Document Type : Research Paper

Authors

1 Professor, Economics, Allameh Tabataba`i University, Tehran, Iran

2 Ph.D. Student, Economics, Allameh Tabataba`i University, Tehran, Iran

Abstract

The expansion of the globalization process has increased the relationships among financial markets in different countries, which itself has motivated investors to move among them to make more profit. Given the situation in Iran after sanctions, the possibility of investing in well-known financial markets is facing with the risk of sanctions. The present study aims to evaluate the existence of volatility spillover among the financial markets of Iran and Islamic oil exporters countries.  To this aim, a multivariate factor stochastic volatility (SV) model and stock price index data were used with daily frequency for the period 12/05/2008-02/19/2020. Based on the results, the main hypothesis that the volatility spillover among the financial markets of OPEC oil-exporting Islamic countries follows a common and uniform random trend is accepted for the United Arab Emirates, Saudi Arabia, and Qatar, but not for Iran and Nigeria. Therefore, diversifying the portfolio for Iranian investors in the financial markets of OPEC Islamic oil exporters can reduce the investment risk in the long run which make such economies an appropriate investment destination for Iranians due to the conditions of sanctions.

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Main Subjects

Abbas, Q., Khan, S., & Shah, S. Z. A. (2013). Volatility transmission in regional Asian stock markets. Emerging Markets Review, 16, 66-77.
Abbes, M. B., & Trichilli, Y. (2015). Islamic stock markets and potential diversification benefits. Borsa Istanbul Review, 15(2), 93-105.
Doz, C., & Renault, E. (2006). Factor stochastic volatility in mean models : a GMM approach. Econometric Reviews, 25(2-3), 275-309.
Engle, R. F., & Patton, A. J. (2007). What good is a volatility model?. In Forecasting volatility in the financial markets (pp. 47-63). Butterworth-Heinemann.
Fama, E. F. (1995). Random walks in stock market prices. Financial analysts journal, 51(1), 75-80.
Forbes, K., & Rigobon, R. (2000). No Contagion, only interdependence. Massachusetts Institute of Technology, Sloan School of Management. Working Paper.
Harris, R. D., & Pisedtasalasai, A. (2006). Return and volatility spillovers between large and small stocks in the UK. Journal of Business Finance & Accounting , 33(910), 1556-1571.
Harvey, A., Ruiz, E., & Shephard, N. (1994). Multivariate stochastic variance models. Review of Economic Studies, 61(2), 247-264.
Hosseinioun, N., Behname, M., Ebrahimi Salari, T. (2016). Volatility transmission of the rate of returns in Iranian stock, gold and foreign currency markets. Iranian Journal of Economic Research, 21(66), 123-150. doi: 10.22054/ijer.2016.7049, [In Persian].
Jacquier, E., Polson, N. G., & Rossi, P. (1999). Stochastic volatility: Univariate and multivariateextensions. CIRANO.
Jorion, P. (1985). International portfolio diversification with estimation risk. Journal of Business, 259-278.
Khiabani, N., & Dehghani, M. (2014). The role of oil market in explaining the volatility of gold and foreign exchange (Dollars/Euro) markets. Iranian Journal of Economic Research, 19(58), 207-238, [In Persian].
Majdoub, J., & Mansour, W. (2014). Islamic equity market integration and volatility spillover between emerging and US stock markets. The North American Journal of Economics and Finance, 29, 452-470.
Mamipour, S., & Feli, A. (2017). The impact of oil price volatility on Tehran stock market at sector-level: A variance decomposition approach. Monetary & Financial Economics, 24(13), 205-236. doi: 10.22067/pm.v24i14.58846, [In Persian].
Melino, A., & Turnbull, S. M. (1990). Pricing foreign currency options with stochastic volatility. Journal of econometrics, 45(1-2), 239-265.
Moghaddas Bayat, M., Shirinbakhsh, S., & Mohammadi, T. (2018). Analyzing volatility of Tehran stock exchange using MSBVAR-DCC model. ـJournal of Financial Management Perspective, 8(22), 97-112, [In Persian].
Neaime, S. (2012). The global financial crisis, financial linkages and correlations in returns and volatilities in emerging MENA stock markets. Emerging Markets Review, 13(3), 268-282.
Pitt, M. K., & Shephard, N. (1999). Filtering via simulation: Auxiliary particle filters. Journal of the American statistical association, 94(446), 590-599.
Poon, S. H., & Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of economic literature, 41(2), 478-539.
Pourebadolahan, M., Asgharpour, H., & Zolghadr, H. (2015). Examining relationship between stock prices and exchange rate in oil-exporting countries. Economic Development Policy, 2(4), 61-86. doi: 10.22051/edp.2015.2072, [In Persian].
Rajgopal, S., & Venkatachalam, M. (2011). Financial reporting quality and idiosyncratic return volatility. Journal of Accounting and Economics, 51(1-2), 1-20.
Ross, S. A. (2013). The arbitrage theory of capital asset pricing. In Handbook of the fundamentals of financial decision making: Part I (pp. 11-30).  
Seyedhosseini S.M., & Ebrahimi S.B. (2013). Volatility transmission analysis among the stock markets, case study, Iran, Turkey and Uae stock markets.  Financial Knowledge of Security Analysis (Financial Studies), 19(6), 81-97, [In Persian].
Shephard, N. (1996). Statistical aspects of ARCH and stochastic volatility. Monographs on Statistics and Applied Probability, 65, 1-68.
Shiller, R. J. (1980). Do stock prices move too much to be justified by subsequent changes in dividends? (No. w0456). National Bureau of Economic Research.
Taylor, J. W. (2004). Smooth transition exponential smoothing. Journal of Forecasting, 23(6), 385-404.
Tsay, R. S. (2005). Analysis of financial time series(Vol. 543). John wiley & sons.
Vuolteenaho, T. (2002). What drives firm‐level stock returns?. The Journal of Finance, 57(1), 233-264.
Xu, X. E., & Fung, H. G. (2002). Information flows across markets: evidence from China–backed stocksdual–listed in Hong Kong and New York. Financial review, 37(4), 563-588.
Zhou, X., Nakajima, J., & West, M. (2014). Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models. International Journal of Forecasting, 30(4), 963-980.