Investigating the Impact of Time-varying Volatility of Macroeconomic Indices on the Predictability of Optimal Stock Portfolio Return in Tehran Stock Exchange

Document Type : Original Article

Authors

1 PhD Candidate Science and Research Branch, Islamic Azad University Tehran, Iran

2 Professor Faculty Member Department of Accounting, Science and Research Branch, Islamic Azad University Tehran, Iran (Correspond Author.)

3 Assistant Professor and Faculty Member Department of Accounting, Science and Research Branch, Islamic Azad University Tehran, Iran

4 Assistant Professor and Faculty Member Department of Economy, Science and Research Branch, Islamic Azad University Tehran, Iran

Abstract

In this study, 3 models of Time-Varying Parameters (TVP), Dynamic Model Selection (DMS) and Dynamic Model Averaging (DMA) and a comparison with the Ordinary Least Squares (OLS) method in MATLAB in the time period 2003-2013 (with data on a monthly basis) are discussed. In the present study, the variables of unofficial exchange rate changes, interest rate changes and inflation in oil price forecast returns for stocks in Tehran Stock Exchange are used. The study concludes that dynamic models with time-varying parameters are more accurate in predicting returns in the Stock Exchange, in a way that the MAFE and MSFE models, DMA, DMS which have complete dynamics are more efficient than other models. As a consequence, it can be said that the variability of the coefficients of the variables in the TVP model cannot lead to higher accuracy in predicting returns in the Stock Exchange, and it is required that the dynamics of time-varying variables of the model used to predict stock returns be taken into consideration

Keywords


  1. Abbasinezhad, H., Mohammadi, S. (2014). Comparison of multivariate volatility models in estimating the relationship between exchange rate and stock index, Investment Knowledge Quarterly, No. 11, 201-222, (in Persian).

  2. Aloui, C., Jammazi, R. (2010). The effects of crude oil shocks on stock market shifts behaviour: a regime switching approach, Energy Economics, 31(5), 789-799.

  3.  Amirhoseini, Z., Ghobadi, M. (2010). Examining the power of conditional downside capital asset pricing model for predicting risk and return, Journal of Engineering and Portfolio Management, No. 5, 88-115, (in Persian).

  4. Bayati, M. (2005). The relationship between inflation and TEPIX and TEDPIX, MS Thesis, Allameh University.

  5. Chan, J. C. C., Eisenstat, E. (2015). Bayesian model comparison for time-varying parameter VARs with stochastic volatility, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, Australian National University.

  6. Daisy Li, Y., Iscan, T. (2014).  The Impact of monetary policy shocks on stock prices: Evidence from Canada and the United States, Journal of International Money and Finance, No. 29, 876-896.

  7. Dangl, T., Halling, M. (2012). Predictive regressions with time-varying coefficients, Journal of Financial Economics, 106, 157-181.

  8. Fux, S. (2014). Essays on return predictability and term structure modeling, PhD Thesis, Frederiksberg: Copenhagen Business School.

  9. Ghysels, E., Harvey, A. C., Renault, E. (2002). Stochastic volatility, Statistical Methods in Finance, C. R. Rao and G. S. Maddala, Eds., Amsterdam: North-Holland, 119-191.

  10. Golarzi, G., Chehreneghar, A. (2015). Comparison of the performances of state space approach and OLS approach in the fitness of Fama and French three factor model for predicting the return of Tehran Stock Exchange, Asset Management and Financing Quarterly Journal, No. 2, 69-78, (in Persian).

  11. Gupta, R., Hammoudeh, S., Kim, W. (2014). Forecasting China’s foreign exchange reserves using dynamic model averaging: the roles of macroeconomic fundamentals, financial stress and economic uncertainty, North American Journal of Economics and Finance, 28, 170–189.

  12. Heidari, H., Salehinezhad, Z. (2012). State-space models and their applications in the economy, Islamic Azad University, The First International Seminar of Econometrics, Methods and Applications.

  13. Jammazi, R., Aloui, C. (2009). Wavelet decomposition and regime shifts: assessing the effects of crude oil shocks on Stock market returns, Energy Policy, 38(3), 1415-1435.

  14. Johannes, M., Korteweg, A., Polson, N. (2014). Sequential learning, predictability, and optimal portfolio returns, The Journal of Finance, 69 (2), 611-644.

  15. Karamustafa, O., Kucukkale, Y. (2003). Long run relationships between stock market returns and macroeconomic performance: evidence from Turkey, Economic Working Paper Archive at Wustl, EconWPA No. 0309010. Available at: http://ideas.repec.org/p/wpa/wuwpfi /0309010.html.

  16. Khezri, M., Sahabi, B., Yavari, K., Heidari, H. (2015). The impacts of time-varying determinants of inflation: tate-space models, Economic Models, 9(30), 25-45, (in Persian).

  17. Koop, G., Korobilis, D. (2011). Forecasting inflation using dynamic model averaging, International Economic Review, 53(3), 867-886.

  18. Maghsoud, H. (2007). Predicting stock market returns based on artificial neural networks and its compassion with multivariate models, MS Thesis, Islamic Azad University, Science and Research Branch.

  19. Mehrara, M., Fallahty, Z., Heidari, N. (2013). The relationship between systematic risk and stock return in Tehran Stock Exchange (since 1387 to 1392) using the capital asset pricing model, Policy and Economic Progress, No. 1, 67-91, (in Persian).

  20. Morakabati,  M. (2014). The effects of monetary shocks on companies listed in the field of energy efficiency, MS Thesis, Islamic Aazad University, Central Tehran Branch.

  21. Nakajima, J. (2011). Time-varying parameter VAR model with stochastic volatility: an overview of methodology and empirical applications, Monetary and Economic Studies.

  22. Naser, H., Alaali, F. (2015). Can oil prices help predict US stock market returns: an evidence using a DMA approach, Empirical Economics, 49(2), 449-479.

  23. Osoulian, M. (2005). Investigating the effects of some of the macro economic variables on stock price index, MS Thesis, Tehran University.

  24. Paye, B., S., Timmermann, A. (2006). Instability of return prediction models, Journal of Empirical Finance, 13(3), 274-315.

  25. Pesaran, M. H., Timmermann, A. (1995). Predictability of stock returns: robustness and economic significance, The Journal of Finance, 50(4), 1201-1228.

  26. Poitras, M. (2004). The impact of macroeconomic announcements on stock prices: in search of state dependence, Southern Economic Journal, Vol. 70, No. 3, 549-565.

  27. Raftery, A., Karny, M., Ettler, P. (2010). Online prediction under model uncertainty via dynamic model averaging: application to a cold rolling mill, Technimetrics, 52-66.

  28. Rahnamay Roodposhti, F., Kalantari Dehghi, M. (2014). Multi-fractal models in financial sciences, roots, properties and applications, Investment Knowledge Quarterly for Analyzing Securities, No. 24, (in Persian).

  29. Seyyedhoseini, M., Babakhani, M., Ebrahimi, B. (2012). An introduction to volatility transmission models in stock market, Bours Publication, 1st edition, (in Persian).

  30. Shephard, N. (2005). Stochastic volatility: selected readings, Oxford University Press, and M. Pitt, Likelihood Analysis of Non-Gaussian Measurement Time Series, Biometrika, 84 (3), 1997, 653–667.

  31. Shojaei, A. (2013). Forecasting inflation in Iranian economy using dynamic models, PhD Thesis, Islamic Azad University, Science and Research Branch.

  32. Stock, J. H., Watson, M. W. (2008). Phillips curve inflation forecasts, Working Paper, National Bureau of Economic Research.

  33. Taghavi, M., Janani, M. (2000). Tehran Stock Exchange and macro economic variables, Bours Magazine, No. 24, 16, (in Persian).

  34. Torabi, T., Taghi, H. (2010). The effects of macro economic variables on return indices of Tehran Stock Exchange, Economic Models, 4(1), (in Persian).

  35. Wang, Y., Ma, F., Wei, Y., Wu, C. (2016). Forecasting realized volatility in a changing world: a dynamic model averaging approach, Journal of Banking and Finance, Vol. 64, 136-149.

  36. Zolfaghari, M. (2010). Investigating the dynamic changes Tehran Stock Exchange efficiency, MS Thesis, Buali Sina University