TY - JOUR T1 - Time-Varying Beta Estimation: A Comparison of DCC-GARCH and Rolling-Window Methods in Turkish Industry Portfolios TT - Zamanla Değişen Betanın Tahmini: Türk Sektör Portföylerinde DCC-GARCH ve Rolling-Window Yöntemlerinin Karşılaştırılması AU - Çobanoğlu, Cihan PY - 2025 DA - November Y2 - 2025 DO - 10.21121/eab.20250408 JF - Ege Academic Review JO - eab PB - Ege University WT - DergiPark SN - 1303-099X SP - 753 EP - 768 VL - 25 IS - 4 LA - en AB - This study empirically compares the accuracy of two common methods for estimating time-varying betas in Turkish industry portfolios: rolling-window OLS regression and the DCC model. Using daily returns from 2004 to 2024, the methods are evaluated based on their alignment with CAPM predictions, specifically the insignificance of Jensen’s alpha and the significance of the market risk premium. Findings show that despite its complexity, the DCC model does not outperform the rolling-window approach. The rolling-window approach produces insignificant Jensen’s alpha estimates for more industries and yields slightly higher mean and t-statistics for the market risk premium. These findings challenge the view that rolling-window estimators are inefficient due to assuming beta constancy within short windows and suggest that the DCC model’s reliance on multiple constant parameters imposes a rigid structure that may hinder its adaptability to evolving market conditions. This study contributes to the literature by directly comparing these two widely used methods and highlighting the importance of carefully considering model assumptions when estimating time-varying betas. KW - CAPM KW - Time-varying beta KW - DCC KW - Rolling-window KW - Jensen’s alpha KW - Market risk premium N2 - Bu çalışma, Türk sektör portföylerinde zamanla değişen betaların tahmininde yaygın olarak kullanılan iki yöntem olan kayan pencere en küçük kareler regresyonu ile DCC modelini ampirik olarak karşılaştırmaktadır. 2004–2024 dönemine ait günlük getiriler kullanılarak, bu yöntemler CAPM öngörüleriyle özellikle Jensen alfa değerlerinin anlamsızlığı ve piyasa risk priminin anlamlılığı temelinde değerlendirilmiştir. Bulgular, karmaşıklığına rağmen DCC modelinin kayan pencere yaklaşımından daha iyi performans göstermediğini ortaya koymaktadır. Kayan pencere yaklaşımı, daha fazla sektör için istatistiksel olarak anlamsız Jensen alfa tahminleri verirken, piyasa risk primi için biraz daha yüksek ortalama ve t-istatistikleri üretmektedir. Bu bulgular, kısa pencere sürelerinde betanın sabit olduğu varsayımına dayanan kayan pencere yöntemlerinin etkin olmadığı görüşüne meydan okumakta ve DCC modelinin çok sayıda sabit parametreye dayalı yapısının, değişen piyasa koşullarına uyum sağlamasını zorlaştırabileceğini göstermektedir. Çalışma, bu iki yaygın yöntemi doğrudan karşılaştırarak ve zamanla değişen beta tahmininde model varsayımlarının dikkatli değerlendirilmesinin önemini vurgulayarak literatüre katkı sağlamaktadır. CR - Adrian, T. & Franzoni, F. (2009). Learning about beta: Time varying factor loadings, expected returns, and the conditional CAPM. Journal of Empirical Finance, 16(4), 537–556. https://doi.org/10.1016/j.jempfin.2009.02.003 CR - Agrrawal, P., Gilbert, F. W. & Harkins, J. (2022). Time dependence of CAPM betas on the choice of interval frequency and return timeframes: Is there an optimum? Journal of Risk and Financial Management, 15(11), 520. https://doi.org/10.3390/jrfm15110520 CR - Alexander, G. J. & Chervany, N. L. (1980). On the estimation and stability of beta. The Journal of Financial and Quantitative Analysis, 15(1), 123–137. https://doi.org/10.2307/2979022 CR - Aloy, M., Laly, F., Laurent, S. & Lecourt, C. (2021). Modeling time varying conditional betas: A comparison of methods with application for REITs. In G. Dufrénot & T. Matsuki (Eds.), Recent Econo¬metric Techniques for Macroeconomic and Financial Data (pp. 229–264). Springer. https://doi.org/10.1007/978-3-030-54252-8_9 CR - Baillie, R. T., Calonaci, F. & Kapetanios, G. (2022). Hierarchical time varying estimation of asset pricing models. Journal of Risk and Financial Management, 15(1), 14. https://doi.org/10.3390/jrfm15010014 CR - Bali, T. G., Engle, R. & Tang, Y. (2016). Dynamic conditional beta is alive and well in the cross section of daily stock returns. Management Science, 63(11), 3760–3779. https://doi.org/10.1287/mnsc.2016.2536 CR - Bauwens, L., Laurent, S. & Rombouts, J. V. K. (2006). Multi¬variate GARCH models: A survey. Journal of Applied Econometrics, 21(1), 79–109. https://doi.org/10.1002/jae.842 CR - Bollerslev, T., Engle, R. F. & Wooldridge, J. M. (1988). A capital asset pricing model with time varying covariances. Journal of Political Economy, 96(1), 116–131. https://doi.org/10.1086/261527 CR - Brooks, R. D., Faff, R. W. & Lee, J. H. H. (1992). The form of time variation of systematic risk: Some Australian evidence. Applied Financial Economics, 2(4), 191–198. https://doi.org/10.1080/758527100 CR - Brooks, R. D., Faff R. W. & McKenzie, M. D. (1998). Time varying beta risk of Australian industry portfolios: A comparison of modelling techniques. Australian Journal of Management, 23(1), 45–66. https://doi.org/10.1177/031289629802300101 CR - Caporin, M. & McAleer, M. (2012). Do we really need both BEKK and DCC? A tale of two multi¬variate GARCH models. Journal of Economic Surveys, 26(4), 736–751. https://doi.org/10.1111/j.1467-6419.2011.00683.x CR - Choudhry, T. & Wu, H. (2008). Forecasting ability of GARCH vs Kalman filter method: Evidence from daily UK time varying beta. Journal of Forecasting, 27(8), 670–689. https://doi.org/10.1002/for.1096 CR - Çatık, A. N., Huyugüzel Kışla, G. & Akdeniz, C. (2020). Time varying impact of oil prices on sectoral stock returns: Evidence from Turkey. Resources Policy, 69, 101845. https://doi.org/10.1016/j.resourpol.2020.101845 CR - Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized auto¬regressive conditional hetero¬skedasticity models. Journal of Business & Economic Statistics, 20(3), 339–350. https://doi.org/10.1198/073500102288618487 CR - Engle, R. F. (2016). Dynamic conditional beta. Journal of Financial Econometrics, 14(4), 643–667. https://doi.org/10.1093/jjfinec/nbw006 CR - Engle, R. F. & Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlation multi¬variate GARCH (NBER Working Paper 8554). National Bureau of Economic Research. https://doi.org/10.3386/w8554 CR - Esteban, M. V. & Orbe-Mandaluniz, S. (2010). A non¬parametric approach for estimating betas: The smoothed rolling estimator. Applied Economics, 42(10), 1269–1279. https://doi.org/10.1080/00036840701721257 CR - Fabozzi, F. J. & Francis, J. C. (1977). Stability tests for alphas and betas over bull and bear market conditions. The Journal of Finance, 32(4), 1093–1099. https://doi.org/10.2307/2326515 CR - Fama, E. F. & French, K. R. (1992). The cross-section of expected stock returns. The Journal of Finance, 47(2), 427–465. https://dx.doi.org/10.2307/2329112 CR - Fama, E. F. & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25–46. https://dx.doi.org/10.1257/0895330042162430 CR - Fama, E. F. & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607–636. https://dx.doi.org/10.1086/260061 CR - Ferson, W. E. & Harvey, C. R. (1991). The variation of economic risk premiums. Journal of Political Economy, 99(2), 385–415. https://doi.org/10.1086/261755 CR - Graham, J. R. & Harvey, C. R. (2001). The theory and practice of corporate finance: Evidence from the field. Journal of Financial Economics 60(2–3), 187–243. https://doi.org/10.1016/s0304-405x(01)00044-7 CR - Groenewold, N. & Fraser, P. (2000). Forecasting beta: How well does the ‘five year rule of thumb’ do? Journal of Business Finance & Accounting, 27(7–8). https://doi.org/10.1111/1468-5957.00341 CR - Hwang, S., & Valls Pereira, P. L. (2006). Small sample properties of GARCH estimates and persistence. The European Journal of Finance, 12(6–7), 473–494. https://doi.org/10.1080/13518470500039436 CR - İlbasmış, M. (2024). A comparison of forecasting accuracy between two dynamic conditional correlation (DCC) models. Journal of Economics Business and Political Researches, 9(23), 1–11. https://doi.org/10.25204/iktisad.1388428 CR - Jagannathan, R. & Wang, Z. (1996). The conditional CAPM and the cross-section of expected returns. The Journal of Finance, 51(1), 3–53. https://doi.org/10.2307/2329301 CR - Korkmaz, T., Çevik, E. I., Birkan, E. & Özataç, N. (2010). Testing CAPM using Markov switching model: The case of coal firms. Economic Research-Ekonomska Istraživanja, 23(2), 44–59. https://doi.org/10.1080/1331677X.2010.11517411 CR - Lewellen, J. & Nagel, S. (2006). The conditional CAPM does not explain asset pricing anomalies. Journal of Financial Economics, 82(2), 289–314. https://doi.org/10.1016/j.jfineco.2005.05.012 CR - Lettau, M. & Ludvigson, S. (2001). Resurrecting the (C)CAPM: A cross‐sectional test when risk premia are time‐varying. Journal of Political Economy, 109(6), 1238–1287. https://doi.org/10.1086/323282 CR - Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587–615. https://doi.org/10.2307/2977249 CR - Mergner, S. & Bulla, J. (2008). Time varying beta risk of Pan-European industry portfolios: A comparison of alternative modeling techniques. The European Journal of Finance, 14(8), 771–802. https://doi.org/10.1080/13518470802173396 CR - Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768–783. https://doi.org/10.2307/1910098 CR - Nieto, B., Orbe, S. & Zarraga, A. (2014). Time varying market beta: Does the estimation methodology matter? Statistics and Operations Research Transactions, 38(1), 13–42. http://hdl.handle.net/2117/88929 CR - Shah, A. & Moonis, S. A. (2003). Testing for time-variation in beta in India. Journal of Emerging Market Finance, 2(2), 163–180. https://doi.org/10.1177/097265270300200202 CR - Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.2307/2977928 CR - Silvennoinen, A. & Teräsvirta, T. (2009). Multivariate GARCH models. In T. G. Andersen, R. A. Davis, J-P. Kreiß, T. Mikosch (Eds.), Handbook of Financial Time Series (pp. 201–229). Springer-Verlag. https://doi.org/10.1007/978-3-540-71297-8_9 CR - Ustaoğlu, E. (2022). Time-varying beta coefficients of BIST sector indices. Muhasebe ve Finansman Dergisi, 96, 135–150. https://doi.org/10.25095/mufad.1143257 UR - https://doi.org/10.21121/eab.20250408 L1 - https://dergipark.org.tr/en/download/article-file/4596032 ER -