Experimental Study of the Effect of Exchange Rate Volatility Spillover on Capital Market index: A Case Study of Selected Oil-Exporting OPEC Member Countries

Document Type : Original Article

Authors

1 PhD candidate, Islamic Azad University Kish International Branch, Islamic Azad University, Kish Island, Iran.

2 Assistant Professor, Department of Financial Engineering ,Kish International Branch, Islamic Azad University, Kish Island, Iran

10.30495/ijfma.2023.69011.1902

Abstract

Exchange rate fluctuations are always one of the variables affecting economic activities and thereby affecting the behavior of actors in capital markets. Therefore, the study of these relationships is of particular importance. On the other hand, the present study is conducted to study the effect of exchange rate fluctuations’ spillover on capital market indicators of selected oil-exporting OPEC member countries. These countries are similar in terms of economic reliance on oil resources, but different in terms of economic growth rate and capital market characteristics. The research period is from 2016 to 2021 and data related to exchange rates and capital market indicators of Iran, Iraq, UAE, Qatar, Saudi Arabia, Oman and Bahrain are collected from reliable sources and using multivariate GARCH models. Findings from the experimental data show that currency fluctuations in the capital market affect the capital market index of Iran, Iraq, UAE, Qatar, Saudi Arabia, Oman, and Bahrain. This effect is asymmetric only in the Iranian capital market, and is symmetrical in other countries.

Keywords


  1. 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.
  2. Antonakakis, N., & Gabauer, D. (2021). International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression, International Review of Financial Analysis, 166, 117-138.
  3. Belke, A., Dubova, I., & Volz, U. (2018). Bond yield spillovers from major advanced economies to emerging Asia. Pacific Economic Review, 23(1), 109–126.
  4. Claus, E., Claus, I., & Krippner, L. (2016). Monetary policy spillovers across the Pacific when interest rates are at the zero lower bound. Reserve Bank of New Zealand discussion paper series dp2016/08.. Reserve Bank of New Zealand.
  5. Dale‐ Olsen, H., 2012. Executive pay determination and firm performance - Empirical evidence from a compressed wage environment. The Manchester School, 80(3), pp.355-376.
  6. Diebold, F. X., & Yilmaz, K (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66.
  7. Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 11(1), 122-150.
  8. Engle, R. F., & Susmel, R. (1993). Common volatility in international equity markets. Journal of Business & Economic Statistics, 11(2), 167-176.
  9. Garcia-de Andoain, C., & Kremer, M. (2017). Beyond spreads: Measuring sovereign market stress in the Euro Area. Economics Letters, 159, 153–156.
  10. Kang SH, Uddin GS, Troster V, Yoon S-Min, (2021) Directional Spillover Effects between ASEAN and World Stock Markets, Journal of Multinational Financial Management, doi: https://doi.org/10.1016/j.mulfin.2021.100592
  11. Kim, S., Lee, B.-S., 2015. Spillover effects of the U.S. financial crisis on financial markets in emerging Asian countries. International Review of Economics & Finance 39, 192-210.
  12. Kim, S., Lee, J.-W., 2012. Real and financial integration in East Asia. Review of International Economics 20 (2), 332-349.
  13. Kohonen, A., 2013. On detection of volatility spillovers in overlapping stock markets. Journal of Empirical Finance, 22, pp.140-15
  14. Korobilis, D., & Yilmaz, K. (2018). Measuring dynamic connectedness with large Bayesian VAR models. Technical report.. University of Essex, Essex Business School.
  15. Krippner, L. (2013). A tractable framework for zero lower bound Gaussian term structure models. Reserve Bank of New Zealand discussion paper series dp2013/02.. Reserve Bank of New Zealand.
  16. Li, Y., Giles, D.E., 2015. Modelling volatility spillover effects between developed stock markets and Asian emerging stock markets. International Journal of Finance & Economics 20 (2), 155-177.
  17. Mensi, W., Hammoudeh,S., Kang, S.H., 2017a. Dynamic linkages between developed and BRICS stock markets: portfolio risk analysis. Financial Research Letters 21, 26-33.
  18. Mensi, W., Al-Yahyaee, K.H., Kang, S.H., 2017b. Time-varying volatility spillovers between stock and precious metal markets with portfolio implications. Resources Policy 53, 88-102.
  19. Morales-Zumaquero, A., & Sosvilla-Rivero, S. (2016). Volatility Spillovers between Foreign-Exchange and Stock Markets. The Quarterly Review of Economics and Finance. Volume 70, Pages 121-136.
  20. Nakamura, E., & Steinsson, J. (2018a). High frequency identification of  onetary non-neutrality: The information effect. Quarterly Journal of Economics.
  21. Santamaria, G.S., Gomez-Gonzalez, J. E., Hurtado-Guarin, J. L., & Melo-Velandia, L. F. (2017). Stock market volatility spillovers: Evidence for Latin America. Finance Research Letters, 20, 207-216.
  22. Tillmann, P. (2016). Unconventional monetary policy and the spillovers to emerging markets. Journal of International Money and Finance, 66, 136–156.
  23. Xiong, Z. & Han, L. (2015). Volatility spillover effect between financial markets: evidence since the reform of the RMB exchange rate mechanism. Financial Innovation, Volume 1(1).
  24. Wu, J. C., & Xia, F. D. (2016). Measuring the macroeconomic impact of monetary policy at the zero lower bound. Journal of Money, Credit and Banking, 48(2-3), 253–291.
  25. Zhang, B., Wang, P., (2022). Return and volatility spillovers between China and world oil markets. Economic Modelling 42, 413-420.