Financial Management Perspective

Financial Management Perspective

Investigating the Volatility Persistence in Tehran Stock Exchange

Document Type : Original Article

Authors
Moslem Nilchi 1
Daryush Farid 2
Moslem Peymani 3
Hamidreza Mirzaei 4
1 Ph.D. Candidate in Financial Engineering, Yazd Universtity, Yazd,
2 Associate Prof, Department of Accounting and Finance, Yazd University, Yazd, Iran.
3 Assistant Prof, Department of Finance and Banking, Allameh Tabataba'i University, Tehran, Iran.
4 Assistant prof, Department of Accounting and Finance , Yazd University, Yazd.Iran
10.52547/jfmp.12.39.9
Abstract
One of the important factors affecting the participation of investors in the stock market is the existence of information about the trends and price volatility of this market. In the past years, the economic sanctions and the Covid-19 epidemic have affected the Iran stock market. The continuous decrease of the stock market index is one of the consequences of volatility persistence waves caused by these events. Some financial theories have shown that the decline in stock prices can be caused by the existence of a unit root in the volatility of the market's price returns. In this research, the hypothesis of the drop in stock prices due to the presence of a unit root in the volatility was investigated with the data of the Tehran Stock Exchange index between 2016.September.21 to 2022.March.19 and using the stochastic volatility model introduced by Su and Lee (1999). The findings of this paper indicate that the posterior estimate of the coefficient of ϕ in the Stochastic volatility model is equal to one, therefore, it is not possible to reject the inappropriate performance of the market and the hypothesis of the fall of the Tehran Stock Exchange index as a result of the volatility persistence.
Keywords
Unit Root
Volatility Persistence
Stock Price Index
Stochastic Volatility
  1. Almenberg, J., & Dreber, A. (2015). Gender, stock market participation and financial literacy. Economics Letters, 137, 140-142.
  2. Bandi, F.M. and Phillips, P.C.B. (2003). Fully Nonparametric Estimation of Scalar Diffusion Models. Econometrica, 71(1), 241-83.
  3. Bentes, S. R. (2021). How COVID-19 has affected stock market persistence? Evidence from the G7’s. Physica A: Statistical Mechanics and its Applications, 581, 126210.
  4. Black, F. (1976). Studies of stock market volatility changes. 1976 Proceedings of the American statistical association bisiness and economic statistics section.
  5. Broto, C., & Ruiz, E. (2004). Estimation methods for stochastic volatility models: a survey. Journal of Economic surveys, 18(5), 613-649.
  6. Chan, J. C. and Grant, A. L. (2016). Modeling energy price dynamics: GARCH versus stochastic volatility. Energy Economics, Vol. 54, pp. 182-189.
  7. Chib, S. (1995). Marginal likelihood from the Gibbs output. Journal of the american statistical association, 90(432), 1313-1321.
  8. Chou, R.Y. (1988). Volatility Persistency and stock valuation: some empirical evidence using GARCH. Journal of Applied Econometrics, 3, 279-294.
  9. Engle, R.F. and Bollerslev, T. (1986). Modeling the persistence of conditional variances. Econometric Reviews, 5, 1-50.
  10. Farhadian, A., Rostami, M., Nilchi, M. (2020). Compare Canonical stochastic volatility model of focal MSGJR-GARCH to measure the volatility of stock returns and calculating VaR.  ـJournal of Financial Management Perspective, 10(32),131-158.(in Persian)
  11. Koop. Bayesian Econometrics. Wiley & Sons, New York, 2003.
  12. Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B. (2004). Bayesian data analysis, 2nd edn. London: Chapman & Hall.
  13. Geweke, J. (2007). Bayesian model comparison and validation, American Economic Review, 97(2), 60-64.
  14. Hansen, P. R., Huang, Z., & Shek, H. H. (2012). Realized GARCH: a joint model for returns and realized measures of volatility, Journal of Applied Econometrics, 27(6), 877-906.
  15. Harvey, A.C., Ruiz, E. and Shephard, N. (1994). Multivariate stochastic variance models. Review of Economic Studies, 61, 247–264.
  16. Jacquier, E., Polson, N., and Rossi, P. (2004). Bayesian analysis of stochastic volatility models with fat-tails and correlated errors. Journal of Econometrics, (122):185–212.
  17. Kass, R. E. and Raftery, A. E. (1995). Bayes Factor. Journal of the Americana Statistical Association, 90, 773-795.
  18. Kim, S. Shephard, N. & Chib, S. (1998). "Stochastic Volatility:
    Likelihood Inference and Comparison with ARCH Models". Review
    of Economic Studies,     65: 361-393.
  19. Lenk, P. J., & DeSarbo, W. S. (2000). Bayesian inference for finite mixtures of generalized linear models with random effects. Psychometrika, 65(1), 93-119
  20. Li, Y., & Yu, J. (2012). Bayesian hypothesis testing in latent variable models. Journal of Econometrics, 166(2), 237-246.
  21. Malkiel, B. G. (1979). The capital formation problem in the United States. The Journal of Finance, 34(2), 291-306.
  22. Melino, A., & Turnbull, S. M. (1990). Pricing foreign currency options with stochastic volatility. Journal of econometrics, 45(1-2), 239-265.
  23. Merton, Robert C., (1980), On estimating the expected return on the market, Journal of Financial Economics, 8, 323-361.
  24. Molaei, S., Vaez Barzani, M., & Samadi, S. (2016). An Empirical Analysis of Price Jump and Asymmetric Information in Tehran Stock Exchange. Financial Management Strategy, 4(2), 65-81. (in Persian)
  25. Nakajima, J., & Omori, Y. (2009). Leverage, heavy-tails and correlated jumps in stochastic volatility models. Computational Statistics & Data Analysis, 53(6), 2335-2353.
  26. Ozdemir, Z. A., Gokmenoglu, K., & Ekinci, C. (2013). Persistence in crude oil spot and futures prices. Energy, 59, 29-37.
  27. Pagan, A., & Ullah, A. (1988). The econometric analysis of models with risk terms. Journal of applied Econometrics, 3(2), 87-105.
  28. Park, J.Y. and Phillips, P.C.B. (2001), nonlinear regressions with integrated time series. Econometrica, 69, 117-161.
  29. Perron, P. and Ng, S. (1996). Useful Modifications to some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties. The Review of Economic Studies, 63, 435-463.
  30. Pindyck, R. S. (1984), 'Risk, inflation, and the stock market', Amnerican Economic Review, 74, 335-351.
  31. Poterba, J. and L. Summers (1986), 'The persistence of volatility and stock market fluctuations', American Economic Review, 76, 1142-1151.
  32. Razmi, S. F., Behname, M., Bajgiran, B. R., & Razmi, S. M. J. (2020). The impact of US monetary policy uncertainties on oil and gas return volatility in the futures and spot markets. Journal of Petroleum Science and Engineering, 191, 107232.
  33. Rostami, M., & Makiyan, S. N. (2020). Modeling Stock Return Volatility Using Symmetric and Asymmetric Nonlinear State Space Models: Case of Tehran Stock Market. Journal of Economic Modeling Research, 11(41), 197-229.(in Persian)
  34. Sadorsky, P. (2005). Stochastic volatility forecasting and risk management, Applied Financial Economics, 15, 121–135.
  35. Schwert, G.W. (1989). Tests for Unit Roots: A Monte Carlo Investigation, Journal of Business and Economic Statistics, 7(2), 147-59.
  36. Shephard, N. (2005). Stochastic Volatility: Selected Readings. Oxford: Oxford University Press.
  37. So, M.P. and Li, W.K. (1999). Bayesian unit-root testing in stochastic volatility models. Journal of Business and Economic Statistics, 17(4), 491-496.
  38. Wang, Y., Wu, C., & Yang, L. (2016). Forecasting crude oil market volatility: A Markov switching multifractal volatility approach, International Journal of Forecasting, 32(1), 1-9.
  39. Wright, J. (1999). An Empirical Likelihood Ratio Test for a Unit Root. Econometric Theory, 15(2), 257-257.
  40. Yu, J. (2002). Forecasting volatility in the New Zealand stock market. Applied Financial Economics, 12(3), 193-202.
  41. Zavadska, M., Morales, L., & Coughlan, J. (2020). Brent crude oil prices volatility during major crises. Finance Research Letters, 32, 101078.
  42. Zhong, M. Darrat, A. F. & Anderson, D. C. (2003). "Do US Stock Prices Deviate from their Fundamental Values? Some New Evidence". Journal of Banking & Finance 27(4): 673-697.
  43. Farhadian, A., Rostami, M., & Nilchi, M. (2020). Compare Canonical stochastic volatility model of focal MSGJR-GARCH to measure the volatility of stock returns and calculating VaR. ـJournal of Financial Management Perspective, 10(32), 131-158. doi: 10.52547/JFMP.10.32.131
Financial Management Perspective
Volume 12, Issue 39
Summer 2022
Pages 9-31