Abrupt Changes in Volatility: Evidence from TEPIX Index in Tehran Stock Exchange

Document Type: Research Paper

Authors

1 Professor, Faculty of Economics, University of Tehran, Iran,

2 PhD, Faculty of Economics, University of Tehran, Iran

Abstract

In this paper, we have examined abrupt changes in volatility of TEPIX index in Tehran stock exchange during August 23, 2010 to June 12, 2014. Applying the iterated cumulative sum of squares (ICSS) algorithm proposed by Inclan and Tiao (1994) and the modified version of this algorithm consisting Kappa 1 and Kappa 2 test statistics developed by Sansó et al. (2004), we have specified that the detection of abrupt changes are mainly explained by local economic and political factors and probably they are behind those changes. This finding is in line with that of Aggarwal et al. (1999) who discovered that country-specific factors play a key role in determining those sudden shifts in financial markets. In addition, the results of this study ratify the findings of the previous ones suggesting that, when the abrupt changes are embedded into standard GARCH models, the estimated persistence of volatility is decreased significantly.

Keywords


  1. Aggarwal, R., Inclan, C. and Leal, R., (1999). ‘Volatility in emerging markets’, Journal of Financial and Quantitative Analysis, 34, 33-55.
  2. Andreou, E. and Ghysels, E., (2002). ‘Detecting multiple breaks in financial market volatility dynamics’. Journal of Applied Econometrics, 17, 579-600.
  3. Bollerslev, T., Chou, R. Y. and Kroner, K. F., (1992). ‘ARCH modeling in finance’, Journal of Econometrics, 52, 5-59.
  4. Cai, J. (1994). ‘A Markov model of switching-regime ARCH’. Journal of Business and Economic Statistics, 12, 309–316. Fasano, U., & Iqbal, I. (2003). GCC countries: From oil dependence to diversification, www.imf.org/external/pubs/med/2003/eng/fasano/index.htm
  5. Charles, A. and Darné, O., (2014). ‘Volatility persistence in crude oil markets’. Energy Policy, 65, 729–742.
  6. Ewing B.T. and Malik F. (2010). ‘Estimating volatility persistence in oil prices under structural breaks’. The Financial Review, 45, 1011-1023.
  7. Fang, W.S., Miller, S. M. and Lee, C.S., (2008). ‘Cross-Country Evidence on Output Growth Volatility: Nonstationary Variance and Garch Models’. Scottish Journal of Political Economy, 55(4), Available at SSRN: http://ssrn.com/abstract=981265 or http://dx.doi.org/10.2139/ssrn.981265.
  8. Hamilton, J. D. and Susmel, R., (1994), ‘Autoregressive Conditional Heteroskedasticity and Changes in Regime’, Journal of Econometrics, 64(1-2), 307-333.
  9. Hammoudeh S. and Li H. (2008). ‘Sudden changes in volatility in emerging markets: The case of Gulf Arab stocks markets’. International Review of Financial Analysis, 17, 47-63.
  10. Inclan, C. and Tiao, G., (1994). ‘Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance’, Journal of American Statistical Association, 89, 913-923.
  11. Kang, S.H., Hwan, G.C. and Seong, M.Y., (2009), ‘Modeling sudden volatility changes: Evidence from Japanese and Korean stock markets’. Physica A, 388, 3543_3550.
  12. Karoglou, M., (2010). ‘Breaking down the non-normality of stock returns’, The European Journal of Finance, 16(1), 79-95.
  13. Kokoszka P. and Leipus R. (2000). Change-point estimation in ARCH models.Bernoulli, 6, 513-539.
  14. Kumar, D. and Maheswaran, S., (2013). ‘Detecting sudden changes in volatility estimated from high, low and closing prices’. Economic Modelling, 31, 484–491.
  15. Lastrapes, W., 1989, ‘Exchange Rate Volatility and U.S. Monetary Policy: An ARCH Application’, Journal of Money, Credit and Banking, 21(1), 66-77.
  16. Newey, W.K. and West, K.D., (1994). ‘Automatic lag selection in covariance matrix estimation’. Review of Economic Studies 61 (4), 631–654.
  17. Poterba, J.M. and Summers, L.H., (1988). ‘Mean reversion in stock prices: Evidence and Implications’. Journal of Financial Economics, 22(1), 27-59.
  18. Ross, G.J. (2013) ‘Modelling financial volatility in the presence of abrupt Changes’. Physica A, 392,350-360.
  19. Sansó, A., Aragó, V. and Carrion-i-Silvestre, J. L., 2004, ‘Testing for Changes in the Unconditional Variance of Financial Time Series’, Revista de Economía Financiera, 4,32-53.
  20. Todea, A. and Petrescu, D., (2012). ‘Sudden changes in volatility - the case of the five Financial Investment Companies in Romania’. Procedia Economics and Finance, 3, 40 – 48.
  21. Výrost, T., Baumohl, E., Lyocsa, S., (2011), ‘On the relationship of persistence and number of breaks in volatility: new evidence for three CEE countries’. available online at http://mpra.ub.uni-muenchen.de/27927/.
  22. Wang, P. and Moore, T., (2009), ‘Sudden Changes in Volatility: The Case of Five Central European Stock Markets’, Journal of International Financial Markets Institutions and Money, 19(1), 33-46.