RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ

Yıl: 2020 Cilt: 8 Sayı: 1 Sayfa Aralığı: 181 - 195 Metin Dili: Türkçe DOI: 10.15295/bmij.v8i1.1364 İndeks Tarihi: 14-11-2020

RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ

Öz:
Çalışmada Türk bankacılık sektörü tarafından alınan sendikasyon kredileri ile küresel ve yerel riskgöstergeleri arasındaki ilişkilerinin asimetri ve frekans boyutunda belirlenmesi amaçlanmıştır. Bu amaçdoğrultusunda 2018 Kasım -2019 Temmuz tarihleri arasında Türk bankacılık sektörü tarafından alınan toplamsendikasyon kredileri ile global ekonomik belirsizlik endeksi, VIX endeksi, Libor, Türkiye 5 yıllık CDS primi,Türkiye jeopolitik risk endeksi ve BIST Bankacılık sektörü endeks oynaklığı arasındaki ilişkiler geleneksel,asimetrik ve asimetrik frekans nedensellik testleri ile analiz edilmiştir. Uygulanan testler sonucunda sendikasyonkredileri ile ele alınan tüm risk göstergeleri arasında nedensellik ilişkisi tespit edilmiştir. Sonuçlar, tespit edilenilişkilerin hem farklı frekanslarda hem de farklı asimetrik boyutlarda olduğunu göstermektedir.
Anahtar Kelime:

EFFECTS OF RISK INDICATORS ON SYNDICATED LOANS: ANALYSIS ON THE BASIS OF ASYMMETRY AND FREQUENCY DIMENSION

Öz:
In the study, it is aimed to determine the relationships between the syndicated loans received by Turkish banking sector and global and local risk indicators on asymmetry and frequency dimension. In line with this purpose, the relationships between the total syndicated loans and global economic policy index, VIX index, Libor, Turkish 5-year CDS premium, Turkish geopolitical risk index and BIST banking sector index volatility are analyzed by traditional, asymmetric and frequency domain asymmetric causality tests. According to the test results there are causality relationships between syndicated loans and all of the selected risk indicators. Findings indicate that the determined relationships are at difference frequencies and dimensions.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Acharya, V. V., Eisert, T., Eufinger, C., & Hirsch, C. (2018). Real effects of the sovereign debt crisis in Europe: Evidence from syndicated loans. The Review of Financial Studies, 31(8), 2855-2896. https://doi.org/10.1093/rfs/hhy045
  • Amiram, D., Beaver, W. H., Landsman, W. R., & Zhao, J. (2017). The effects of credit default swap trading on information asymmetry in syndicated loans. Journal of Financial Economics, 126(2), 364-382. https://doi.org/10.1016/j.jfineco.2016.10.001
  • Breitung, J., & Candelon, B. (2006). Testing for short and long-run causality: A frequency-domain approach. Journal of Econometrics, 132(2), 363-378. https://doi.org/10.1016/j.jeconom.2005.02.004
  • Champagne, C., & Kryzanowski, L. (2007). Are current syndicated loan alliances related to past alliances?. Journal of Banking & Finance, 31(10), 3145-3161. https://doi.org/10.1016/j.jbankfin.2006.11.018
  • Ciner, C. (2011). Information transmission across currency futures markets: Evidence from frequency domain tests. International Review of Financial Analysis, 20, 134-139. https://doi.org/10.1016/j.irfa.2011.02.010
  • Doornik J. A., & Hansen, H. (2008). An Omnibus Test for Univariate and Multivariate Normality. Oxford Bulletin of Economics and Statistics, 70(1), 927-939 https://doi.org/10.1111/j.1468-0084.2008.00537.x
  • Drago, D., & Gallo, R. (2017). The impact of sovereign rating changes on European syndicated loan spreads: The role of the rating-based regulation. Journal of International Money and Finance, 73, 213-231. https://doi.org/10.1016/j.jimonfin.2017.02.029
  • Fang, X., Li, Y., Xin, B. & Zhang, W. (2016). Financial statement comparability and debt contracting: Evidence from the syndicated loan market. Accounting Horizons, 30(2), 277-303. https://doi.org/10.2308/acch-51437
  • Gadanecz, B. (2004). The syndicated loan market: structure, development and implications. BIS Quarterly Review, December, 75-89. https://ssrn.com/abstract=1967463
  • Geweke, J. (1982). Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association, 77, 304-324. https://doi.org/10.1080/01621459.1982.10477803
  • Giannetti, M., & Laeven, L. (2012). The flight home effect: Evidence from the syndicated loan market during financial crises. Journal of Financial Economics, 104(1), 23-43. https://doi.org/10.1016/j.jfineco.2011.12.006
  • Godlewski, C. J., & Weill, L. (2008). Syndicated loans in emerging markets. Emerging Markets Review, 9(3), 206- 219. https://doi.org/10.1016/j.ememar.2008.04.001
  • Gong, D., Jiang, T., & Wu, W. (2018). A foreign currency effect in the syndicated loan market of emerging economies. Journal of International Financial Markets, Institutions and Money, 52, 211-226. https://doi.org/10.1016/j.intfin.2017.09.022
  • Granger, C. W. J., & Yoon, G. (2002). Hidden cointegration. University of California, Economics Working Paper No. 2002-02. http://dx.doi.org/10.2139/ssrn.313831
  • Hacker, R. S., & Hatemi-J, A. (2005). A test for multivariate ARCH effects. Applied Economics Letters, 12(7), 411-417. https://doi.org/10.1080/13504850500092129
  • Harm, C. (2001). European financial market integration: the case of private sector bonds and syndicate loans. Journal of International Financial Markets, Institutions and Money, 11(3-4), 245-263. https://doi.org/10.1016/S1042-4431(01)00039-7
  • Hatemi-J, A. (2012). Asymmetric Causality Tests with an Application. Empirical Economics, 43(1), 447-456. https://doi.org/10.1007/s00181-011-0484-x
  • Hosoya, Y. (1991). The decomposition and measurement of the interdependence between secondorder stationary processes. Probability Theory and Related Fields, 88(4), 429-444. https://doi.org/10.1007/BF01192551.
  • Ng, S., & Perron, P. (2001). LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power. Econometrica, 69(6), 1519-1554. https://doi.org/10.1111/1468-0262.00256
  • Pişkin, F. (2016). Türk Bankacılık Sektörü Tarafından Alınan Sendikasyon Kredilerinde Spreadi Belirleyen Faktörler. İstanbul Üniversitesi İktisat Fakültesi Mecmuası, 66(2), 113-158. https://dergipark.org.tr/tr/download/article-file/329997
  • Ranjbar, O., Chang, T., Nel, E., & Gupta, R. (2017). Energy consumption and economic growth nexus in South Africa: Asymmetric frequency domain approach. Energy Sources, Part B: Economics, Planning, and Policy, 12(1), 24-31. https://doi.org/10.1080/15567249.2015.1020120
  • Thomas, H., & Wang, Z. (2004). The integration of bank syndicated loan and junk bond markets. Journal of Banking & Finance, 28(2), 299-329. https://doi.org/10.1016/j.jbankfin.2003.01.001
APA Kamışlı M (2020). RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ. , 181 - 195. 10.15295/bmij.v8i1.1364
Chicago Kamışlı Melik RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ. (2020): 181 - 195. 10.15295/bmij.v8i1.1364
MLA Kamışlı Melik RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ. , 2020, ss.181 - 195. 10.15295/bmij.v8i1.1364
AMA Kamışlı M RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ. . 2020; 181 - 195. 10.15295/bmij.v8i1.1364
Vancouver Kamışlı M RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ. . 2020; 181 - 195. 10.15295/bmij.v8i1.1364
IEEE Kamışlı M "RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ." , ss.181 - 195, 2020. 10.15295/bmij.v8i1.1364
ISNAD Kamışlı, Melik. "RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ". (2020), 181-195. https://doi.org/10.15295/bmij.v8i1.1364
APA Kamışlı M (2020). RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ. Business and Management Studies: An International Journal, 8(1), 181 - 195. 10.15295/bmij.v8i1.1364
Chicago Kamışlı Melik RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ. Business and Management Studies: An International Journal 8, no.1 (2020): 181 - 195. 10.15295/bmij.v8i1.1364
MLA Kamışlı Melik RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ. Business and Management Studies: An International Journal, vol.8, no.1, 2020, ss.181 - 195. 10.15295/bmij.v8i1.1364
AMA Kamışlı M RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ. Business and Management Studies: An International Journal. 2020; 8(1): 181 - 195. 10.15295/bmij.v8i1.1364
Vancouver Kamışlı M RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ. Business and Management Studies: An International Journal. 2020; 8(1): 181 - 195. 10.15295/bmij.v8i1.1364
IEEE Kamışlı M "RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ." Business and Management Studies: An International Journal, 8, ss.181 - 195, 2020. 10.15295/bmij.v8i1.1364
ISNAD Kamışlı, Melik. "RİSK GÖSTERGELERİNİN SENDİKASYON KREDİLERİNE ETKİLERİ: ASİMETRİ VE FREKANS BOYUTUNDA ANALİZ". Business and Management Studies: An International Journal 8/1 (2020), 181-195. https://doi.org/10.15295/bmij.v8i1.1364