Identifying and ranking financial market risks in Iran

Document Type : Research Paper


Department of Economic, Faculty of Economic and Management, Shiraz Branch, Islamic Azad University, Shiraz, Iran


Fluctuations in asset prices and the resulting uncertainty are one of the most important macroeconomic variables that affect different sectors of the economy in various ways. Therefore, this study is conducted to identify and rank financial market risks in Iran. This study was performed in two parts: qualitative and quantitative. The statistical population in the qualitative section includes the University of Professors in the field of economics in Iran, where $10$ people were selected as a statistical sample by available sampling. The statistical population in the quantitative section were professors and doctoral students in the field of economics. By random sampling method, $30$ people were selected. Data collection tools are the Delphi questionnaire and pairwise comparison questionnaire. Data analysis was performed by fuzzy Delphi and AHP methods. In the results of the qualitative section, 7 components including $4$ components for bad economic uncertainty and $3$ components for good economic uncertainty were identified. In the results of the quantitative section, stock index uncertainty with a weight of $0.391$ and inflation uncertainty with a weight of $0.276$ rank first and second in bad economic uncertainty. Also, economic growth uncertainty with a weight of $0.493$ and liberalization of financial markets with a weight of $0.311$ rank first and second in good economic uncertainty, respectively.


[1] A. Ang and J. Chen, Asymmetric correlations of equity portfolios, J. Financ. Econ. 63 (2002), no. 3, 443–494.
[2] N. Antonakakis, I. Chatziantoniou, and G. Filis, Dynamic co-movements of stock market returns, implied volatility
and policy uncertainty, Econ. Lett. 120 (2013), no. 1, 87–92.
[3] S. Baker, N. Bloom, and J. Davis Steven, Measuring economic policy uncertainty, Quart. J. Econ. 4 (2016),
no. 131, 1593–1636.
[4] T. Bali and H. Zhou, Risk, uncertainty, and expected returns, J. Financ. Quant. Anal. 10 (2016), no. 51, 707–735.
[5] T.G. Bali, K.O. Demirtas, and H. Levy, Is there an intertemporal relation between downside risk and expected
returns?, J. Financ. Quant. Anal. 44 (2009), no. 4, 883–909.
[6] J. Barunik and M. Nevrla, Quantile spectral beta: A tale of tail risks, investment horizons, and asset prices, 2019.
[7] M. Bijsterbosch and P. Gu´erin, Characterizing very high uncertainty episodes, Econ. Lett. 121 (2013), no. 2,
[8] C.C. Binder, Measuring uncertainty based on rounding: New method and application to inflation expectations, J.
Monetary Econ. 90 (2017).
[9] N. Bloom, The impact of uncertainty shocks, J. Economet. 77 (2009), no. 3, 623–685.
[10] T. Bollerslev, S. Z. Li, and B. Zhao, Good volatility, bad volatility and the cross-section of stock returns, J. Financ.
Quant. Anal. 55 (2020), no. 3, 751–781.
[11] T. Bollerslev, S.Z. Li, and V. Todorov, Roughing up beta: Continuous vs. discontinuous betas, and the cross-section
of expected stock returns, J. Financ. Econ. 120 (2016), no. 3, 464–490.
[12] T. Bollerslev and V. Todorov, Tails, fears, and risk premia, J. Finance 66 (2011), no. 6, 2165–2211.
[13] O. Bondarenko and C. Bernard, Option-implied dependence and correlation risk premium, Working Paper, University of Illinois, Chicago (2020).
[14] D. Bredin and S. Fountas, Macroeconomic uncertainty and macroeconomic performance: Are tey related?, Manchester School 73 (2005), no. 1, 58–76.[15] G. Caggiano, E. Castelnuovo, and N. Groshenny, Uncertainty shocks and unemployment dynamics: an analysis
of post-wwii us recessions, vol. 0166, 2013.
[16] F. Chabi-Yo, M. Huggenberger, and F. Weigert, Multivariate crash risk, J. Financ. Econ. 145 (2019), no. 1,
[17] F. Chabi-Yo and S. Ruenzi F. Weigert, Crash sensitivity and the cross section of expected stock returns, J. Financ.
Quant. Anal. 53 (2018), no. 3, 1059–1100.
[18] C.H. Cheng and Y. Lin, Evaluating the best mail battle tank using fuzzy decision theory with linguistic criteria
evaluation, Eur. J. Oper. Res. 142 (2002), no. 147.
[19] M. Cremers, M. Halling, and D. Weinbaum, Agregate jump and volatil- ity risk in the cross-section of stock
returns, J. Finance 70 (2015), no. 2, 577–614.
[20] D. Cronin, R. Kelly, and B. Kennedy, Money growth, uncertainty and macroeconomic activity: A multivariate
garch analysis. empirica, Empirica 38 (2011), no. 2, 155–167.
[21] D. Das and S. B. Kumar, International economic policy uncertainty and stock prices revisited: Multiple and partial
wavelet approach, Econ. Lett. 164 (2018), 100–108.
[22] M. Dzielinski, Measuring economic uncertainty and its impact on the stock market, Finance Res. Lett. 9 (2012),
no. 3, 167–175.
[23] R. Elkamhi and D. Stefanova, Dynamic hedging and extreme asset co–movements, Rev. Financ. Stud. 28 (2014),
no. 3, 743–790.
[24] R. F. Engle and A. Mistry, Priced risk and asymmetric volatility in the cross-section of skewness, J. Econ. 182
(2014), 135–144.
[25] C.B. Erb, C.R. Harvey, and T.E. Viskanta, Political risk, economic risk, and financial risk, Financ. Anal. J. 52
(1996), no. 6, 29–46.
[26] A. Farago and R. Tedongap, Downside risks and the cross-section of asset returns, J. Financ. Econ. 129 (2018),
no. 1, 69–86.
[27] J. Gao, S. Zhu, N. O’Sullivan, and M. Sherman, The role of economic uncertainty in uk stock returns, J. Risk
Financ. Manag. 12 (2019), no. 1, 5.
[28] E. Guglielminetti, The effects of uncertainty shocks on the labor market: A search approach, Http://Econ.
Sciences-Po. Fr/Sites/ Default/ Files/Elisa. Pdf (2013).
[29] M. Helseth, S. Krakstad, and P. Molnar, Can policy and financial risk predict stock markets?, J. Econ. Behav.
Organ. 176 (2020), 701–719.
[30] Y. Hong, J. Tu, and G. Zhou, Asymmetries in stock returns: Statistical tests and economic evaluation, Rev.
Financ. Stud. 20 (2006), no. 5, 1547–1581.
[31] B. Kelly and H. Jiang, Tail risk and asset prices, Rev. Financ. Stud. 27 (2014), no. 10, 2841–2871.
[32] W.L. Kumo, Macroeconomic uncertainty and aggregate private investment in south africa, South Afr. J. Econ.
74 (2006), no. 2, 190–204.
[33] S. Leduc and Z. Liu, Uncertainty shocks are aggregate demand shocks, J. Monetary Econ. 82 (2016), 20–35.
[34] X.M. Li, New evidence on economic policy uncertainty and equity premium, Pacific-Basin Finance J. 46 (2017),
[35] F. Longin and B. Solnik, Extreme correlation of international equity markets, J. Finance 56 (2001), no. 2, 649–676.
[36] Z. Lu and S. Murray, Bear beta, J. Financ. Econ. 131 (2019), no. 3, 736–760.
[37] K. Jurado Ludvigson, C. Sydney, and S. Ng, Measuring uncertainty, Amer. Econ. Rev. 105 (2015), no. 3, 1177–
[38] C. Luo, L. Liu, and D. Wang, Multiscale financial risk contagion between international stock markets: Evidence
from emd-copula-covar analysis, North Amer. J. Econ. Finance 58 (2021), 101512.[39] P. Orlowski, P. Schneider, and F. Trojani, On the nature of jump risk premia, Working Paper, University of
Lugano (2019).
[40] L. P´astor and P. Veronesi, Uncertainty about government policy and stock prices, J. Finance 67 (2012), no. 4,
[41] A. J. Patton, On the out-of-sample importance of skewness and asymmetric dependence for asset allocation, J.
Financ. Econ. 2 (2004), no. 1, 130–168.
[42] G. Segal, I. Shaliastovich, and A. Yaron, Good and bad uncertainty: Macroeconomic and financial market implications, J. Financial Econ. 117 (2015), 369–397.
[43] K. Sek´sci´nska, J. Rudzinska-Wojciechowska, and D. Jaworska, Self-control and financial risk taking, J. Economic
Psychol. 85 (2021).
[44] L. Serven, Macroeconomic uncertainty and private investment in developing countries: An empirical investigation,
World Bank Policy Res. Work. Paper 2035 (1998), no. 1–34.
[45] Y. Wang, B. Zhang, X. Diao, and C. Wu, Commodity price changes and the predictability of economic policy
uncertainty, Econ. Lett. 127 (2015).
Volume 13, Issue 2
July 2022
Pages 2405-2412
  • Receive Date: 02 June 2021
  • Revise Date: 02 August 2021
  • Accept Date: 26 August 2021