ÜLKE KREDİ DERECELENDİRMENİN MEKÂNSAL ANALİZİ: GIPSI ÜLKELERİ

Ülke kredi notları, temerrüt olasılığının ileriye dönük tahminini yansıttıkları için küresel olarak izlenmektedir. Ayrıca, ülke riskinin bir belirleyicisi olarak da kabul edilmektedir. Birçok çalışma, kredi notlarının belirleyicilerini ekonomik, finansal veya politik açıdan bulmaya çalışmaktadır. Bu çalışmalarda birçok farklı ekonometrik yöntem (en küçük kareler, sıralı tepki modeli veya sıralı probit modelleri gibi) kullanılırken, ülkeler arasındaki karşılıklı bağımlılığın göz ardı edilmesi büyük bir soruna neden olabilmektedir. Bu çalışma, kredi notları için mekânsal yöntemler uygulayarak ilgili literatüre katkı sağlamayı amaçlamaktadır. Geleneksel modellerin aksine, mekânsal modeller, ülkeler arasındaki yayılma etkilerini dikkate almaktadır. Bu nedenle bu çalışmada GIPSI (Yunanistan, İrlanda, Portekiz, İspanya ve İtalya) ülkeleri için 2003-2021 dönemi için üçer aylık veriler kullanılmıştır. LM(Lagrance Multiplier) ve LR(Likelihood Ratio) testleri mekânsal otoregresif modelin uygun olduğunu desteklemektedir. Yapılan tahminlemelere göre, açıklayıcı değişkenler (kişi başına GSYİH, uluslararası rezervler, GSYİH büyümesi, faiz dışı denge, cari işlemler dengesi ve devlet borcu) istatistiksel olarak anlamlı bulunmuştur. Ayrıca mekansal etkileşimin varlığını kanıtlayan mekansal otoregresif katsayısı (ρ) anlamlı bulunmuştur.

SPATIAL ANALYSIS OF THE SOVEREIGN CREDIT RATINGS: A CASE OF GIPSI COUNTRIES

Sovereign credit ratings are monitored globally because they reflect the forward-looking estimate of the default probability. In addition, they are widely accepted as an indicator of sovereign risk. Many studies try to find the determinants of the credit ratings from economic, financial or political perspectives. While many different econometric methods (like ordinary least square, ordered response model or ordered probit models) are used in these studies, ignoring the interdependency between the countries can cause a major problem. This study aims to contribute to the related literature by applying spatial methods for credit ratings. In contrast to the conventional models, spatial models consider the spillover effects between the countries. For this reason, quarterly data for GIPSI (Greece, Ireland, Portugal, Spain and Italy) countries are used from 2003 to 2021. LM (Lagrange Multiplier) test and the LR (Likelihood Ratio) tests support that the spatial autoregressive model (SAR) is appropriate. According to estimations, the explanatory variables (GDP per capita, international reserves, GDP growth, primary balance, current account balance and government debt) are found to be statistically significant. In addition, the spatial autoregressive coefficient (ρ) is significant, which provides the existence of spatial interaction.

___

  • Abad, P., Alsakka, R., & ap Gwilym, O. (2018). The influence of rating levels and rating convergence on the spillover effects of sovereign credit actions. Journal of International Money and Finance, 85, 40-57.
  • Afonso, A., Gomes, P., & Rother, P. (2007). What “hides” behind sovereign debt ratings? (No. 711). ECB Working Paper.
  • Aizenman, J., Hutchison, M., & Jinjarak, Y. (2013). What is the risk of European sovereign debt defaults? Fiscal space, CDS spreads and market pricing of risk. Journal of International Money and Finance, 34, 37-59.
  • Beirne, J., & Fratzscher, M. (2013). The pricing of sovereign risk and contagion during the European sovereign debt crisis. Journal of International Money and Finance, 34, 60-82.
  • Binici, M., & Hutchison, M. (2018). Do credit rating agencies provide valuable information in market evaluation of sovereign default Risk? Journal of International Money and Finance, 85, 58-75.
  • Bissoondoyal-Bheenick, E. (2005). An analysis of the determinants of sovereign ratings. Global Finance Journal, 15(3), 251-280.
  • Blasques, F. Jan Koopman, S. Lucas, A. and Schaumburg, J. (2016). Spillover dynamics for systemic risk measurement using spatial financial time series models. Journal of Econometrics, 195: 211–223.
  • Bozic, V., & Magazzino, C. (2013). Credit rating agencies: The importance of fundamentals in the assessment of sovereign ratings. Economic Analysis and Policy, 43(2), 157-176.
  • Caceres, C., V. Guzzo, and M. Segoviano (2010). Sovereign Spreads: Global Risk Aversion, Contagion or Fundamentals?, IMF Working Paper, 10/120.
  • Cantor, R., & Packer, F. (1996a). Sovereign risk assessment and agency credit ratings. European financial management, 2(2), 247-256.
  • Cantor R. and Packer, F. (1996b). Determinants and impact of sovereign credit ratings. Economic Policy Review, 2, 37-53. Federal Reserve Bank of New York.
  • Canuto, O., Dos Santos, P. F. P., & de Sá Porto, P. C. (2012). Macroeconomics and sovereign risk ratings. Journal of International Commerce, Economics and Policy, 3(02), 1250011.
  • Chodnicka-Jaworska, P. (2014). Credit rating determinants for European countries. Global Journal of Management and Business Research: C Finance, 15(9).
  • De Vries, T., & de Haan, J. (2016). Credit ratings and bond spreads of the GIIPS. Applied Economics Letters, 23(2), 107-111.
  • Debarsy, N., Dossougoin,C., Ertur, C. and Gnabo, J. (2018). Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach. Journal of Economic Dynamics and Control 87: 21–45.
  • Dieckmann, S. and Plank, T. (2012). Default risk of advanced economies: An empirical analysis of credit default swaps during the financial crisis. Review of Finance 16 (4): 903–934.
  • Eder, Armin, and Sebastian Keiler. (2015). CDS spreads and contagion amongst systematically important financial institutions-A spatial econometric approach. International Journal of Finance and Economics 20: 291–309.
  • Elhorst, J.P., (2014). Spatial Econometrics, From Cross-Sectional Data to Spatial Panels. Springer, Heidelberg.
  • Erdem, O., & Varli, Y. (2014). Understanding the sovereign credit ratings of emerging markets. Emerging Markets Review, 20, 42-57.
  • Fourie, L., & Botha, I. (2015). Sovereign credit rating contagion in the EU. Procedia Economics and Finance, 24, 218-227.
  • Gültekin-Karakaş, D., Hisarcıklılar, M., & Öztürk, H. (2011). Sovereign risk ratings: Biased toward developed countries?. Emerging Markets Finance and Trade, 47(sup2), 69-87.
  • Haque, N., Kumar, M., Mark, N. et al.(1996). The Economic Content of Indicators of Developing Country Creditworthiness. IMF Econ Rev 43, 688–724.
  • Hilscher, J., & Nosbusch, Y. (2010). Determinants of sovereign risk: Macroeconomic fundamentals and the pricing of sovereign debt. Review of Finance, 14(2), 235-262.
  • Hu, Y. T., Kiesel, R., & Perraudin, W. (2002). The estimation of transition matrices for sovereign credit ratings. Journal of Banking & Finance, 26(7), 1383-1406.
  • Huyugüzel Kışla, G. & Önder, A. Ö. (2018). Spatial analysis of sovereign risks: The case of emerging markets. Finance Research Letters, 26, 47-55.
  • Huyugüzel Kışla, G., Muradoğlu, Y. G., & Önder, A. Ö. (2022). Spillovers from one country’s sovereign debt to CDS (credit default swap) spreads of others during the European crisis: a spatial approach. Journal of Asset Management, 1-20.
  • Kabadayı, B., & Çelik, A. A. (2015). Determinants of Sovereign Ratings in Emerging Countries: Qualitative Dependent Variables Panel Data Analysis. International Journal of Economics and Financial Issues, 5(3), 656-662.
  • Kırkıl, M. (2020). Ülke Kredi Riski Derecelendirmede: İç Ekonomik Veriler ile Temerrüt Olasılığı İlişkisinin İncelenmesi. Journal of Economic Policy Researches, 8(1), 57-74.
  • Lesage, J.P., Pace, R.K. (2010). Spatial Econometric Models. In: Fisher, M.M. (Ed.), Handbook of Applied Spatial Analysis. Springer, pp. 355–374.
  • Mellios, C., & Paget-Blanc, E. (2006). Which factors determine sovereign credit ratings? The European Journal of Finance, 12(4), 361-377.
  • Mili, Mehdi. (2018). Systemic risk spillovers in sovereign credit default swaps in Europe: A spatial approach. Journal of Asset Management, 19: 133–143.
  • Micu, M., Remolona, E., & Wooldridge, P. (2004). The price impact of rating announcements: evidence from the credit default swap market. BIS Quarterly Review, 2(June), 55-65.
  • Mora, N. (2006). Sovereign credit ratings: Guilty beyond reasonable doubt? Journal of Banking & Finance, 30(7), 2041-2062.
  • Özturk, H., Namli, E., & Erdal, H. I. (2016). Modelling sovereign credit ratings: The accuracy of models in a heterogeneous sample. Economic Modelling, 54, 469-478.
  • Proença, C., Neves, M., Dias, J. C., & Martins, P. (2021). Determinants of sovereign debt ratings in clusters of European countries–effects of the crisis. Journal of Financial Economic Policy.
  • Reinhart, C. M. (2002). Default, currency crises, and sovereign credit ratings. the world bank economic review, 16(2), 151-170.
  • Rowland, P. & Torres, J.L. (2004). Determinants of Spread and Creditworthiness for Emerging Market Sovereign Debt: A Panel Data Study. Banco de la Republica de Colombia Working Paper.
  • Seldadyo, H., Elhorst, J. P., & De Haan, J. (2010). Geography and governance: Does space matter? Papers in Regional Science, 89(3), 625-640.
  • Stawasz-Grabowska, E. (2020). Sovereign credit rating determinants of the EU countries: The role of the euro area crisis and its legacy. Entrepreneurial Business and Economics Review, 8(2), 47-69.
  • Yuan, C., & Pongsiri, T. J. (2015). Fiscal austerity, growth prospects, and sovereign CDS spreads: The Eurozone and beyond. International Economics, 141, 50-79.