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Dynamic Relationship between Macroeconomic Variables and Stock Return Volatility in Tehran Stock Exchange: Multivariate MS ARMA GARCH Approach

Author

Listed:
  • Heidari , Hassan

    (Professor of Economics, Urmia University)

  • Refah-Kahriz, Arash

    (M.A. in Economics, Urmia University, Urmia)

  • Hashemi Berenjabadi, Nayyer

    (M.A. in Economics, Urmia University)

Abstract

Investigation of changes in the stock price in Tehran Stock Exchange (TSE) has been always one of the most important challenges in the TSE. The importance of this issue is due to its applications in forecasting the stock price volatility in the stock exchange. Hence, this study investigates the impact of the most important macroeconomic variables which affects the stock return volatility in Tehran Stock Exchange in different regimes during the period 1998:1- 2015:4 by applying non-linear Multivariate MS-ARMA-GARCH approach. The results show that the rate of GDP growth has a significant negative impact on the stock return volatility. The inflation rate, money growth rate and exchange rate volatility have a significant positive impact in different regimes but, oil price volatility has different effects on the stock return volatility. In addition, the results show that the stability in the low return regime (bear regime) is more than the high return regime (bull regime). Therefore, the results recommend that planners and economic authorities through the adopting and implementation of appropriate policies to increase economic growth such as optimal allocation of resources, increased competitiveness, as well as focusing on other economic capacities of the country such as knowledge economy, increasing tourism, transportation sector, information and communication technology, using private sector capacities and increasing investment and also unification of the exchange rate to reduce the exchange rate volatility and reducing the supply of money and inflation rate to reduce volatility in the stock market return

Suggested Citation

  • Heidari , Hassan & Refah-Kahriz, Arash & Hashemi Berenjabadi, Nayyer, 2018. "Dynamic Relationship between Macroeconomic Variables and Stock Return Volatility in Tehran Stock Exchange: Multivariate MS ARMA GARCH Approach," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, vol. 5(2), pages 223-250, August.
    • Handle: RePEc:ris:qjatoe:0113
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    References listed on IDEAS

    as
    1. Patricia Fraser & David Power, 1997. "Stock return volatility and information: an empirical analysis of Pacific Rim, UK and US equity markets," Applied Financial Economics, Taylor & Francis Journals, vol. 7(3), pages 241-253.
    2. Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.
    3. Massimo Guidolin & Allan Timmermann, 2005. "Economic Implications of Bull and Bear Regimes in UK Stock and Bond Returns," Economic Journal, Royal Economic Society, vol. 115(500), pages 111-143, January.
    4. repec:cup:cbooks:9781107034662 is not listed on IDEAS
    5. John M. Maheu & Thomas H. McCurdy & Yong Song, 2012. "Components of Bull and Bear Markets: Bull Corrections and Bear Rallies," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 391-403, February.
    6. Christian Francq & Michel Roussignol & Jean‐Michel Zakoian, 2001. "Conditional Heteroskedasticity Driven by Hidden Markov Chains," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(2), pages 197-220, March.
    7. William F. Sharpe, 1963. "A Simplified Model for Portfolio Analysis," Management Science, INFORMS, vol. 9(2), pages 277-293, January.
    8. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    9. Choong, Chee-Keong & Baharumshah, Ahmad Zubaidi & Yusop, Zulkornain & Habibullah, Muzafar Shah, 2010. "Private capital flows, stock market and economic growth in developed and developing countries: A comparative analysis," Japan and the World Economy, Elsevier, vol. 22(2), pages 107-117, March.
    10. Pascal St-Amour & Stephen Gordon, 2000. "A Preference Regime Model of Bull and Bear Markets," American Economic Review, American Economic Association, vol. 90(4), pages 1019-1033, September.
    11. Diaz, Elena Maria & Molero, Juan Carlos & Perez de Gracia, Fernando, 2016. "Oil price volatility and stock returns in the G7 economies," Energy Economics, Elsevier, vol. 54(C), pages 417-430.
    12. Westerhoff, Frank & Reitz, Stefan, 2005. "Commodity price dynamics and the nonlinear market impact of technical traders: empirical evidence for the US corn market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 641-648.
    13. Rezazadeh, Ali, 2016. "The Impact of Macroeconomic Variables on Tehran Stock Market Returns Volatility: GARCH-X Approach," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, vol. 3(2), pages 121-136, July.
    14. Schuknecht, Ludger & von Hagen, Jürgen & Wolswijk, Guido, 2009. "Government risk premiums in the bond market: EMU and Canada," European Journal of Political Economy, Elsevier, vol. 25(3), pages 371-384, September.
    15. Brooks,Chris, 2014. "Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9781107661455, December.
    16. Alfaro, Laura & Chanda, Areendam & Kalemli-Ozcan, Sebnem & Sayek, Selin, 2004. "FDI and economic growth: the role of local financial markets," Journal of International Economics, Elsevier, vol. 64(1), pages 89-112, October.
    17. Jan Henneke & Svetlozar Rachev & Frank Fabozzi & Metodi Nikolov, 2011. "MCMC-based estimation of Markov Switching ARMA-GARCH models," Applied Economics, Taylor & Francis Journals, vol. 43(3), pages 259-271.
    18. He, Xue-Zhong & Westerhoff, Frank H., 2005. "Commodity markets, price limiters and speculative price dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 29(9), pages 1577-1596, September.
    19. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    20. Straetmans, S.T.M. & Candelon, B. & Ahmed, J., 2012. "Predicting and capitalizing on stock market bears in the U.S," Research Memorandum 019, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    21. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
    22. Dueker, Michael J, 1997. "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 26-34, January.
    23. Ntantamis, Christos & Zhou, Jun, 2015. "Bull and bear markets in commodity prices and commodity stocks: Is there a relation?," Resources Policy, Elsevier, vol. 43(C), pages 61-81.
    24. Cai, Jun, 1994. "A Markov Model of Switching-Regime ARCH," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 309-316, July.
    25. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    26. Haas, Markus & Liu, Ji-Chun, 2015. "Theory for a Multivariate Markov--switching GARCH Model with an Application to Stock Markets," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112855, Verein für Socialpolitik / German Economic Association.
    27. Markus Haas, 2004. "A New Approach to Markov-Switching GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 2(4), pages 493-530.
    28. Gil-Alana, Luis A. & Gupta, Rangan & Olubusoye, Olusanya E. & Yaya, OlaOluwa S., 2016. "Time series analysis of persistence in crude oil price volatility across bull and bear regimes," Energy, Elsevier, vol. 109(C), pages 29-37.
    29. Szabolcs Blazsek & Anna Downarowicz, 2008. "Regime switching models of hedge fund returns," Faculty Working Papers 12/08, School of Economics and Business Administration, University of Navarra.
    30. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3351-3363.
    31. Maio, Paulo & Philip, Dennis, 2015. "Macro variables and the components of stock returns," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 287-308.
    32. Chauvet, Marcelle & Potter, Simon, 2000. "Coincident and leading indicators of the stock market," Journal of Empirical Finance, Elsevier, vol. 7(1), pages 87-111, May.
    33. Abugri, Benjamin A., 2008. "Empirical relationship between macroeconomic volatility and stock returns: Evidence from Latin American markets," International Review of Financial Analysis, Elsevier, vol. 17(2), pages 396-410.
    34. Corradi, Valentina & Distaso, Walter & Mele, Antonio, 2013. "Macroeconomic determinants of stock volatility and volatility premiums," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 203-220.
    35. Adrian R. Pagan & Kirill A. Sossounov, 2003. "A simple framework for analysing bull and bear markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 23-46.
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    More about this item

    Keywords

    Bull & Bear regimes; Macroeconomic variables; Multivariate MS ARMA GARCH approach; Nonlinear effects; Stock return volatility;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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