Varyansta Yapısal Kırılmalar ile Uzun Hafıza Varlığının Analizi: İskandinav Ülkelerinin Borsalarına Uygulanması

Öz

Hisse senedi piyaysasında fiyat oluşurken menkul kıymete ilişkin tüm bilgiler, fiyat oluşumunu etkilemektedir. Hisse senedi piyasalarında uzun hafızanın varlığı, ilgili piyasaların zayıf formda etkin olmadığını göstermektedir. Bu çalışmada, 01/09/2008-30/09/2022 dönemine ilişkin İskandinav ülkeleri olan Danimarka, İsveç, Norveç ve Finlandiya hisse senedi piyasalarındaki volatilitede meydana gelen şok etkileri araştırılmıştır. Geweek ve Porter-Hudak testi ile bu ülkelerin volatilite serilerinde uzun hafıza parametresine ilişkin sıfır hipotezi reddedilmiştir. Ayrıca, Lo Modifiye Edilmiş R/S testi ile volatilite serisi ilgili kritik değerler aralığının üzerinde sonuç elde edilmiştir. Varyanstaki yapısal kırılmalar dikkate alınarak yinelenen kümülatif kareler toplamı (ICSS) yöntemi ile FIGARCH modeli uygulanmıştır. Çalışmada bulunan ülkelerden, Danimarka (d= 0.37064), Norveç (d=0.46677), İsveç (d=0.50199) ve Finlandiya (d=0.44732) sonucuna ulaşılmıştır. Bu ülkelerin borsalarına ilişkin getiri serileri hemen dengeye ulaşmaktadır. Ancak, zayıf formda etkin olmayan volatilite serilerinin mevcut ve gelecekte oluşabilecek fiyatın, geçmiş fiyatlarından bağımsız olmadığına işaret etmektedir. Bu bulgular, İskandinav ülkelerin getiri serilerinde uzun hafıza özelliğinin bulunmadığını ancak volatilite serilerinde uzun hafıza özelliğinin varlığını ve dolayısıyla bu ülkelerin borsalarının volatilite serilerinin zayıf formda etkin olmadıklarının sonucu elde edilmiştir.

Anahtar Kelimeler

• Zayıf Formda Etkin Piyasa
• Uzun Hafıza
• FIGARCH
• Varyans Kırılması
• İskandinav Ülkeleri

Analysis of The Presence of Long Memory with Structural Breaks in Variance: Application to Stock Markets of Scandinavian Countries

Öz

While the price is formed in the stock market, all information about the security affects the price formation. The existence of long memories in stock markets shows that the relevant markets are not efficient in weak form. In this study, the shock effects on the volatility in the stock markets of Denmark, Sweden, Norway, and Finland, which are Scandinavian countries, for the period 01/09/2008-30/09/2022 were investigated. With the Geweek and Porter-Hudak test, the hypothesis of t (H0: d=0) of the long memory parameter in the volatility series of these countries was rejected. In addition, results above the range of critical values related to the Lo R/S test were obtained. The FIGARCH model was applied by the iterated sum of cumulative squares (ICSS) method, taking into account the structural breaks in the variance. According to the results obtained, the countries in the study were calculated as Denmark (d= 0.37064), Norway (d=0.46677), Sweden (d=0.50199) and Finland (d=0.44732). The return series for the stock markets of these countries immediately reached equilibrium. However, it indicates that the current and future prices of the weak-form ineffective volatility series are not independent of the past prices. Therefore, while there is no long memory feature in the return series of Scandinavian countries, it has been found that the long memory feature is in the volatility series, and therefore these countries are not efficient in the weak form.

Anahtar Kelimeler

• Weak-Form Efficient Market
• Long Memory
• FIGARCH
• Variance Break
• Scandinavian Countries

Kaynakça

1. Ajmi, A. N., Ghorbel, A., & Trabelsi, A. (2005). The Reserve Bank of Australia Intervention: Exchange Rate Volatility from FIGARCH Modelling. SSRN Electronic Journal, 1992, 1–10. https://doi.org/10.2139/ssrn.460120
2. Akardeniz, E., & Engin, C. (2019). TCMB Faiz Kararlarının Döviz Kuru Volatilitesine Etkisi [The Effect of CBRT Interest Decisions to Exchange Rate Volatility]. Finansal Araştırmalar ve Çalışmalar Dergisi, 11(20), 1–27. https://doi.org/10.14784/marufacd.599086
3. Anis, A. A., & Lloyd, E. H. (1976). The Expected Value of the Adjusted Rescaled Hurst Range of Independent Normal Summands. Biometrika, 63(1), 111. https://doi.org/10.2307/2335090
4. Antonakakis, N., & Darby, J. (2013). Forecasting volatility in developing countries’ nominal exchange returns. Applied Financial Economics, 23(21), 1675–1691. https://doi.org/10.1080/09603107.2013.844323
5. Arouri, M. E. H., Hammoudeh, S., Lahiani, A., & Nguyen, D. K. (2012). Long memory and structural breaks in modeling the return and volatility dynamics of precious metals. Quarterly Review of Economics and Finance, 52(2), 207–218. https://doi.org/10.1016/j.qref.2012.04.004
6. Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3–30. https://doi.org/10.1016/S0304-4076(95)01749-6
7. Baillie, R. T., Han, Y.-W., Myers, R. J., & Song, J. (2007). Long Memory and FIGARCH Models For Daily and High Frequency Commodity Prices. In Futures (No. 594; Issue 594).
8. Beine, M., Bénassy-Quéré, A., & Lecourt, C. (2002). Central bank intervention and foreign exchange rates: New evidence from FIGARCH estimations. Journal of International Money and Finance, 21(1), 115–144. https://doi.org/10.1016/S0261-5606(01)00040-7

9. Bentes, S. R. (2015). Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: New evidence. Physica A: Statistical Mechanics and Its Applications, 438, 355–364. https://doi.org/10.1016/j.physa.2015.07.011
10. Brooks, C. (2008). Introductory Econometrics for Finance. In Cambridge University Press.
11. Bruffaerts, C., Verardi, V., & Vermandele, C. (2014). A generalized boxplot for skewed and heavy-tailed distributions. Statistics and Probability Letters, 95, 110–117. https://doi.org/10.1016/j.spl.2014.08.016
12. Cavalcante, J., & Assaf, A. (2004). Long Range Dependence in Returns and Volatility of the Braziliain Stock Market. Eur. Rev. Econ. Financ., 3, 5–22.
13. Çevik, E. İ., & Erdoğan, S. (2009). Bankacılık Sektörü Hisse Senedi Piyasasının Etkinliği: Yapısal Kırılma ve Güçlü Hafıza [ Efficiency of Banking Sector Stock Market: Structural Break and Long Memory]. Doğuş Üniversitesi Dergisi, 10(1), 26–40.
14. Chang, C. L., McAleer, M., & Tansuchat, R. (2012). Modelling long memory volatility in agricultural commodity futures returns. Annals of Financial Economics, 7(2), 1–27. https://doi.org/10.1142/S2010495212500108
15. Chortareas, G., Jiang, Y., & Nankervis, J. C. (2011). Forecasting exchange rate volatility using high-frequency data: Is the euro different? International Journal of Forecasting, 27(4), 1089–1107. https://doi.org/10.1016/j.ijforecast.2010.07.003
16. Eyüboğlu, K., & Eyüboğlu, S. (2022). BIST Ana Sektör Endekslerinde Zayıf Formda Etkinliğin Yapısal Krıılmalı Uzun Hafıza Modelleri ile Analizi [Analysis of Weak-Form Efficiency With Structural Fracture Long Memory Models in Bist Main Sector Indexes]. Abant Sosyal Bilimler Dergisi, 22(2), 702–720. https://doi.org/10.11616/asbi.1097446
17. Ezzat, H. (2013). Long Memory Processes and Structural Breaks in Stock Returns and Volatility: Evidence from the Egyptian Exchange. International Research Journal of Finance and Economics, 113(51465), 136–146. http://mpra.ub.uni-muenchen.de/51465/
18. Fama, E. F. (1965). The Behavior of Stock-Market Prices. Journal of the American Statistical Association, 38(1), 36–105. https://doi.org/10.2307/2277297
19. Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383–417.
20. Geweke, J., & Porter‐Hudak, S. (1983). the Estimation and Application of Long Memory Time Series Models. Journal of Time Series Analysis, 4(4), 221–238. https://doi.org/10.1111/j.1467-9892.1983.tb00371.x
21. Güngör, S., Başçı, E. S., & Karaca, S. S. (2021). Yapısal Kırılmalar Altında Asimetrik Bilginin Hisse Senedi Getiri Oynaklığına Etkisi: BİST 100 Endeksi’nde Bir Uygulama [The Impact of Asymmetric Information on Stock Return Volatility under Structural Break: An Application on BIST 100 Index]. Muhasebe ve Finansman Dergisi, 852110(89), 133–154. https://doi.org/10.25095/mufad.852110
22. Kang, S. H., Cheong, C., & Yoon, S. M. (2010). Long memory volatility in Chinese stock markets. Physica A: Statistical Mechanics and Its Applications, 389(7), 1425–1433. https://doi.org/10.1016/j.physa.2009.12.004
23. Kang, S. H., Cho, H. G., & Yoon, S. M. (2009). Modeling sudden volatility changes: Evidence from Japanese and Korean stock markets. Physica A: Statistical Mechanics and Its Applications, 388(17), 3543–3550. https://doi.org/10.1016/j.physa.2009.05.028
24. Kang, S. H., & Yoon, S. M. (2007). Long memory properties in return and volatility: Evidence from the Korean stock market. Physica A: Statistical Mechanics and Its Applications, 385(2), 591–600. https://doi.org/10.1016/j.physa.2007.07.051
25. Kasman, A., Kasman, S., & Torun, E. (2009). Dual long memory property in returns and volatility: Evidence from the CEE countries’ stock markets. Emerging Markets Review, 10(2), 122–139. https://doi.org/10.1016/j.ememar.2009.02.002
26. Kasman, A., & Torun, E. (2007). Long memory in the Turkish stock market return and volatility. Central Bank Review, 2, 13–27. http://www.tcmb.gov.tr/research/cbreview/july07-2.pdf
27. Korkmaz, T., Cevik, İ. E., & Özataç, N. (2009). Testing for Long Memory in ISE using ARFIMA-FIGARCH model and structural break test. International Research Journal of Finance and Economics, 29, 186–191.
28. Kutlu, S., & Yurttagüler, İ. M. (2014). Türkiye’de Reel Döviz Kurlarının Uzun Hafıza Özellikleri: Kesirli Bütünleşme Analizi. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, XXXVI(I), 373–389. https://doi.org/10.14780/iibdergi.201417552
29. Lo, A. W. (1991). Long-Term Memory in Stock Market Prices. Econometrica, 59(5), 1279. https://doi.org/10.2307/2938368
30. Lux, T., & Kaizoji, T. (2007). Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching. Journal of Economic Dynamics and Control, 31(6), 1808–1843. https://doi.org/10.1016/j.jedc.2007.01.010
31. 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. https://doi.org/10.5296/ajfa.v4i2.2027
32. Oh, G., Kim, S., & Eom, C. (2008). Long-term memory and volatility clustering in high-frequency price changes. Physica A: Statistical Mechanics and Its Applications, 387(5–6), 1247–1254. https://doi.org/10.1016/j.physa.2007.08.061
33. Özdemir, A., & Çelik, İ. (2020). Pay Piyasalarında Etkin Piyasalar Hipotezinin Farklı Dağılım Varsayımları Bağlamında Uzun Hafıza Modelleri ile Tespiti: ABD ve Türkiye Karşılaştırması [Determination of Effecitive Market Hypothesis in Stock Markets By Long Memory Models in The Context of . In Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi (Vol. 21, Issue 1). https://doi.org/10.24889/ifede.481059
34. Özdemir, A., Vergili, G., & Çelik, İ. (2018). Döviz Piyasalarının Etkinliği Üzerinde Uzun Hafızanın Rolü : Türk Döviz Piyasasında Ampirik Bir Araştırma [The Role of Long Memory on the Efficiency of Foreign Exchange Markets: An Ampricial Resarch in the Turkhish Foreign Exchange Market]. BDDK Bankacılık ve Finansal Piyasalar, 12(1), 87–107.
35. Pehlivan, G. G., & Utkulu, U. (2007). Türkiye’nin Tüketim Fonksiyonu: Parçalı Hata Düzeltme Modeli Bulguları [Turkey’s Consumption Function: Fractional ECM (FECM) Evidence]. Akdeniz İ.İ.B.F Dergisi, 14, 39–65.
36. Pierdzioch, C., Döpke, J., & Hartmann, D. (2008). Forecasting stock market volatility with macroeconomic variables in real time. Journal of Economics and Business, 60(3), 256–276. https://doi.org/10.1016/j.jeconbus.2007.03.001
37. Sansó, A., Carrion, J. L., & Aragó, V. (2004). Testing for changes in the unconditional variance of financial time series. Revista de Economiá Financiera, 4, 32–53. http://dspace.uib.es/xmlui/handle/11201/152078
38. Teverovsky, V., Taqqu, M. S., & Willinger, W. (1999). A critical look at Lo’s modified R/S statistic. Journal of Statistical Planning and Inference, 80(1), 211–227. https://doi.org/10.1016/s0378-3758(98)00250-x
39. Türkyılmaz, S., & Balıbey, M. (2014). Türkiye Hisse Senedi Piyasası Oynaklığındaki Asimetrik Uzun Hafıza Özelliği [ASymmetric Long Memory Property in Volatility of Turkey Stock Market]. Bankacılık ve Finansal Araştırmalar Dergisi, 1(1), 20.
40. Vilasuso, J. (2002). Forecasting exchange rate volatility. Economics Letters, 76(1), 59–64. https://doi.org/10.1016/S0165-1765(02)00036-8