Application of modified GARCH methodology: developed financial markets versus emerging financial markets

  • Nenad Penezić Full Professor, Phd, Educons University, Faculty of Business Economy, Sremska Kamenica, Vojvode Putnika 85-87
  • Goran Anđelić Full Professor, Phd, Educons University, Faculty of Business Economy, Sremska Kamenica, Vojvode Putnika 85-87
  • Marko R Milošević Research Assistant, Phd, Educons University, Faculty of Business Economy, Sremska Kamenica, Vojvode Putnika 85-87
  • Vilmoš Tot Associate Professor, Phd, Union – Nikola Tesla University, Faculty of Strategic and Operational Management, Staro Sajmište 29, Belgrade
Keywords: GARCH, risk, developed financial markets, emerging markets


The subject of this research is to analyze and test the modified GARCH methodology in terms of quantifying the impact of inflation rates, interest rates on government bonds, reference interest rates, and exchange rates on daily rates of return on investment activities in the observed financial markets of North America, Serbia and Croatia. The aim of the research, i.e. a special focus in the research, is to compare the obtained results between the developed financial markets and the financial markets of developing countries, as well as to test the modified GARCH methodology in the observed financial markets. The key indicators in the research, presumed to affect the daily return rates, were the following: inflation rate, interest rates on government bonds, reference interest rate and exchange rate. The time period covered by the research is from 2005 to 2017, where the width of the research time horizon allows testing the modified GARCH methodology in the periods before, during and after the global financial crisis. In addition to the use of modified GARCH econometric models, the research methodology includes the use of AIC, SIC and HQC (Akaike, Schwarz and Hannan-Quinn) criteria for selecting the best models, as well as the appropriate tests that are suitable for and/or adapted to the specific characteristics of financial markets of both developed and developing countries. The research results confirm the role and importance of the modified GARCH methodology for effective investment risk quantification in developed financial markets versus the financial markets of developing countries. In this sense, the obtained research results will be useful to both the academic community and the professional public in the context of investment decision making.


Ali, R., & Afzal, M. (2012). Impact of global financial crisis on stock markets: Evidence from Pakistan and India. Journal of Business Management and Economics, 3 (7), 275-282.

Andreou, E., Matsi, M., & Savvides, A. (2013). Stock and foreign exchange market linkages in emerging economies. Journal of International Financial Markets, Institutions and Money, 27, 248-268.

Brooks, C. (2008), Introductory Econometrics for Finance – second edition. Cambridge, England, UK: Cambridge University Press.

Cakan, E., Doytch, N., & Upadhyaya, K.P. (2015). Does US macroeconomic news make emerging financial markets riskier?. Borsa Istanbul Review, 15 (1), 37-43.

Caporale, G.M., Spagnolo, F., & Spagnolo, N. (2016). Macro news and stock returns in the Euro area: A VAR-GARCH-in-mean analysis. International Review of Financial Analysis, 45, 180-188.

Dedi, L., & Yavas, B.F. (2016). Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies. Cogent Economics & Finance, 4 (1), 1266788.

Duppati, G., Hou, Y.G., & Scrimgeour, F. (2017). The dynamics of price discovery for cross-listed stocks evidence from US and Chinese markets. Cogent Economics & Finance, 5 (1), 1389675.

Geetha, C., Mohidin, R., Chandran, V.V., & Chong, V. (2011). The relationship between inflation and stock market: Evidence from Malaysia, United States and China. International journal of economics and management sciences, 1 (2), 1-16.

Gujarati, D. (2010), Basic Econometrics, Fourth Edition. US: The McGraw−Hill Companies.

Kim, H.Y., & Won, C.H. (2018). Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. Expert Systems with Applications, 103, 25-37.

Li, T., Zhong, J., & Huang, Z. (2020). Potential dependence of financial cycles between emerging and developed countries: Based on ARIMA-GARCH Copula model. Emerging Markets Finance and Trade, 56 (6), 1237-1250.

Prasad, N., Grant, A., & Kim, S.J. (2018). Time varying volatility indices and their determinants: Evidence from developed and emerging stock markets. International Review of Financial Analysis, 60, 115-126.

Rejeb, A.B., & Arfaoui, M. (2016). Financial market interdependencies: A quantile regression analysis of volatility spillover. Research in International Business and Finance, 36, 140-157.

Shaikh, I., & Padhi, P. (2013). The information content of macroeconomic news. Procedia Economics and Finance, 5, 686-695.

Zukarnain Z., Sofian S. (2012). “Empirical Evidence on the Relationship between Stock Market Volatility and Macroeconomics Volatility in Malaysia”. Journal of Business Studies Quarterly, 4 (2), 61-71.

Internet and other sources:

EViews8User’sGuideII, 2013, [accessed 29.03.2018.].

European Central Bank, [accessed 24.04.2018.].

The Federal Reserve System, [accessed 24.04.2018.].

Zagreb Stock Exchange, 2016. [accessed 13.03.2018.].

Investor Bulletin, Interest rate risk —When Interest rates Go up, Prices of Fixed-rate. [accessed 29.03.2018.].

Belgrade Stock Exchange, 2017. [accessed 13.03.2018.].

Central Bank of Croatia, [accessed 15.04.2018.].

Central Bank of Serbia, [accessed 15.04.2018.].

Dow Jones Industrial Average, [accessed 13.03.2018.].

Original Scientific Paper