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The estimation and determinants of emerging market country risk and the dynamic conditional correlation GARCH model

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  • Marshall, Andrew
  • Maulana, Tubagus
  • Tang, Leilei

Abstract

Country risk assessment is central to the international investment, which recently has increasingly focused on emerging markets (EM). In this paper we proxy for country risk in EM by using time-varying beta. We extend existing literature by applying a dynamic conditional correlation GARCH model. After confirming beta is time varying in twenty EM over the period January 1995 to December 2008 we investigate the GARCH (1,1) model and find the t-distribution generates the lowest forecast errors compared to the normal error distribution and a generalised error distribution. In a comparison of previous modelling techniques the results of our modified Diebold-Mariano test statistics suggest that the Kalman Filter model outperforms the GARCH model and the Schwert and Seguin (1990) model. Using a DCC-GARCH model our evidence suggests that considering dynamic betas can improve beta out-of-sample predicting ability and therefore offers potential gains for investors. Finally, we find dynamic betas across EM are strongly associated with each nation's interest rates, US interest rates and the Consumer Price Index (CPI) and to a lesser extent the exchange rates. Our results have some similarities to those in previous studies of developed markets in the economic determinants of time-varying beta but differences exist in the results on best model to forecast time-varying beta. These findings have implications for estimating country risk for investment and risk management purposes in EM.

Suggested Citation

  • Marshall, Andrew & Maulana, Tubagus & Tang, Leilei, 2009. "The estimation and determinants of emerging market country risk and the dynamic conditional correlation GARCH model," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 250-259, December.
    • Handle: RePEc:eee:finana:v:18:y:2009:i:5:p:250-259
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    2. Jelena Stankevičiene & Tatjana Sviderske & Algita Miečinskiene, 2013. "Relationship between Economic Security and Country Risk Indicators in EU Baltic Sea Region Countries," Entrepreneurial Business and Economics Review, Centre for Strategic and International Entrepreneurship at the Cracow University of Economics., vol. 1(3), pages 21-33.
    3. Wang, Hao & Xu, Ning & Yin, Haiyan & Ji, Hao, 2022. "The dynamic impact of monetary policy on financial stability in China after crises," Pacific-Basin Finance Journal, Elsevier, vol. 75(C).
    4. Hisham Al Refai & Gazi Mainul Hassan, 2018. "The Impact of Market-wide Volatility on Time-varying Risk: Evidence from Qatar Stock Exchange," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 17(2_suppl), pages 239-258, August.
    5. Hooy Chee-Wooi & Robert D. Brooks, 2015. "The Components of Systematic Risk and Their Determinants in The Malaysian Equity Market," Asian Academy of Management Journal of Accounting and Finance (AAMJAF), Penerbit Universiti Sains Malaysia, vol. 11(2), pages 151-176.
    6. Malin Song & Kuangnan Fang & Jing Zhang & Jianbin Wu, 2019. "The Co-movement Between Chinese Oil Market and Other Main International Oil Markets: A DCC-MGARCH Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1303-1318, December.

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