Examining the Leverage Effect, Dynamic Conditional Correlation, and Volatility Spillover Among Selected Indices of the Tehran Stock Exchange: Evidence from the ARMA-DCC-GJR-GARCH Model

Document Type : Research Paper

Authors

1 Associate Prof., Department of Management and Economic, Faculty of Management and Economics, Semnan University, Semnan, Iran.

2 Ph.D. Candidate, Department of Financial Engineering, Faculty of Management and Economics, Semnan University, Semnan, Iran.

Abstract

Objective
In financial literature, there are two well-explored characteristics of volatility. The first pertains to the asymmetric reactions of volatility to positive and negative news, while the second involves the presence of volatility spillover (contagion) between markets and various financial assets. The asymmetric behavior of volatility refers to empirical evidence that a negative return shock causes a greater increase in volatility than a positive return shock of the same size. Also, concerning the asymmetric impact of news on stock volatility, two hypotheses, namely the leverage effect and volatility contagion, have been postulated. Accordingly, this study aims to explore the leverage effect, dynamic conditional correlation, and volatility spillover among ten selected indices of the Tehran Stock Exchange. These ten industry indices collectively constitute over 75 percent of the overall Tehran Stock Exchange index.
 
Methods
In this study, the ARMA (1,1) form was utilized to construct the mean model. Then, the GJR-GARCH model was used to check the leverage effects. Finally, the DCC-GARCH (1,1) framework was employed, which helped to deeply analyze the dynamic linkages in volatility among selected indices of the Tehran Stock Exchange. The daily return data of industry indices, comprising a total of 1117 observations, was utilized during the period from March 25, 2018, to November 16, 2022.
 
Results
The result of the GJR coefficient, which was positive and significant for all return series -except for the Chemical and Oil Product Indexes- Indicates leverage effects exist. Also, the result of DCC (1,1) indicates the conditional correlation between all variables is positive and volatility spillover among them was strongly confirmed.
 
Conclusion
Financial markets, particularly the stock market, exhibit varied responses to positive and negative shocks, and these shocks impact the correlation between variables. For this reason, this research aimed to investigate the time frame during which the stock market underwent substantial fluctuations. This approach allowed for a more thorough and accurate examination of leverage effects, volatility spillover, and dynamic conditional correlation between returns. In the first half of 2019, despite the drop in the prices of commodities, oil, and the COVID-19 pandemic, the stock market experienced stunning growth, while investors were excited to buy regardless of the fundamental conditions of the companies. This enthusiasm to buy spread among stock market industries, but from the second half of 2019, the situation was completely reversed, and the market sold their shares at the slightest negative news. Hence, based on the results revealing the presence of leverage effect, dynamic conditional correlation, and volatility contagion, investors and portfolio managers can use these findings to mitigate risks and optimize their portfolios.
 

Keywords

Main Subjects

References

 
Abdullah, A. M., Saiti, B. & Masih, M. (2016). The impact of crude oil price on Islamic stock indices of South East Asian countries: Evidence from MGARCH-DCC and wavelet approaches. Borsa Istanbul Review, 16(4), 219-232.
Abounoori, E., Abdollahi, M. (2013). Modeling Different Sector Volatility of Iran Stock Exchange Using Multivariate GARCH Model. Financial Research Journal, 14(1), 1-16. (in Persian)
Abounoori, E., Ziauddin, H. (2020). Return and Volatility of International Oil Price and Stock Index in OPEC Member Countries. Quarterly Journal of Economical Modeling, 14(1), 1-24. (in Persian)
Aielli, G. P. (2013). Dynamic conditional correlation: on properties and estimation. Journal of Business & Economic Statistics, 31(3), 282-299.
Allen, D., Amram, R., & McAleer, M. (2013). Volatility spillovers from the Chinese stock market to economic neighbors.  Mathematics and Computers in Simulation, 94, 238-257.
Armstrong, J. (2018). The Markowitz Category. SIAM Journal on Financial Mathematics, 9(3), 994-1016.
Baillie, R. T., Bollerslev, T. & Mikkelsen, H. O. (1996). Fractionally integrated generalized Auttoregressive conditional heteroskedasticity. Journal of econometrics, 74(1), 3-30.
Bala, D. A. & Takimoto, T. (2017). Stock market's volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach. Borsa Istanbul Review, 17(1), 25-48.
Barkhordari, F., Pour Azizi Gelin Gheshlaghi, S., Hoseini, A. (2017). The effect of exchange rate volatilities and it's spillover effect on the index of Tehran Stock Exchange. Journal of Investment knowledge, 6(21), 1-14. (in Persian)
Baruník, J., Kočenda, E. & Vácha, L. (2016). Gold, oil, and stocks: Dynamic correlations. International Review of Economics & Finance, 42, 186-201.
Bautder, D., Bodnar, T., Parolya, N., & Schmid, W. (2018). Bayesian mean–variance analysis: optimal portfolio selection under parameter uncertainty. Quantitative Finance, 1-22.
Bautwens, L. & LAutrent, S. (2005). A new class of multivariate skew densities, with application to generalized Auttoregressive conditional heteroscedasticity models. Journal of Business & Economic Statistics, 23(3), 346-354.
Bautwens, L., Hafner, C. M. & Pierret, D. (2013). Multivariate volatility modeling of electricity futures. Journal of Applied Econometrics, 28(5), 743-761.
Billio, M., Caporin, M., & Gobbo, M. (2006). Flexible dynamic conditional correlation multivariate garch models for asset allocation. Applied Financial Economics Letters, 2(02), 123- 130.
Bollerslev, T. (1986). Generalized Auttoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. The review of economics and statistics, 498-505.
Bollerslev, T., Engle, R. F. & Wooldridge, J. M. (1988). A capital asset pricing model with time-varying covariances. Journal of political Economy, 96(1), 116-131.
Bonato, M., Caporin, M. & Ranaldo, A. (2013). Risk spillovers in international equity portfolios. Journal of Empirical Finance, 24, 121-137.
Cappiello, L., Engle, R., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4, 537–572.
Dornbusch, R., Park, Y. C. & Claessens, S. (2000). Contagion: understanding how it spreads. The World Bank Research Observer, 15(2), 177-197.
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized Auttoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.
Engle, R. F. (2011). Long-term skewness and systemic risk. Journal of Financial Econometrics, 9(3), 437-468.
Engle, R.F. & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 122-150.
Engle, R. F., Ng, V. K., & Rothschild, M. (1990). Asset pricing with a factor-ARCH covariance structure: Empirical estimates for treasury bills. Journal of Econometrics, 45(1–2), 213–237.
Engle, R.F., Sheppard, K. (2001). Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH. Working Paper. University of California, San Diego.
European Commission (2014). Quarterly report on the Euro area 13(4). Brussels.
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.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
Hamao, Y., Masulis, R. W. & Ng, V. (1990). Correlations in price changes and volatility across international stock markets. The review of financial studies, 3(2), 281-307.
He, C., Silvennoinen, A. & Teräsvirta, T. (2008). Parameterizing unconditional skewness in models for financial time series. Journal of Financial Econometrics, 6(2), 208-230.
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.
Jarque, C. M. & Bera, A.K. (1987). A Test for Normality of Observations and Regression Residuals. International Statistical Review / Revue Internationale de Statistique, 55(2), 163–172. 
Jiang, Y., Yuyuan, F. & Weihuan, R. (2019). Risk Spillover and Portfolio management between precious metal and BRICS stock markets. Physica A, 534.
Kodres, L. E. & Pritsker, M. (2002). A rational expectations model of financial contagion. The journal of finance, 57(2), 769-799.
Koutmos, G. & Booth, G. G. (1995). Asymmetric volatility transmission in international stock markets. Journal of international Money and Finance, 14(6), 747-762.
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.
Lautrent, S., Boudt, K., & Danielsson, J. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244-257.
Ling, S., & McAleer, M. (2003). Asymptotic theory for a vector ARMA-GARCH model. Econometric theory, 19(2), 280-310.
Malik, F. & Ewing, B. T. (2009). Volatility transmission between oil prices and equity sector returns. International Review of Financial Analysis, 3(18), 95-100.
Markowitz, H. (1952). Portfolio selection. The journal of finance, 7 (1), 77-91.
Massacci, D. (2014). A two-regime threshold model with conditional skewed Student t distributions for stock returns. Economic Modelling, 43, 9-20.
McAleer, M., Hoti, S., & Chan, F. (2009). Structure and asymptotic theory for multivariate asymmetric conditional volatility. Econometric Reviews, 28(5), 422-440.
Merton, R. C. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of financial economics, 8(4), 323-361.
Mo, B., Nie, H. & Jiang, Y. (2018). Dynamic linkages among the gold market, US dollar and crude oil market, Physica A, 491, 984-994.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
Pouryaghoubi, H. & Ashrafi, Y. (2020). Spillover Effect On Different industries For Capital Market. Journal of Investment knowledge, 9(34), 277-293. (in Persian)
Saiti, B. & Noordin, N. H. (2018). Does Islamic equity investment provide diversification benefits to conventional investors? Evidence from the multivariate GARCH analysis. International Journal of Emerging Markets. 13(1), 267-289.
Saiti, B., Bacha, O. I. & Masih, M. (2016). Testing the conventional and Islamic financial market contagion: evidence from wavelet analysis. Emerging Markets Finance and Trade, 52(8), 1832-1849
Tse, Y. K. & Tsui, A. K. C. (2002). A multivariate generalized Auttoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business & Economic Statistics, 20(3), 351-362.
Valls Ruiz, N. (2014). Volatility in financial markets: The impact of the global financial crisis. Ph.D. Thesis.
Wang, Y. & Liu, L. (2016). Spillover effect in Asian financial markets: A VAR-structural GARCH analysis. China Finance Review International, 6(2), 150-176.
Way, R., Lafond, F., Lillo, F., Panchenko, V. & Farmer, J. D. (2019). Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves. Journal of Economic Dynamics and Control, 101, 211-238.
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.
Zamani, S., Souri, D., Sanaei Alam, M.(2011). A Dynamic Investigation to Indexes Spillovers in Tehran Stock Exchange Using a Multivariate Dynamic Model. Journal of Economic Research, 45(4), 29-54. (in Persian)
Zhong, G. Y., Li, J. C., Jiang, G. J., Li, H. F., & Tao, H. M. (2018). The time delay restraining the herd behavior with Bayesian approach. Physica A: Statistical Mechanics and its Applications, 507, 335-346.
Financial Research Journal
Volume 26, Issue 1
2024
Pages 54-80

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