İKİLİ UZUN HAFIZADA ASİMETRİ ETKİSİ: BİST BANKA ÖRNEĞİ

Yıl 2019, Cilt: 6 Sayı: 1, 92 - 106, 29.04.2019

Harun Kaya İsmail Çelik

https://doi.org/10.30798/makuiibf.516455

Cited By: 1

Öz

Çalışmanın amacı, Türk bankacılık sektör endeksinin
getiri ve volatilitesinde ikili uzun hafıza özelliğini ARFIMA-FIGARCH ve
ARFIMA-FIEGARCH modeli ile inceleyerek etkin piyasalar hipotezini test
etmektir. Bu amaçla modelde veri seti olarak 2008-2017 dönemi Borsa İstanbul
Banka Endeksi (XUBANK) kapanış fiyatları kullanılmıştır. İkili uzun hafızayı
test etmek için farklı hata dağılım varsayımlarına göre kurulan ARFIMA-FIGARCH
model tahminlerine göre, getiride uzun hafıza özelliğine ilişkin bulgular elde
edilemezken;  volatilitede uzun hafıza
özelliğini destekler bulgulara ulaşılmıştır. Ayrıca, söz konusu dönemde ortaya
çıkan yapısal kırılmanın volatilitedeki uzun hafıza üzerinde istatistiki bir
etkisinin olmadığı tespit edilmiştir. Bilgi şoklarının asimetrik etkisini de
ölçmek için ARFIMA-FIEGARCH modeli Student-t dağılımına göre tahmin edilmiş ve
getiride uzun hafıza olmadığı tespit edilmiştir. Ancak getiri volatilitesinde
uzun hafıza parametresinin 0,74 olduğu ve negatif bilgi şoklarının pozitif
bilgi şoklarına göre daha fazla oynaklığa sebep olduğu gözlenmiştir. 

Anahtar Kelimeler

Etkin Piyasa Hipotezi, Bankacılık Sektörü, İkili Uzun Hafıza, ARFIMA-FIGARCH, ARFIMA-FIEGARCH, Volatilite

ASYMMETRY EFFECT IN DUAL LONG MEMORY: BIST BANK CASE

Öz

The aim of this paper is to test the efficient market
hypothesis by examining the dual long memory feature of the Turkish banking
sector index in return and volatility with the ARFIMA-FIGARCH and
ARFIMA-FIEGARCH models. For this purpose, closing prices of 2008-2017 period
Stock Exchange Istanbul Bank Index (XUBANK) were used as data set in the model.
According to the ARFIMA-FIGARCH model estimates established according to
different error distribution assumptions to test the dual long memory, while no
findings can be obtained about the long memory feature in the return; the
volatility has long been supported by findings that support long memory. Moreover,
it has been determined that structural break has no statistical effect on the
long memory related to the volatility in the mentioned period. In
order to measure the asymmetric effect of the information shocks, the
ARFIMA-FIEGARCH model was estimated according to the Student-t distribution and
it was found that there was no long memory in the return. However, it was
observed that the rate of long memory in the volatility of return was 0.74 and
the negative information shocks caused more volatility than the positive
information shocks. 

Anahtar Kelimeler

Efficient Market Hypothesis, Banking Sector, Dual Long Memory, ARFIMA-FIGARCH, ARFIMA-FIEGARCH, Volatility

Kaynakça

• BAI, J., PERRON, P. (1998), Estimating and Testing Linear Models with Multiple Structural Changes, Econometrica, 66, 47-78.
• BAI, J., PERRON, P. (2003), Computation and Analysis of Multiple Structural Change Models, Journal of Applied Econometrics, 18, 1-22.
• BAILLIE, R. T., BOLLERSLEV, T. ve MIKKELSON, H. O. (1996), Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics, 74(1), 3-30.
• BAILLIE, R. T. (1996), Long Memory Process And The Fractional Integration In Econometrics, Journal of Econometrics, 73, 5-59.
• BOLLERSLEV, T. (1986), Generalized Autoregressive Conditional Heteroscedasticity, Journal of Econometrics, 31(3), 307-327.
• BOLLERSLEV, T., MIKKELSEN, H. O., (1996), Modelling And Pricing Long Memory in Stock Market Volatility, J Economic, 73, 151–184.
• BREALEY, R. A., MYERS, S. C., (2003), Principles of Corporate Finance, NewYork: McGraw Hill Companies.
• ÇEVİK, E. İ. (2012), İstanbul Menkul Kıymetler Borsası’nda Etkin Piyasa Hipotezinin Uzun Hafıza Modelleri İle Analizi: Sektörel Bazda Bir İnceleme, Yaşar Üniversitesi E-Dergisi, 7(26), 4437-4454.
• ÇEVİK, E. İ., ERDOĞAN, S. (2009), Bankacılık Sektörü Hisse Senedi Piyasasının Etkinliği: Yapısal Kırılma ve Güçlü Hafıza, Doğuş Üniversitesi Dergisi, 10(1), 26-40.
• ELDER, J., SERLETIS, A. (2007), On Fractional Integrating Dynamics in The US Stock Market, Chaos, Solitons and Fractals, 34(3),777-781.
• ENGLE, R. F. (1982), Autoregressive Conditional Heteroscedasticity with Estimates of The Variance of United Kingdom Inflation. Econometrica, 50 (4), 987-1007.
• EOMA, C., CHOI, S., OH, G. ve JUNG, W.S. (2008), Hurst Exponent and Prediction Based On Weak-Form Efficient Market Hypothesis of Stock Markets, Physica A, 387, 4630-4636.
• FAMA, E. F., (1970), Efficient Capital Markets: A Review of Theory and Empirical Works, Journal of Finance, 25(2), 383-417.
• GEWEKE, J., PORTER-HUDAK, S. (1983), The Estimation And Application Of Long Memory Time Series Models, Journal of Time Series Analysis, 4, 221-238.
• GRANGER, C. W. J. (1980), Long Memory Relationships and The Aggregation of Dynamic Models, Journal of Econometrics, 14 (2), 227-238.
• GRANGER, C. W. J., JOYEUX, R. (1980), An Introduction to long memory time series models and fractional differencing, Journal of Time Series Analysis, 1(1), 15-29.
• HOSKING, J. R. M. (1981), Fractional Differencing, Biometrica, 68(1), 165-176.
• INCLAN, C., TIAO, G. C. (1994), Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance, Journal of the American Statistical Association, 89(427), 913-923.
• KILIÇ, R. (2004), On The Long Memory Properties of Emergingcapital Markets: Evidence From Istanbul Stock Exchange, Applied Financial Economics, 14, 915–922.
• KORKMAZ, T., ÇEVİK, E. İ. ve ÖZATAÇ, N. (2009), Testing for Long Memory in ISE Using ARFIMA-FIGARCH Model And Structural Break Test, International Research Journal of Finance and Economics, 26, 186-191.
• LO, A. W. (1991), Long-Term Memory in Stock Market Prices, Econometrica, 59(5), 1279-1313.
• LUX, T., KAIZOJI, T. (2007), Forecasting Volatility and Volume in The Tokyo Stock Market: Long Memory, Fractality And Regime Switching, Journal of Economic Dynamics ve Control, 31(6), 1808-1843.
• MAHESHCHANDRA, J. P. (2012), Long Memory Property in Return And Volatility: Evidence From The Indian Stock Markets, Asian Journal of Finance & Accounting, 4(2), 218-230.
• MCMILLAN, D. G., THUPAYAGALE, P. (2008), Efficiency of The South African Equity Market, Applied Financial Economics Letters, 4(5), 327-330.
• MUN, H.W., SUNDARAM, L. ve YIN, O.S. (2008), Leverage Effect And Market Efficiency of Kuala Lumpur Composite Index, International Journal of Business and Management, 3(4),138-144.
• NELSON, D. B. (1991), Conditional Heteroskedasticity İn Asset Returns: A New Approach, Econometrica, 59(2), 347-370.
• PHILLIPS, P.C.B. (1999a), Discrete Fourier Transforms of Fractional Processes. Unpublished Working Paper, 1243, Cowles Foundation For Research in Economics, 18.05.2018 tarihinde Yale Üniversitesi: http://Cowles.Econ.Yale.Edu/P/ Cd/D12a/D1243.pdf adresinden alındı.
• PHILLIPS, P.C.B. (1999b), Unit Root Log-Periodogram Regression. Unpublished Working Paper, No. 1244, Cowles Foundation For Research in Economics, 18.05.2018 tarihinde Yale Üniversitesi: http://Cowles.Econ.Yale.Edu/P/Cd/D12a/D1244.pdf adresinden alındı.
• ROBINSON, P.M. (1995), Log-Periodogram Regression of Time Series With Long Range Dependence, Annals Of Statistics, 23, 1048-1072.
• SANSO, A., ARAGO, V. and CARRION, J. L., (2004), Testing For Changes in the Unconditional Variance of Financial Time Series, Revista de Economİa Financiera,1-24.TURGUTLU, E. (2004), Fisher Hipotezinin Tutarlılığının Testi: Parçalı Durağanlık Ve Parçalı Koentegrasyon Analizi, DEÜ İİBF Dergisi, 19(2), 55-75.
• TÜRKYILMAZ, S., BALIBEY, M. (2014), Türkiye Hisse Senedi Piyasası Getiri Ve Oynaklığındaki Uzun Dönem Bağımlılık İçin Ampirik Bir Analiz, DEÜ Sosyal Bilimler Enstitüsü Dergisi, 16(2), 281-302.
• URAL, C., KÜÇÜKÖZMEN, C. (2011), Analyzing The Dual Long Memory in Stock Market Returns, Ege Academic Review, 11 (Özel Sayı), 19-28.