Investigation of the volatility spillover effect and dynamic conditional correlations in Tehran Stock Exchange using wavelet based Bayesian conditional variance heteroscedasticity

Document Type : Research Paper


1 urmia

2 uromieh university

3 Assistant Professor, Economics and Islamic Banking Department, Faculty of Economics, Kharazmi University, Tehran, Iran

4 Professor of Economics, Urmia University


Skewness, fat tails and frequency dimension are important features of financial time series that have not been taken into account in classical econometric models. Therefore, in this study, the Bayesian method for conditional variance heteroscedasticity based on wavelet analysis has been used to investigate the volatility spillover effect and dynamic conditional correlations in three sub-periods between the daily return data of selected Tehran Stock Exchange (TSE) indices during the period from December 14, 2008 to April 20, 2019. Sub-periods are defined according to the Iran nuclear deal and agreement between Iran and the P5+1, known as Joint Comprehensive Plan of Action (JCPOA), which includes the pre-JCPOA period, JCPOA period, and the period after United States withdrawal from the JCPOA. The results of the Bayesian DCC GARCH (1,1) model, with the rejection of the constant conditional correlation hypothesis versus the dynamic conditional correlation hypothesis based on posterior marginal distribution in all subsections, indicates that the impact of shocks on the volatility of stock returns in wavelets and sub-periods are not the same. Also, Bayesian dynamic conditional correlation graphs are recommended for each sub-periods and in each wavelet, a different stock for a suitable investment.


Main Subjects

  1. Aaltonen, J., & Östermark, R. (1997). A rolling test of Granger causality between the Finnish and Japanese security markets. Omega, 25(6), 635-642.
  2. Ahmad, W., Bhanumurthy, N. R., & Sehgal, S. (2014). The Eurozone crisis and its contagion effects on the European stock markets. Studies in Economics and Finance, 31(3), 325-352.‏
  3. Asai, M. (2016). Bayesian Analysis of General Asymmetric Multivariate GARCH Models and News Impact Curves. Journal of the Japan Statistical Society, 45(2), 129-144.‏
  4. Braverman, A., & Minca, A. (2014). Networks of common asset holdings: Aggregation and measures of vulnerability. Available at SSRN 2379669.‏
  5. Bala, D. A., & Takimoto, T. (2017). Stock markets volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach. Borsa Istanbul Review, 17(1), 25-48.‏
  6. Belke, A. H., & Osowski, T. U. (2019). Measuring fiscal spillovers in EMU and beyond: A Global VAR approach. Scottish Journal of Political Economy, 66(1), 54-93.‏
  7. Bauwens, L., & Laurent, S. (2005). A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models. Journal of Business & Economic Statistics, 23(3), 346-354.‏
  8. Bonga-Bonga, L. (2018). Uncovering equity market contagion among BRICS countries: an application of the multivariate GARCH model. The Quarterly Review of Economics and Finance, 67, 36-44.‏
  9. Christie, A. A. (1982). The stochastic behavior of common stock variances: Value, leverage and interest rate effects. Journal of financial Economics, 10(4), 407-432.‏
  10. Campbell, J. Y., & Hentschel, L. (1991). No news is good news: An asymmetric model of changing volatility in stock returns (No. w3742). National Bureau of Economic Research.‏
  11. Daubechies, I. (1990). The wavelet transform, time-frequency localization and signal analysis. IEEE transactions on information theory, 36(5), 961-1005.‏
  12. Dornbusch, R., Park, Y. C., & Claessens, S. (2000). Contagion: understanding how it spreads. The World Bank Research Observer, 15(2), 177-197.‏
  13. Dajcman, S. (2015). An empirical investigation of the nexus between sovereign bond yields and stock market returns–a multiscale approach. Engineering Economics, 26(2), 108-117.‏
  14. Doan, T. A. (2013). RATS handbook for ARCH. GARCH and Volatility Models, Estima, June. ‏
  15. Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.‏
  16. Ebadi, J., Elahi, N., & houshmand gohar S. (2019). Effect of Exchange Rate Change Shocks on Systemic Risk Index Among Mutual Funds. Journal of Economic Research and Policies, 27(89), 373-398 (In Persian).
  17. Engle, R. F., Ito, T., & Lin, W. L. (1988). Meteor showers or heat waves? Heteroskedastic intra-daily volatility in the foreign exchange market (No. w2609). National Bureau of Economic Research.‏
  18. European Commission (2014). Quarterly report on the Euro area 13(4), Brussels.
  19. Faini, R. (2006). Fiscal policy and interest rates in Europe. Economic Policy, 21(47), 444-489.‏
  20. Fioruci, J. A., Ehlers, R. S., & Andrade Filho, M. G. (2014). Bayesian multivariate GARCH models with dynamic correlations and asymmetric error distributions. Journal of Applied Statistics, 41(2), 320-331.‏
  21. Fischer, B. (1976). Studies of stock price volatility changes. In Proceedings of the Business and Economic Statistics Section (pp. 177-181).‏
  22. Fiorucci, J. A., Ehlers, R. S., Louzada, F., & Fiorucci, M. J. A. (2016). Package ‘bayesDccGarch’.‏
  23. Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The journal of Finance, 57(5), 2223-2261.‏
  24. Forbes, K., & Rigobon, R. (2000). Contagion in Latin America: Definitions, measurement, and policy implications (No. w7885). National Bureau of Economic Research.‏
  25. French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of financial Economics, 19(1), 3.‏
  26. Fry-McKibbin, R., & Hsiao, C. Y. L. (2018). Extremal dependence tests for contagion. Econometric Reviews, 37(6), 626-649.‏
  27. Gulzar, S., Mujtaba Kayani, G., Xiaofeng, H., Ayub, U., & Rafique, A. (2019). Financial cointegration and spillover effect of global financial crisis: a study of emerging Asian financial markets. Economic Research-Ekonomska Istraživanja, 32(1), 187-218.‏
  28. Hoseini Ebrahimabad, S.A., Heidari, H., Jahangiri, KH., & Ghaemi Asl, M. (2019). Using Bayesian Approach to Study the Time Varying Correlation among Selected Indices of Tehran Stock Exchange. Financial Research Journal, 21(1), 59-78 (In Persian).
  29. Hassan, S. A., & Malik, F. (2007). Multivariate GARCH modeling of sector volatility transmission. The Quarterly Review of Economics and Finance, 47(3), 470-480.‏
  30. Hou, Y. G., & Li, S. (2020). Volatility and skewness spillover between stock index and stock index futures markets during a crash period: New evidence from China. International Review of Economics & Finance, 66, 166-188.‏
  31. Jafari, M., Shakeri, A., & Mohammadi, T. (2018). Impact of Fluctuations in Financial Markets on Oil Prices and Iran's Economic Security. Journal Strategic Studies Quartely, 21(80), 101-134 (In Persian).
  32. Jian, Z., Wu, S., & Zhu, Z. (2018). Asymmetric extreme risk spillovers between the Chinese stock market and index futures market: An MV-CAViaR based intraday CoVaR approach. Emerging Markets Review, 37, 98-113.‏
  33. Jiang, Y., Jiang, C., Nie, H., & Mo, B. (2019). The time-varying linkages between global oil market and China's commodity sectors: Evidence from DCC-GJR-GARCH analyses. Energy, 166, 577-586.‏
  34. Jahangiri, K.H., & Hoseini Ebrahimabad, S. A. (2017). The Study of Monetary Policy, Exchange Rate and Gold Effects on the Stock Market in Iran Using MS-VAR-EGARCH Model. Financial Research Journal, 19(3), 389-414‏ (In Persian).
  35. Jahangiri, K.H., & Hekmati Farid, S. (2015). Investigating the Effects of Volatility Spillover between Stock, Gold, Oil and Exchange Markets. Journal of Economics Research, 15(56), 161-194 (In Persian).
  36. Khochiani, R., & Nademi, Y. (2018). Revisiting the Relationship between Inflation and Output Gap in Iranian Economy Using Wavelet Transform Approach. Journal of Economics Research, 69(2), 307-334 (In Persian).
  37. Karimi, S., Heidarian, M., & Dehghan, SH. (2018). Analysis of overflow effects between oil markets and Tehran Stock Exchange during multiple time scales; (using VAR-GARCH-BEKK model based on wavlet). Financial Economics Quarterly, 12(42), 25-46 (In Persian).
  38. Kodres, L. E., & Pritsker, M. (2002). A rational expectations model of financial contagion. The journal of finance, 57(2), 769-799.‏
  39. King, M. A., & Wadhwani, S. (1990). Transmission of volatility between stock markets. The Review of Financial Studies, 3(1), 5-33.‏
  40. Liu, X., An, H., Huang, S., & Wen, S. (2017). The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH–BEKK model. Physica A: Statistical Mechanics and its Applications, 465, 374-383.‏
  41. Lafuente, J. Á., & Ruiz, J. (2004). The New Market effect on return and volatility of Spanish stock indexes. Applied Financial Economics, 14(18), 1343-1350.‏
  42. Mikhaylov, A. Y. (2018). Volatility spillover effect between stock and exchange rate in oil exporting countries. International Journal of Energy Economics and Policy, 8(3), 321-326.‏
  43. Malik, F., & Ewing, B. T. (2009). Volatility transmission between oil prices and equity sector returns. International Review of Financial Analysis, 3(18), 95-100.‏
  44. Masson, M. P. R. (1998). Contagion: Monsoonal effects, spillovers, and jumps between multiple equilibria (No. 98-142). International Monetary Fund.‏
  45. Majdoub, J., & Sassi, S. B. (2017). Volatility spillover and hedging effectiveness among China and emerging Asian Islamic equity indexes. Emerging Markets Review, 31, 16-31.‏
  46. Nazlioglu, S., Soytas, U., & Gupta, R. (2015). Oil prices and financial stress: A volatility spillover analysis. Energy Policy, 82, 278-288.‏
  47. Nowrouzifar, T., fattahi, S., sohaili, K. (2019). The Impact of Economic Sanctions on the Amount of Dependence between Oil and Financial Market (Extremal Dependence Approach). Economic Modeling, 13(45), 1-17 (In Persian).
  48. Percival, D. B., & Walden, A. T. (2000). Wavelet methods for time series analysis (Vol. 4). Cambridge university press.‏
  49. Pretorius, E. (2002). Economic determinants of emerging stock market interdependence. Emerging Markets Review, 3(1), 84-105.‏
  50. Pindyck, R. S. (1983). Risk, inflation, and the stock market (No. w1186). National Bureau of Economic Research.‏
  51. Pritsker, M. (2001). The channels for financial contagion. In International financial contagion (pp. 67-95). Springer, Boston, MA.‏
  52. Ross, S. A. (1989). Information and volatility: The no‐arbitrage martingale approach to timing and resolution irrelevancy. The Journal of Finance, 44(1), 1-17.‏
  53. Shiferaw, Y. A. (2019). Time-varying correlation between agricultural commodity and energy price dynamics with Bayesian multivariate DCC-GARCH models. Physica A: Statistical Mechanics and its Applications, 526, 120807.‏
  54. Valls Ruiz, N. (2014). Volatility in financial markets: The impact of the global financial crisis.‏ 
  55. Virbickaitė, A., Ausín, M. C., & Galeano, P. (2016). A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection. Computational statistics & data analysis, 100, 814-829.‏
  56. Wolf, E. R. (1999). Peasant wars of the twentieth century. University of Oklahoma Press.‏
  57. Yin, K., Liu, Z., & Jin, X. (2020). Interindustry volatility spillover effects in China’s stock market. Physica A: Statistical Mechanics and its Applications, 539, 122936.‏
  58. Yin, K., Liu, Z., & Liu, P. (2017). Trend analysis of global stock market linkage based on a dynamic conditional correlation network. Journal of Business Economics and Management, 18(4), 779-800.