A Fuzzy Random Walk Technique to Forecasting Volatility of Iran Stock Exchange Index

Document Type: Research Paper


1 Department of Industrial and Mechanical Engineering , Qazvin Branch.,Islamic Azad University

2 Department of Industrial Engineering, Payam Noor University (PNU), P. O .Box 19395-3697 Tehran, Iran

3 Department of Accounting, Islamic Azad Univery



Study of volatility has been considered by the academics and decision makers dur-ing two last decades. First since the volatility has been a risk criterion it has been used by many decision makers and activists in capital market. Over the years it has been of more importance because of the effect of volatility on economy and capital markets stability for stocks, bonds, and foreign exchange markets. This research first deals with the evaluation of 8 various models to forecasting volatility of stock index using daily data of Tehran stock exchange. The used models include simple ones such as random walk as well as more complex models like Arch and Garch group. Forecasting volatility index method is developed in this paper. This method is based a random walk using a fuzzy logic approach. This method is used to fore-casting volatility of Iran stock exchange index. The proposed method is assessed by comparing other methods such as Moving Average, Random walk… Results show that our proposed method is compatible with existent methods.


Main Subjects

[1] Ehteshami, S., Hamidian, M., Hajiha,Z., Shokrollahi, S., Forecasting Stock Trend by Data Mining Algrithm, Advances in Mathematical Finance and Applications, 2018, 3(1), P.97-105. Doi:10.22034/AMFA.2018.539138

[2] Mohammadipour, R., Alavimoghadam, Z., Fatemi, A., Comparison of Selected Performance of Portfolio Investment Companies by Using of Grey Forecasting and Johnson’s Index in Tehran Stock Exchange Market,Advances in Mathematical Finance & Applications,2016, 1(2), P.15-28. Doi:10.22034/AMFA.2016.527813

[3]  Pagan, A.R., Schwert, G.W., Alternative models for conditional stock volatility, Journal of Econometrics, 1990, 45(1-2), P.267-290. Doi.10.1016/0304-4076(90)90101-X

[4] Kenneth, D.W., Dongchul, C., The predictive ability of several models of exchange rate  volatility, Journal of Econometrics, 1995, 69(2),  P.367-391.  Doi:10.1016/0304-4076(94)01654-I

[5] Franses, P.H., and Ghijsels, H., Additive outliers, GARCH and forecasting volatility, International Journal of forecasting,1999, 15(1), P.1-9. Doi:10.1016/s0169-2070(98)00053-3 

[6] Malmsten, H., Evaluating exponential GARCH models, SSE/EFI Working Paper Series in Economics and finance, 2004, No 564.

[7] Wu, J., Threshold GARCH Model: Theory and Application, Paper presented at The University of Western Ontario, 2010.

[8] Kuen, T.U., and Hoong, T.S., Forecasting volatility in the Singapore stock market, Asia Pacific Journal of Management, 1992, 9(1), P.1-13. Doi:10.1007/bf01732034

[9] Poon, S.H., and Granger, C.W.J., Forecasting Volatility in Financial Markets: A Review, Journal of Economic Literature, 2003, XLI, P.478–539. Doi: 10.1257/jel.41.2.478 

[10] Franses, p.h., Dijk, D.V., forecasting stock market volatility using (non-linear) Grach models,1996, 15(3), P. 229-235. Doi:10.1002/(sici)1099-131x(199604)15:33.3.co;2-v 

[11] Kamruzzaman, M., Macroeconomic risk factors of Australian mining companies, M.A., thesis, University of Notre Dame, Australia,2018. https://researchonline.nd.edu.au/theses/191.

[12] Yu, J., Forecasting volatility in the New Zealand stock market, Applied Financial Economics, 2002, 12, P.193-202. Doi:10.1080/09603100110090118 

[13] Azar, A., Hamidian, M., Saberi, M., and Norozi, M., Evaluating the Performance of Forecasting Models for Portfolio Allocation Purposes with Generalized GRACH Method, Advances in Mathematical Finance and Applications,2017, 2(1), P.1-7. doi:10.22034/AMFA.2017.529057

[14] Sugeno, M., and Yasukawa, T., A fuzzy-logic-based approach to Qualitative modeling, IEEE transactions on fuzzy systems,1993, 1(1), P. 1-25. Doi:10.1109/tfuzz.1993.390281 

[15] Zarandi, M.H.F., Turksen, I.B., and Rezaee, B., A systematic approach to fuzzy modeling for rule generation from numerical data , Paper presented at IEEE Annual Meeting of the Fuzzy Information, 2004.

[16] Nwobi-Okoye, C. C., Okiy, S., Performance assessment of multi-input-single-output (MISO) production process using transfer function and fuzzy logic: A case study of soap production, Cogent Engineering,2016,3(1). Doi:10.1080/23311916.2016.1257082 

[17]Mamdani, E.H., Application of fuzzy logic to approximate reasoning using linguistic synthesis, Paper presented at : IEEE Transactions on Computers,1977, P.1182-1191. Doi: 10.1109/TC.1977.1674779