BORSA İSTANBUL’UN ASİMETRİK DİNAMİĞİNİN KANTİL OTOREGRESYON YAKLAŞIMI İLE İNCELENMESİ

Year 2023, Volume: 11 Issue: 1, 57 - 74, 24.07.2023

Müge Özdemir

https://doi.org/10.18825/iremjournal.1283918

Abstract

Bu çalışmada, Borsa İstanbul BİST100 endeksinin asimetrik dinamik sürecini incelemek için normal olmayan süreçler için dirençli çıkarımlar sağlayan kantil otoregresyon yaklaşımına dayalı Koenker-Xiao (2004) kantil birim kök testi ile yeni kanıtlar sunuyoruz. Kantil otoregresyon yaklaşımı, hisse senedi piyasalarını etkileyen farklı büyüklükteki ve işaretteki şokların kalıcılığının ölçülmesine olanak tanır. Bu yaklaşım, endeksin uzun dönem dengesindeki asimetrik dinamiklerin ayarlanmasını yakalayabilir. Bu nedenle, kantil birim kök testleri, en küçük kareler regresyon yöntemine dayanan geleneksel birim kök metodolojilerine kıyasla hisse senedi piyasası dinamiklerine yeni yaklaşımlar katmaktadır. Sonuçlarımız, her frekansta tutarlı olmamakla birlikte, piyasa davranışının sadece ortalamaya dönmediğini, aynı zamanda pozitif ve negatif şoklara farklı bir şekilde asimetrik davranış sergilediğini göstermektedir. Geleneksel birim kök testleriyle karşılaştırıldığında, kantil birim kök testleri Türkiye hisse senedi piyasasının etkin olduğunu desteklemek için daha fazla kanıt sağlar. Ayrıca, şok büyüklüğü ve işaretindeki asimetrik çıkarımlar, hisse senedi piyasalarındaki varlık fiyatlandırması açısından etkinlikten sapmaları tespit etmede yardımcı olduğunu gösteriyoruz.

Keywords

kantil birim kök testi, şok kalıcılığı, asimetriklik, bilgi etkinliği, varlık fiyatlandırması

INVESTIGATION OF BORSA ISTANBUL'S ASYMMETRIC DYNAMICS WITH QUANTILE AUTOREGRESSION APPROACH

Abstract

In this study, we present new evidence with the Koenker-Xiao (2004) quantile unit root test based on the quantile autoregression approach, which provides robust inferences for non-normal processes, to examine the asymmetric dynamic process of the BIST100 Borsa Istanbul index. The quantile autoregression approach allows to measure the persistence of shocks of different magnitudes and signals affecting the stock markets. This approach can catch the adjustment of asymmetrical dynamics in the long-run equilibrium of the index. Therefore, quantile unit root tests add new approaches to stock market dynamics compared to traditional unit root methodologies based on least squares regression method. Our results, although not consistent at all frequencies, show that market behavior not only returns to the mean, but also behaves differently to positive and negative shocks. Compared to traditional unit root tests, quantile unit root tests provide more evidence to support the efficiency of the Turkish stock market. We also show that asymmetric inferences in shock magnitude and sign are helpful in detecting deviations from information efficiency in terms of asset pricing in stock markets.

Keywords

quantile unit root test, shock persistency, asymmetry, information efficiency, asset pricing

References

• Aga, M., & Kocaman, B. (2008). Efficient market hypothesis and emerging capital markets: empirical evidence from Istanbul stock exchange. International Research Journal of Finance and Economics, 13(1), 131-144.
• Akgun, A., & Sahin, I. (2017). The testing of efficient market hypothesis in Borsa Istanbul. Annals Constantin Brancusi U. Targu Jiu, Letters & Soc. Sci. Series, 35.
• Aliyev, F. (2019). Testing market efficiency with nonlinear methods: Evidence from Borsa Istanbul. International Journal of Financial Studies, 7(2), 27.
• Altuntaş, M., Kiliç, E., Pazarci, Ş., & Alican, Umut (2022). Borsa İstanbul Alt Endekslerinde Etkin Piyasa Hipotezinin Test Edilmesi: Fourier Kırılmalı ve Doğrusal Olmayan Birim Kök Testlerinden Kanıtlar. Ekonomi Politika ve Finans Araştırmaları Dergisi, 7(1), 169-185.
• Bahmani-Oskooee, M., Chang, T., Chen, T. H., & Tzeng, H. W. (2016). Revisiting the efficient market hypothesis in transition countries using quantile unit root test. Economics Bulletin, 36(4), 2171-2182.
• Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of financial economics, 49(3), 307-343.
• Baur, D. G., Dimpfl, T., & Jung, R. C. (2012). Stock return autocorrelations revisited: A quantile regression approach. Journal of Empirical Finance, 19(2), 254-265.
• Bektur, Ç., & Aydin, M. (2019). Borsa İstanbul Ve Alt Endekslerinde Zayif Formda Piyasa Etkinliğinin Analizi: Fourier Yaklaşimi. Akademik İncelemeler Dergisi, 14(2), 59-76.
• Bernstein, P. L. (1999). Why the efficient market offers hope to active management. Journal of Applied Corporate Finance, 12(2), 129-136.
• Bofinger, E. (1975). Estimation of a density function using order statistics 1. Australian journal of statistics, 17(1), 1-7.
• Bulut, Ü. (2016). Testing the Weak Form of the Efficient Market Hypothesis: The Case of Turkey. EconWorld2016.
• Cox, D. D., & Llatas, I. (1991). Maximum likelihood type estimation for nearly nonstationary autoregressive time series. The Annals of Statistics, 1109-1128.
• Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
• Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 1057-1072.
• Engle, R. F., & Manganelli, S. (2004). CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of business & economic statistics, 22(4), 367-381.
• Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
• Feng, Y., Chen, R., & Basset, G. W. (2008). Quantile momentum. Statistics and its interface, 1, 243-254.
• Gozbasi, O., Kucukkaplan, I., & Nazlioglu, S. (2014). Re-examining the Turkish stock market efficiency: Evidence from nonlinear unit root tests. Economic Modelling, 38, 381-384.
• Grossman, S. (1976). On the efficiency of competitive stock markets where trades have diverse information. The Journal of finance, 31(2), 573-585.
• Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American economic review, 70(3), 393-408.
• Hallin, M., Jurečková, J., Picek, J., & Zahaf, T. (1999). Nonparametric tests of independence of two autoregressive time series based on autoregression rank scores. Journal of statistical planning and inference, 75(2), 319-330.
• Hasan, M. N., & Koenker, R. W. (1997). Robust rank tests of the unit root hypothesis. Econometrica: Journal of the Econometric Society, 133-161.
• Herce, M. A. (1996). Asymptotic theory of LAD estimation in a unit root process with finite variance errors. Econometric Theory, 12(1), 129-153.
• Jiang, J., & Li, H. (2020). A new measure for market efficiency and its application. Finance research letters, 34, 101235.
• Juhl, T. (1999). Testing for cointegration using M estimators. preprint.
• Kılıç, Y. (2020). Adaptive market hypothesis: Evidence from the Turkey stock market. Journal of Applied Economics and Business Research, 10(1), 28-39.
• Kilic, Y., & Fatih, M. B. (2016). The efficient market hypothesis: Evidence from Turkey. International Journal of Academic Research in Business and Social Sciences, 6(10), 262-272.
• Knight, K. (1989). Limit theory for autoregressive-parameter estimates in an infinite-variance random walk. The Canadian Journal of Statistics/La Revue Canadienne de Statistique, 261-278.
• Knight, K. (1991). Limit theory for M-estimates in an integrated infinite variance. Econometric Theory, 7(2), 200-212.
• Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica: journal of the Econometric Society, 33-50.
• Koenker, R., & Xiao, Z. (2004). Unit root quantile autoregression inference. Journal of the American statistical association, 99(467), 775-787.
• Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American statistical association, 101(475), 980-990.
• Koul, H. L., & Mukherjee, K. (1994). Regression quantiles and related processes under long range dependent errors. Journal of multivariate analysis, 51(2), 318-337.
• Koul, H. L., & Saleh, A. M. E. (1995). Autoregression quantiles and related rank-scores processes. The Annals of Statistics, 23(2), 670-689.
• Lucas, A. (1995). Unit root tests based on M estimators. Econometric Theory, 11(2), 331-346.
• Ma, L., & Pohlman, L. (2008). Return forecasts and optimal portfolio construction: a quantile regression approach. The European Journal of Finance, 14(5), 409-425.
• Nartea, G. V., Valera, H. G. A., & Valera, M. L. G. (2021). Mean reversion in Asia-Pacific stock prices: New evidence from quantile unit root tests. International Review of Economics & Finance, 73, 214-230.
• Novak, I. (2019). Efficient market hypothesis: case of the Croatian capital market. InterEULawEast: journal for the international and european law, economics and market integrations, 6(1), 3-20.
• Özdemir, M. (2022). Analyzing the Efficient Market Hypothesis with the Structural Break and Nonlinear Unit Root Tests: An Application on Borsa Istanbul. Ekoist: Journal of Econometrics and Statistics, (37), 257-282.
• Ozdemir, Z. A. (2008). Efficient market hypothesis: evidence from a small open-economy. Applied Economics, 40(5), 633-641.
• Phillips, P. C. (1995). Fully modified least squares and vector autoregression. Econometrica: Journal of the Econometric Society, 1023-1078.
• Rogers, A. J. (2001). Least absolute deviations regression under nonstandard conditions. Econometric Theory, 17(4), 820-852.
• Rothenberg, T. J., & Stock, J. H. (1997). Inference in a nearly integrated autoregressive model with nonnormal innovations. Journal of Econometrics, 80(2), 269-286.
• Siddiqui, M. M. (1960). Distribution of quantiles in samples from a bivariate population. J. Res. Nat. Bur. Standards B, 64, 145-150.
• Veronesi, P. (1999). Stock market overreactions to bad news in good times: a rational expectations equilibrium model. The Review of Financial Studies, 12(5), 975-1007.
• Weiss, A. A. (1991). Estimating nonlinear dynamic models using least absolute error estimation. Econometric Theory, 7(1), 46-68.
• Xiao, Z. (2001). Likelihood-based inference in trending time series with a root near unity. Econometric Theory, 17(6), 1082-1112.