Main Article Content
In an efficient market, the price process must follow a random walk, and price changes must be random. The presence of short and long-range dependence in the stock price process rejects the random walk and resulting in market inefficiency. The main objective of this paper is to examine the Tehran exchange market inefficiency attributableto the presence of long-range dependence in the market.To do so, we study the time-varying long-range dependence in the Tehran Stock Exchange log-return process using financial econometrics models. We provide clear statistical evidence that the mean log-return price process of the Tehran exchange market is a non-stationary process with short rang memory. Our finding indicates that shocks in the volatility of the Tehran stock market decay more slowly than an exponential decay. The results provide strong evidence in rejecting the random walk and the market efficiency hypotheses in the Tehran stock exchange market.
Mandelbrot BB.When can price be arbitraged efficiently? a limit to the validity of the random walk and martingale models. The Review of Economics and Statistics. 1971;225–236.
Fama FE, French RK. Dividend yields and expected stock returns. Journal of Financial Economics. 1988;22:3–25.
Lo AW, MacKinlay AC. Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test. The Review of Financial Studies. 1988;1(1):41–66.
Poterba JM, Summers LH. Mean reversion in stock prices: Evidence and implications. Journal of Financial Economics. 1988; 22(1):27–59.
Willinger W, Taqqu MS, Teverovsky V. Stock market prices and long-range dependence. Finance and Stochastics. 1999;3(1):1–13.
Christodoulou-Volos C, Siokis FM. Long range dependence in stock market returns. Applied Financial Economics. 2006;16(18): 1331–1338.
Cajueiro DO, Tabak BM. Testing for long-range dependence in world stock markets. Chaos, Solitons and Fractals. 2008;37(3): 918–927.
Aye GC, Balcilar M, Gupta R, Kilimani N, Nakumuryango A, Redford S. Predicting BRICS stock returns using ARFIMA models. Applied Financial Economics. 2014;24(17):1159– 1166.
Crato N, de Lima JF. Long-range dependence in the conditional variance of stock returns. Economics Letters. 1994;45(3):281–285.
Ray BK, Tsay RS. Long-range dependence in daily stock volatilities. Journal of Business & Economic Statistics. 2000;18(2):254–262.
Cavalcante JLP. Long-range dependence in the returns and volatility of the Brazilian stock market; 2003.
Cheong CW, Pei TP. Rolling estimations of long-range dependence volatility for high frequency s&p500 index. In AIP Conference Proceedings, volume 1682, page 030004. AIP Publishing LLC; 2015.
Shirvani A. Stock returns and roughness extreme variations: A new model for monitoring 2008 market crash and 2015 flash crash. Applied Economics and Finance Journal. 2020;7(3):78–95.
Jacobsen B. Long term dependence in stock returns. Journal of Empirical Finance.1996;3(4):393–417.
Cajueiro DO, Tabak BM. Evidence of long-range dependence in Asian equity markets: The role of liquidity and market restrictions; 2004.
Mishra RK, Sehgal S, Bhanumurthy N. A search for long-range dependence and chaotic structure in Indian stock market. Review of Financial Economics. 2011; 20(2):96–104.
Ling SH, Li WK. On fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity. Journal of the American Statistical Association. 1997;92(439):1184–1194.
Engle RF. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica. 1982;50(4):987–1007.
Shirvani A, Volchenkov D. A regulated market under sanctions: On tail dependence between oil, gold, and Tehran stock exchange index. Journal of Vibration Testing and System Dynamics. 2019; 3(3):297–311.
Ng ST, Fan RY, Wong JM. An econometric model for forecasting private construction investment in HongKong. Construction Management and Economics. 2011;29(5): 519– 534.
Baillie RT. Long memory processes and fractional integration in econometrics. Journal of Econometrics. 1996;73(1):5– 59.
Mortazavi SMB, Shiri N, Javadi M, Dehnavi SD. Optimal planning and management of hybrid vehicles in smart grid. Journal of Ciancia& Natura. 2015;37:253–263.
Ghaedi A, Dehnavi SD, Fotoohabadi H. Probabilistic scheduling of smart electric grids considering plug-in hybrid electric vehicles. Journal of Intelligent & Fuzzy Systems. 2019;1:1–12.
Mostafa FB, Hossain S.Revisiting the performance of PCA versus FDA versus Simple Projection for Image Recognition; 2020.
Siami-Namini S. Volatility transmission among oil price, exchange rate and agricultural commodities prices. Applied Economics and Finance. 2019;6(4):41–61.
Fallahi A, Zarei S. Empirical validation on excess volatility puzzle. Asian Journal of Economics, Finance and Management. 2020;42–48.
Trindade AA, Shirvani A, Ma X. A socioeconomic well-being index. arXiv preprint arXiv:2001.01036; 2020.
Hamilton WA. Time series analysis. Princeton University Press, Princeton, N.J; 1994.
Granger CW, Joyeux R. An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis.1980;1(1):15–29.
Hosking J. Fractional differencing. Mathematical Reviews. 1981;464:165–176.
McLeod AI, Hipel KW. Preservation of the rescaled adjusted range: 1. a reassessment of the hurst phenomenon. Water Resources Research. 1978;14(3): 491–508.
Bollerslev T, Mikkelsen HO. Modeling and pricing long memory in stock market volatility. Journal of Econometrics. 1996; 73(1):151–184.
Terdik G. Long-memory processes: Probabilistic properties and statistical methods. Journal of Time Series Analysis. 2014;35(4):390–392.
Ghalanos A. Rugarch: Univariate GARCH models. R package version 1.4–0; 2018.
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