonomik ve Sosyal Araştırmalar Dergisi, Cilt: 14 - Sayı: 1, 51–66. https://ajindex.com/articles/499564

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References (14)

  1. Abiyev, Vasif (2015). “Time-Varying Beta Risk and Its Modeling Techniques for Turkish Industry Portfolios”. Iktisat Isletme ve Finans, 30(352), 79-108.
  2. Baba, Y., Robert F. Engle, D. F. Kraft, ve K. F. Kroner (1990). “Multivariate Simultaneous Generalized ARCH”. Department of Economics, University of California at San Diego, Working Paper. Baesel, Jerome B (1974). “On the Assessment of Risk: Some Further Considerations”. The Journal of Finance, 29(5), 1491-1494.
  3. Blume, Marshall E. (1971). “On the assessment of risk”. The Journal of Finance, 26(1), 1-10.
  4. Bodurtha, James N., ve Nelson C. Mark (1991). “Testing the CAPM with Time‐Varying Risks and Returns”. The Journal of Finance, 46(4), 1485-1505.
  5. Bollerslev, Tim (1992). “ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence”. Journal of Econometrics, 52(1-2), 5-59.
  6. Bollerslev, Tim (1986). “Generalized Autoregressive Conditional Heteroskedasticity”. Journal of Econometrics, 31(3), 307-327.
  7. Bollerslev, Tim (1990). “Modelling the Coherence in Short-run Nominal Exchange Rates: a Multivariate Generalized ARCH Model”. The Review of Economics and Statistics, 72(3), 498-505.
  8. Bollerslev, Tim, Robert F. Engle, ve Jeffrey M. Wooldridge (1988). “A Capital Asset Pricing Model with TimeVarying Covariances”. Journal of Political Economy, 96(1), 116-131.
  9. Bos, Theodore, ve Paul Newbold (1984). “An Empirical İnvestigation of the Possibility of Stochastic Systematic Risk in the Market Model”. Journal of Business, 57(1), 35-41.
  10. Bos, Theodore, ve Thomas A. Fetherston (1992). “Market Model Nonstationarity in the Korean Stock Market”. Pacific-Basin Capital Markets Research 3, 287-301.
  11. Braun, Phillip A., Daniel B. Nelson, ve Alain M. Sunier (1995). “Good News, Bad News, Volatility, and Betas”. The Journal of Finance, 50(5), 1575-1603.
  12. Brooks, Robert D., Robert W. Faff, ve Michael D. McKenzie (1998). “Time‐Varying Beta Risk of Australian İndustry Portfolios: A Comparison of Modelling Techniques”. Australian Journal of Management, 23(1), 1-22.
  13. Büberkökü, Önder, ve Simge Tüzün Şahmaroğlu (2016). “Beta Katsayılarındaki Değişimin Açıklanmasında İşlem Hacminin Etkisinin İncelenmesi: Banka Hisselerine Dayalı Bir Analiz”. İşletme Bilimi Dergisi, 4(1), 1-28.
  14. Chambers, Donald R., Mark J. P. Anson, Keith H. Black, ve Hossein Kazemi (2009). Alternative Investments. New Jersey: Wiley & Sons. Chen, Son-Nan (1981). “Beta Nonstationarity, Portfolio Residual Risk and Diversification”. Journal of Financial and Quantitative Analysis