The relationship between sovereign credit rating and trends of macroeconomic indicators
-
Received May 28, 2019;Accepted September 25, 2019;Published October 4, 2019
-
Author(s)Link to ORCID Index: https://orcid.org/0000-0002-3003-7352
-
DOIhttp://dx.doi.org/10.21511/imfi.16(3).2019.26
-
Article InfoVolume 16 2019, Issue #3, pp. 292-306
- TO CITE АНОТАЦІЯ
-
Cited by1 articlesJournal title:Article title:DOI:Volume: / Issue: / First page: / Year:Contributors:
- 1027 Views
-
226 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The sovereign credit rating provides information about the creditworthiness of a country and thereby serves as a tool for investors in order to make right decisions concerning financial assets worth investments. Thus, determination of a sovereign credit rating is a highly complex and challenging activity. Specialized agencies are involved in rating assessment. So, it’s essential to analyze the efficiency of their work and seek out easily accessible tools for generating assessments of such ratings. The objective of this article is to find out whether sovereign credit rating can be reliably estimated using trends of selected macroeconomic indicators, despite the fact that sovereign credit rating is most likely influenced by non-economic factors. This can be used for strategic considerations at national and multinational levels. The relationships between sovereign credit rating and the trends of macroeconomic indicators were examined using statistical methods, linear multiple regression analysis, cumulative correlation coefficient, and multicollinearity test. The data source used is comprised of selected World Bank indicators meeting the conditions of completeness and representativeness. The data set has shown a cumulative correlation coefficient value greater than 95%, however at 100% multicollinearity. This is followed by the gradual elimination of indicators, but even this did not allow achieving acceptable values. So, the conclusion is that rating levels are not explainable solely by the trends of economic indicators, but other influences, e.g. political. However, the fact that the statistical model yielded acceptable results for five and fewer indicators allowed a regression equation to be found that gives good estimates of a country’s rating. This allows, for example, predicting of ratings relatively easy by forecasting the development of selected macroeconomic indicators.
- Keywords
-
JEL Classification (Paper profile tab)E27, G17, G24
-
References40
-
Tables5
-
Figures3
-
- Figure 1. Complete x graph evaluating multiple regression and correlation analysis
- Figure 2. Trends of cumulative characteristics when gradually eliminating indicators
- Figure 3. Comparison of estimated and real average credit rating marks
-
- Table 1. Non-linear assignment of numeric values to credit rating marks
- Table 2. Comparison of arithmetic means and median when aggregating credit rating marks
- Table 3. Regression coefficients for the 17 indicators
- Table 4. Correlation matrix for 17 indicators
- Table 5. Trends of cumulative characteristics when gradually eliminating indicators from 17 to 2
-
- Afonso, A. (2003). Understanding the determinants of sovereign debt ratings: Evidence for the two leading agencies. Journal of Economics and Finance, 27(1), 56-74.
- Afonso, A., & Jalles, J. T. (2019). Quantitative easing and sovereign yield spreads: Euro-area time-varying evidence. Journal of International Financial Markets Institutions and Money, 58(1), 208-224.
- Bettendorf, T. (2019). Spillover effects of credit default risk in the euro area and the effects on the Euro: A GVAR approach. International Journal of Finance & Economics, 24(1), 296-312.
- Blanco, R., Brennan, S., & Marsh, I. W. (2005). An Empirical Analysis of the Dynamic Relation between Investment‐Grade Bonds and Credit Default Swaps. The Journal of Finance, 60(5).
- Bonam, D., & Lukkezen, J. (2019). Fiscal and Monetary Policy Coordination, Macroeconomic Stability, and Sovereign Risk Premia. Journal of Money Credit and Banking, 51(2-3), 581-616.
- Budinský, P. (2015). Relationship between Sovereign Ratings and CDS Prices. In J. Dyczkowska, & P. Kuźdowicz (Eds.), Controlling and Knowledge. Wroclaw: Publishing House of Wroclaw University of Economics.
- Cai, P., Kim, S., & Wu, E. (2019). Foreign direct investments from emerging markets: The push-pull effects of sovereign credit ratings. International Review of Financial Analysis, 61(1), 110-125.
- Cantor, R. M., & Packer, F. (1996). Determinants and Impact of Sovereign Credit Ratings. SSRN.
- Canuto, O., Pereira Dos Santos, P. F., & De Porto, P. C. (2012). Macroeconomics and Sovereign Risk Ratings. Journal of International Commerce, Economics and Policy, 3(2).
- Čermáková, E., & Mihola, J. (1989). Měření multikolinearity respektující závislost všech proměnných. Ekonomicko-matematický obzor, ročník 25, číslo 2. Praha: Vydává Československá akademie věd v ACADEMII.
- Česká Národní Banka. (2018). Zpráva o inflaci – III/2011. Praha.
- Chebbi, T. (2019). What does unconventional monetary policy do to stock markets in the euro area? International Journal of Finance and Economics, 24(1), 391-411.
- Countryeconomy. (n.d.). Sovereigns Ratings List 2011, 2017.
- Di Cesare, A. (2006). Do Market-Based Indicators Anticipate Rating Agencies?: Evidence for International Banks. Bank of Italy Economic Research Paper, 593.
- Gaillard, N. J. (2009). The determinants of Moody’s sub-sovereign ratings. International Research Journal of Finance and Economics, 31, 194-209.
- Garaj, V., & Šujan, I. (1980). Ekonometria. Bratislava: Alfa.
- Hebák, P. (2004). Vícerozměrné statistické metody (255 p.). Praha: Informatorium
- Hebák, P., & Hustopecký, J. (1987). Vícerozměrné statistické metody s aplikacemi. Praha: Alfa.
- Horny, G., Manganelli, S., & Mojon, B. (2018). Measuring Financial Fragmentation in the Euro Area Corporate Bond Market. Journal of Risk and Financial Management, 11(4), 74.
- Hull, J., Predescu, M., & White, A. (2004). The relationship between credit default swap spreads, bond yields, and credit rating announcements. Journal of Banking and Finance, 28(11), 2789-2811.
- Ismailescu, I., & Kazemi, H. (2010). The Reaction of Emerging Market Credit Default Swap Spreads to Sovereign Credit Rating Changes. Journal of Banking and Finance, 34(12), 2861-2873.
- King, M. R., Ongena, S. A. & Tarashev, N. (2011). Bank standalone credit ratings (BIS Working Papers No. 542).
- Kotěšovcová, J. (2013). Suverénní rating a kritéria jeho stanovení. Finančné trhy.
- Kotěšovcová, J. (2018). Komparace sovereign ratingu vybraných ratingových agentur. Scientia et Societas, 2, 59-78.
- Kou, J. & Varotto, S. (2008). Timeliness of Spread Implied Ratings. European Financial Management, 14(3), 503-527.
- Mellios, C. A., & Paget-Blanc, E. (2006). Which factors determine sovereign credit ratings? The European Journal of Finance, 12(4), 361-377.
- Mihola, J., & Bílková, D. (2014). Measurement of Multicolinearity Using Determinants of Correlation Matrix. International Journal of Mathematical Sciences, 34(2), 1543-1549.
- Mili, M. (2019). The impact of tradeoff between risk and return on mean reversion insovereign CDS markets. Research in International Business and Finance, 48, 187-200.
- Minenna, M., & Aversa, D., (2019). A Revised European Stability Mechanism to Realize Risk Sharing on Public Debts at Market Conditions and Realign Economic Cycles in the Euro Area. Economic Notes, 48(1), 62-103.
- Peškauskaitė, D. & Daiva, J. (2017). Companies Credit Risk Assessment Methods For Investment Decision Making. Mokslas: Lietuvos Ateitis. Vilnius: Vilnius Gediminas Technical University, 9(2), 220-229.
- Pinto, A. (2006). Control and Responsibility of Credit Rating Agencies in the United States. The American Journal of Compartive Law, 54 (American Law in the 21st Century: U.S. National Reports to the XVIIth International Congress of Comparative Law), 341-356.
- Riaz, Y., Shehzad, Ch. T., & Umar, Z. (2019). Pro-cyclical effect of sovereign rating changes on stock returns: a fact or factoid? Applied Economics, 15(51), 1588-1601.
- Rodriguez, I. M., Dandapani, K., & Lawrence, E. R. (2019). Measuring Sovereign Risk: Are CDS Spreads Better than Sovereign Credit Ratings? Financial Management, 48(1), 229-256.
- Sáiz, M. C., Azofra, S. S., & Olmo, B. T. (2019). The single supervision mechanism and contagion between bank and sovereign risk. Journal of Regulatory Economics, 55(1), 67-106.
- Setty, G. & Dodd, R. (2003). Credit Rating Agencies: Their Impact of Capital Flows to Developing Countries. Financial Policy Forum.
- Svítil, M. (2017). Comparison of banking rating systems. Proceedings of the 14 th International Scientific Conference (pp. 382-390).
- Tobera, P. (2019). Credit Rating and the Cost of Public Debt Service in Central and Eastern European Countries from 2005 to 2017. Gospodarka Narodowa, 21(1), 87-109.
- World Bank. (2017) World Development Indicators. DataBank.
- Yalta, A. T., & Yalta, A. Y. (2018). Are credit rating agencies regionally biased? Economic Systems, 42(4), 682-694.
- Zaremba, A., & Kambouris, G. (2019). The sources of momentum in international government bond returns. Applied Economics, 51(8), 848-857.
-
A model for analyzing the financial stability of banks in the VUCA-world conditions
Svitlana Khalatur , Liudmyla Velychko , Olena Pavlenko , Oleksandr Karamushka , Mariia Huba doi: http://dx.doi.org/10.21511/bbs.16(1).2021.16Banks and Bank Systems Volume 16, 2021 Issue #1 pp. 182-194 Views: 2388 Downloads: 588 TO CITE АНОТАЦІЯVUСA is a chaotic and rapidly changing business environment that, based on the variability, uncertainty, complexity and ambiguity of the modern world, transforms the approach of banks to the analysis of financial stability. The aim of the paper is to improve tools for monitoring the impact of VUCA-world conditions on the financial stability of banks, namely a model for studying and analyzing the impact of the modern business space “VUCA” on the financial stability of the country's banks. To test the model, the method of constructing regression equations in multifactor regression analysis is used. For this study, data from some Eastern European countries (Ukraine, Belarus, Latvia, Lithuania, Moldova) were used, and time series data were used for 10 years from 2010 to 2019.
Having considered the definition of “VUCA-world conditions”, the model of modern business space “VUCA” was developed when analyzing the activity of banks in the studied countries. Drivers, consequences, requirements and macroeconomic indicators of the countries’ activities in the VUСA-world conditions are determined. The VUCA-world conditions also consider the study of key macroeconomic indicators that allow building long-term relationships throughout the value chain. The analysis of the studied Eastern European countries showed that with the increase of factors of GDP growth, GNI per capita growth, research and development costs, foreign direct investment, and net inflow of 1%, the effective ratio of bank capital and assets also increases. The assessment, in contrast to the existing ones, makes it possible to consider the impact of the macroeconomic environment of banks on their financial stability. -
Determining and predicting correlation of macroeconomic indicators on credit risk caused by overdue credit
Asie Tsintsadze , Lela Oniani , Tamar Ghoghoberidze doi: http://dx.doi.org/10.21511/bbs.13(3).2018.11Banks and Bank Systems Volume 13, 2018 Issue #3 pp. 114-119 Views: 1382 Downloads: 350 TO CITE АНОТАЦІЯThe banking system guarantees the economic strength of the country. Its sustainability is due to the sustainability of the credit portfolio. Therefore, scientific research on banking risks is always relevant. Basel recommendations and central bank regulations provide risk minimization in case of default of borrower by creating risk reserve, but the high range of macroeconomic factors creates a basis for creating credit risk. The model, which determines the risk factors, may be structurally the same, but the quality of the influence of factors is different in various countries. The influence of macroeconomic factors is particularly evident in developing countries. The impact of economic factors in different countries is high in GDP of these countries. The article focuses on determining the influence of macroeconomic factors on credit risk of systematic banks in Georgia. The coefficients of individual macroeconomic indicators are calculated by using Pearson’s correlation. The credit risk ratio is taken from the bank’s overdue credits and credit portfolio ratio. Based on the correlation coefficients obtained, the expected risk of shock changes is calculated.
-
Research of macroeconomic disables of Ukraine
Economics of Development Volume 17, 2018 issue #4 pp. 20-29 Views: 1119 Downloads: 132 TO CITE АНОТАЦІЯThe crisis in the political and economic spheres in Ukraine has led to an aggravation of macroeconomic imbalances, which in turn worsen the socio-economic situation, complicate the moments of doing business, manifestation of violations and instability in the public administration sector and social tension in society. As the result is the accumulation of macroeconomic imbalances to a critical point that threatens the normal, gradual development of economic processes that should take place in the economic space of Ukraine. The article deals with the main imbalances indicators of the country's economy and their applicability under modern Ukrainian economic policy conditions. The interconnection of the main macro-instability factors in Ukraine economy and other countries of the world is considered, which allows to identity a number of endogenous (external) and exogenous (internal) factors that create imbalances. The mechanism of imbalances detection is proposed, which combines certain categories, methods, principles and methods of their research. The simulation model for identifying macroeconomic imbalances in the Ukrainian economy was developed, based on which the dynamic properties of the macroeconomic imbalances system were investigated, a short-term indicators forecast was constructed, and assessment of the imbalances probability in the future was implemented. Forecast macroeconomic indicators were estimated that fall into critical areas also the gross external debt, changes in the real effective exchange rate, changes in the share of the export market show that external imbalances and disproportion exist. Other macro indicators that form the imbalances table, according to projected calculations, show trends that are close to the ultimate limits and instability risks which confirms the vulnerability of the country's financial and economic system. The obtained forecasting results will allow to prevent new imbalances through the timely and appropriate rapid response management action.