THE LONG MEMORY BEHAVIOR IN TIME-VARYING BETA: AN EMPIRICAL APPLICATION ON BIST

Yıl 2020, , 200 - 210, 20.12.2020

Hüseyin Keskin , İsmail Çelik

https://doi.org/10.21076/vizyoner.733976

Öz

Bu çalışmanın amacı Borsa İstanbul alt endekslerinde bir sistematik risk göstergesi olarak kabul gören zamanla değişen beta katsayısında uzun bellek davranışını araştırmaktır. Borsa İstanbul ulusal endeks ve alt endeksleri ile iki yıllık gösterge tahvil faizine ilişkin Ocak 2009 ve Eylül 2019 arasındaki veriler kullanılarak zamana bağlı değişen beta katsayısını DECO-FIGARCH modeli ile tespit etmenin yanı sıra GPH, Lo R/S ve GSP testleriyle beta katsayısının uzun bellek davranışları analiz edilmiştir. Yapılan analizlere göre seçilen üç alt endeksin (bankacılık, mali ve sınai) de beta katsayısının zamana bağlı olarak değiştiği ve beta katsayısının uzun bellek davranışı sergilediği (ortalamasına hiperbolik hızda geri döndüğü) sonuçlarına ulaşılmıştır. Zamana bağlı değişen beta katsayılarının geçmişe bakılarak öngörülebilir olduğu ve bu nedenle de zayıf formda piyasa etkinliğiyle çeliştiği kanıtlanmaktadır.

Anahtar Kelimeler

: Sermaye Varlıklarını Fiyatlandırma Modeli, Sistematik Risk, DECO-FIGARCH Modeli, Uzun Hafıza, Zamanla Değişen Beta Katsayısı

THE LONG MEMORY BEHAVIOR IN TIME-VARYING BETA: AN EMPIRICAL APPLICATION ON BIST

Öz

The study aims to investigate the long memory behavior in time-varying beta, a systematic risk indicator, in İstanbul Stock Exchange (BIST) sub-indices. Using the data regarding BIST national indices, sub-indices and two-year benchmark bond interest rate between January 2009 and September 2019, the time-varying beta coefficient is determined with DECO-FIGARCH model, and the long memory behaviors of the beta coefficient are analyzed with GPH, Lo R / S and GSP tests. It is found that the beta coefficient of the three sub-indices (banking, financial and industrial) changes over time and the beta coefficient demonstrates long memory behavior (mean-reverting at a hyperbolic speed). It is indicated that the time-varying beta coefficients are forecastable and our findings contradict the weak-form of market efficiency.

Anahtar Kelimeler

Capital Asset Pricing Model, Systematic Risk, DECO-FIGARCH Model, Long Memory, Time-Varying Beta Coefficient

Kaynakça

• Abiyev, V. (2015). Time-varying beta risk and its modeling techniques for Turkish industry portfolios. İktisat İşletme ve Finans Dergisi, 30(352), 79-108.
• Aielli, G.P. (2013). Dynamic conditional correlation: On properties and estimation. Journal of Business and Economic Statistic, 31(3), 282-299.
• Baillie, R. T., Bollerslev, T. and Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal Of Econometrics, 74(1), 3-30.
• Barışık, S. and Çevik, E. İ. (2008). Yapısal kirilma testleri ile Türkiye’de işsizlik histerisinin analizi: 1923-2006 dönemi. KMU İIBF Dergisi, 10(14), 1-26.
• Blume, M. (1971). On the assessment of risk. The Journal of Finance, 26(1) , 1-10.
• Bollerslev, T., Engle, R. F. and Wooldridge, J. M. (1988). A capital asset pricing model with time varying covariances. Journal of Political Economy, 96(1), 116-131.
• Brealey, R. A., Myers, S. and Allen, F., (2014). Principles of corporate finance. 11th ed., New York: McGraw-Hill Companies.
• Brooks, R. D., Faff, R. W. and McKenzie, M. D. (1998). Time‐varying beta risk of Australian industry portfolios: A comparison of modelling techniques. Australian Journal of Management, 23(1), 1-22.
• Brooks, R., Faff, R. and McKenzie, M. (2002). Time-varying country risk: An assessment of alternative modeling techniques. European Journal of Finance, 8, 249-279.
• Brooks, R., Faff, R. and Ariff, M. (1998). An investigation into the extent of beta instability in the singapore stock market. Pacific-Basin Finance Journal, 6, 87-101.
• Büberkökü, Ö. and Şahmaroğlu, S. (2016). Beta katsayılarındaki değişimin açıklanmasında işlem hacminin etkisinin incelenmesi: Banka hisselerine dayalı bir analiz. İşletme Bilimi Dergisi, 4, 1-28.
• Choudhry, T. (2001). The long memory of time-varying beta: Examination of three emerging asian stock markets. Managerial Finance, 27(1/2), 5-14.
• Choudhry, T. and Wu, H. (2007). Time-varying beta and forecasting Uk company stock returns: Garch models vs Kalman filter method. Southampton: University of Southampton Press.
• Elton, E., Gruber, M., Brown, S. and Goetzmann, W. (2014). Modern portfolio theory and investment analysis. New Jersey: John Wiley and Sons Inc.
• Engle, R. and Kelly, B. (2012). Dynamic equicorrelation. Journal of Business and Economic Statistics, 30, 212-228
• Faff, R. , Lee, W. and John, H. (1992). Time stationarity of systematic risk: Some Australian evidence. Journal of Business Finance and Accounting, 19(2), 253-270.
• Geweke J. and Porter-Hudak S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis, 4, 221-238.
• Gümrah, Ü. and Konuk, S. (2018). Zamanla değişen beta: Borsa İstanbul bankacılık sektörü uygulaması. Ekonomik ve Sosyal Araştırmalar Dergisi, 14(1), 51-66.
• Hurst H. (1951). Long term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 770-799.
• Jagannathan, R. and Wang, Z. (1996). The conditional capm and the cross‐section of expected returns. The Journal of Finance, 51(1), 3-53.
• Lintner, J. (1965). The valuation of risk assets on the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47, 13-37.
• Lo, A. W. (1991). Long term memory in stock market prices. Econometrica, 59, 1279-1313.
• Mandelbrot, B. B, and Wallis, J. R. (1969). Some long-run properties of geophysical records. Water Resouces Research, 5(2), 321-340.
• Mandelbrot, B. B. (1971). When can price be arbitraged efficiently? A limit to the validity of the random walk and martingale models. Review of Economics and Statistics, 53(3), 225-236.
• Mensi, W. Yahyaee, K. and Kang, S. (2017). Time-varying volatility spillovers between stock and precious metal markets with portfolio implications. Resources Policy, 53, 88-102.
• Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica: Journal of the econometric societybeka, 768-783.
• Robinson, P. M. and Henry, M. (1999). Long and short memory conditional heteroskedasticity in estimating the memory parameter of levels. Econometric Theory, 15(03), 299-336.
• Robinson, P.M. (1995). Gaussian semiparametric estimation of long range dependence. Annals of Statistics, 23, 1630-1661.
• Ross, S., Westerfield, R. and Jaffe, J. (2013). Corporate finance 10th ed. New York: The McGraw-Hill Companies.
• Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 425-442.