Selecting an Appropriate Model to Study the Transmission Volatility between the Financial Markets of Selected Islamic Oil Exporting Countries

Document Type : Research Paper


1 Ph.D. Candidate in Economics, University of Allameh Tabataba'i

2 Professor of Economics, University of Allameh Tabataba'i

3 Associate Professor of Economics, University of Allameh Tabataba'i


In recent years, with the expansion of the globalization process, the relationship between the financial markets of different countries, including developing countries such as OPEC oil-exporting islamic countries, has increased more than ever, resulting in modeling the volatility of returns in international stock markets in terms of science. finance has become an important issue. the importance of this issue in risk management is the pricing of financial derivatives and the coverage of the resulting risk, marketing and selection of financial portfolios. experimental results show that the modeling of transmission volatility is very important in obtaining the results of the presence or absence of transmission volatility between different financial markets. accordingly, in the present paper, two approaches of multivariate dynamic conditional correlation (DCC) and multivariate factor stochastic volatility in modeling transmission volatility between selected markets using the risk value and calculating the loss function are compared. to obtain experimental results from stock price index data in the financial markets of five selected islamic and oil countries, including Iran, Saudi Arabia, UAE, Qatar and Nigeria with daily frequency during the period 12/05/2008 to 19/02/2020 used. experimental results of Kupiec and christoffersen tests show that both approaches can be used to calculate the risk value, but due to the comparison of loss functions, the multivariate factor stochastic volatility modeling approach compared to the GARCH approach of dynamic correlation (DCC) multivariate is preferred


Main Subjects

  1. Abbas, Q., Khan, S., & Shah, S. Z. A. (2013). Volatility transmission in regional Asian stock markets. Emerging Markets Review, 16, 66-77.
  2. Abbasinejad, H., Mohammadi, S., & Ebrahimi, S. (2014).  Comparing Between Multivariate Volatility Models in Estimation of Exchange Rate and Stock Index Relationship. Journal of Investment Knowledge, 3 (11), 201-221 (In Persian).
  3. Abbes, M. B., & Trichilli, Y. (2015). Islamic stock markets and potential diversification benefits. Borsa Istanbul Review, 15(2), 93-105.
  4. Branger, N., Kraft, H., & Meinerding, C. (2009). What is the impact of stock market contagion on an investor’s portfolio choice?. Insurance: Mathematics and Economics, 45(1), 94-112.
  5. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
  6. Caporin, M. (2008). Evaluating value-at-risk measures in the presence of long memory conditional volatility. The Journal of Risk, 10(3), 79.
  7. Engle, R. F. & Patton, J.A. (2001). What Good is a Volatility Model?. NYU Working Paper. No. S-DRP-01-03.
  8. Fama, E. (1965). The Behavior of Stock Market Prices. Journal of Business, 38: 34–105.
  9. Flavin, M. (1983). Excess Volatility in the Financial Markets: A Reassessment of the Empirical Evidence. Journal of Political Economy, 91, 929-956.
  10. Forbes, K., & Rigobon, R. (2000). No Contagion, only interdependence. Massachusetts Institute of Technology, Sloan School of Management. Working Paper.
  11. Harris, R. D., & Pisedtasalasai, A. (2006). Return and volatility spillovers between large and small stocks in the UK. Journal of Business Finance & Accounting, 33(9‐10), 1556-1571.
  12. Harvey, A., Ruiz, E., & Shephard, N. (1994). Multivariate stochastic variance models. The Review of Economic Studies, 61(2), 247-264.
  13. Jacquier, E., Polson, N. G., & Rossi, P. (1999). Stochastic volatility: Univariate and multivariate extensions. CIRANO.
  14. Jorion, P. (1985). International portfolio diversification with estimation risk. Journal of Business, 259-278.
  15. 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.
  16. Melino, A., & Turnbull, S. M. (1990). Pricing foreign currency options with stochastic volatility. Journal of econometrics, 45(1-2), 239-265.
  17. Moftakhar Daryaie Nejad, K. (2012). Returns and Volatility Spillover Effects on the Estimated VaR of Gold and Exchange Rate Portfolio.Master Thesis, Faculty of Management and Economics, Sharif University of Technology (In Persian).
  18. 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.
  19. Pakizeh, K. (2011). Volatility and Returns (Empirical Evidence from Tehran and International Stock Exchanges). Journal of Economic Modeling Research, 1(2), 1-20 (In Persian).
  20. Poon, S. H., & Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of economic literature, 41(2), 478-539.
  21. Rajgopal, S., & Venkatachalam, M. (2011). Financial reporting quality and idiosyncratic return volatility. Journal of Accounting and Economics, 51(1-2), 1-20.
  22. Sajjad, R., Hedayati, S., & Hedayati, S. (2013). Comparison of stochastic volatility model and GARCH models, by calculating the risk value. Journal of Financial Engineering and Securities Management, 4(15), 79-97 (In Persian).
  23. Sajjad, R., Hedayati, S., & Hedayati, S. (2013). Estimation of Value at Risk by using Extreme Value Theory in Tehran Stock Exchange. Journal of Investment Knowledge, 3(9), 133-155 (In Persian).
  24. Shiller, R. J. (2001). Do stock prices move too much to be justified by subsequent changes in dividends?  American Economic Review, 71, 421-436.
  25. Taylor, J. W. (2004). Smooth transition exponential smoothing. Journal of Forecasting, 23(6), 385-404.
  26. Tsay R. S. (2002). Analysis of Financial Time Series, John Wiley & Sons.
  27. 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.
  28. Vuolteenaho, T. (2002). What drives firm‐level stock returns?. The Journal of Finance, 57(1), 233-264.
  29. Xu, X. E., & Fung, H. G. (2002). Information flows across markets: evidence from China–backed stocks dual–listed in Hong Kong and New York. Financial review, 37(4), 563-588.