Empirical Study on the Existence of Long-term Memory in TSE Returns

Document Type : applied

Authors

1 Allameh Tabataba'i University

2 allameh tabatabaee

Abstract

Over the past few decades, long memory processes were assigned an essential part of the time series analysis. This feature changes the statistical behavior of estimations and predictions drastically. Consequently, many theoretical results and methodologies used in time series with short memory such as ARMA processes are not suitable for long memory models. Therefore, time series memory of Tehran Stock Exchange returns are estimated and interpreted in this paper. To do this, R/S, MRS, and GPH tests are used to estimate the fractional difference parameter. Test results show the existence of long memory in stock exchange returns series; therefore, long memory models should be used to estimate and forecast. Also the weak form of market efficiency hypothesis can be disaffirmed by using the results.
JEL: C16، G1، G14
How to cite this paper: Raoofi, A., & Mohammadi, T. (2018). Empirical Study on the Existence of Long-term Memory in TSE Returns. Quarterly Journal of Risk Modeling and Financial Engineering, 2(3), 397–424. (In Persian)

Keywords

Main Subjects

References

Aydogan, K., & Booth, G. G. (1988). Are There Long Cycles in Common Stock Returns? Southern Economic Journal, 55(1), 141-149.
Baillie R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 74(1), 3-30.
Banerjee, A., & Giovanni, U. (2005). Modelling Structural Breaks, Long Memory and Stock Market Volatility: an Overview. Journal of Econometrics, 129(1-2), 1-34.
Barkoulas, J. T., Baum, C. F., & Travlos, N. (2000). Long Memory in the Greek Stock Market. Applied Financial Economics, 10(2), 177-184.
Bayani, O. (2012). Analyze the Impact of Macroeconomic Variables on the Volatility of the Stock Price Index of Tehran Stock Exchange: Application of FIEGARCH Model. Master's Thesis, Faculty of Economics, University of Isfahan, Iran. (In Persian)
Berg, L. (1998). Short and Long-run Dependence in Swedish Stock Returns. Applied Financial Economics, 7(4), 435-443.
Davies, R. B., & Harte, D. S. (1987). Test for Hurst effect. Biometrika, 74(1), 95-101.
Dejong, D. N., Nankervis, J. C., & Savin, N. E. (1992). Integration Versus Trend Stationarity in Time Series. Econometrica, 60(2), 423-433.
Diebolt, C., & Guiraud, V. (2005). A Note on Long Memory Time Series. Quality and Quantity, 39(6), 827-836.
Elder, J., & Serletis, A. (2008). Long Memory in Energy Futures Prices. Review Of Financial Economics, 17(2), 146-155.
Erfani, A. (2009). Forecasting of Tehran Securities Price Index Using ARFIMA Model. Journal of Economic Research, 44(1), 163-180. (In Persian)
Fei-xue, H. (2010). Long-term Memory for USD/CNY Based on GPH. Studies in Sociology of Science, 1(2), 23-29.
Geweke, J. & Porter-Hudak, S. (1983). The Estimation and Application of Long Memory Time Series Models. Journal of Time Series Analysis, 4(1), 221-38.
Granger, C. W. J., & Joyeux, R. (1980). An Introduction to Long Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29.
Granger, W. J. C., & Ding, Z. (1996). Varieties of Long Memory Models. Journal of Econometrics. North Holland. Elsevier 73(1), 61-77.
Grau-Carles, P. (2000). Empirical Evidence of Long-Range Correlations in Stock Returns. Physica A: Statistical Mechanics and its Applications, 287(3-4), 396-404.
Green, W. H. (2003). Econometric Analysis. Fifth Edition, New Jersey: Prentice Hall.
Harris, R., & Sollis, R. (2003). Applied Time Series Modeling and Forecasting. Wiley, West Sussex.
Harvey, A. C. (1993). Long Memory in Stochastic Volatility. Working Paper, London School of Economics.
Hosking, J. R. M. (1981). Fractional Differencing. Biometrika, 68(1), 165–176.
Hurst, H. (1951). Long-term Capacity of Reservoirs. Trans Amer Soc Civ Eng., Engng, 116(1), 770–808.
Jin, H. J., & Frechette, D. L. (2004). A New T-test for the R/S Analysis and Long Memory in Agricultural Commodity Prices. Applied Economics Letters, 11(11), 661-667.
Khalozadeh, h., & Khaki, a. (2003). Evaluating Forecasting Methods for Stock Price and Introduce Nonlinear Model using Neural Networks. Journal of Economic Research, 7(3), 61-78. (In Persian)
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the Null Hypothesis of Stationarity. Journal of Econometrics, 54(1-3), 159-178.
Lo, A. (1991). Long Term Memory in Stock Market Prices. Econometrica, 59(5), 1279-1313.
Lux, T., & Kaizoji, T. (2007). Forecasting Volatility and Volume in The Tokyo Stock Market: Long memory, Fractality and Regime Switching. Journal of Economic Dynamics & Control, 31(6), 1808-1843.
Mahmoudi, V., Mohammadi, S., & Chitsazan, H. (2010). A Study of Long Memory Trend for International Oil Markets. Journal of Economic Modeling Research, 1(1), 29-48. (In Persian)
Man, K. S. (2003). Long Memory Time Series and Short Term Forecasts. International Journal of Forecasting, 19(3), 477-491.
Man, K. S., & Tiao, G. C. (2006). Aggregation Effect and Forecasting Temporal Aggregates of Long Memory Processes. International Journal of Forecasting, 22(2), 267-281.
Mandelbrot B. B., & Wallis J. R. (1969). Computer Experiments with Fractional Gaussian Noises. Parts 1, 2, 3. Water Resources Research, 5(1), 967-988.
Mandelbrot, B. B., (1972). Statistical Methodology for Non-Periodic Cycles: from the Covariance to R/S Analysis. Annals of Economic and Social Measurement, 1(3), 259-290.
Mandelbrot, B. B., (1975). Limit Theorems on the Self-Normalized Range for Weakly and Strongly Dependent Processes. Z. Wahrscheinlichke its theorie verw. Gebiete, 31(4), 271-285.
Mandelbrot, B., & van Ness, J. W. (1968). Fractional Brownian Motions, Fractional Noises and Applications. SIAM Review, 10(4), 422-437.
McLeod, A. I., & Hippel, A. W. (1978). Preservation of the Rescaled Adjusted Range: A Reassessment of the Hurst Phenomenon. Water Resources Research, 14(1), 491‏-508.
Mohammadi, S., & Chitsazan, H. (2012). Analysing Long Run Memory in Tehran Stock Exchange. Journal of Economic Research, 46(4), 207-226. (In Persian)
Odaki, M. (1993). On the Invertibility of Fractionally Differenced ARIMA Processes. Biometrika. 80(3), 703-709.
Olan, T. H. (2002). Long Memory in Stock Returns: Some International Evidence. Applied Financial Economics, 12(10), 725-729.
Peters. E. E. (1991). Fractal Market Analysis. New York: Wiley.
Philip, S., (2004). Long Memory Versus Structural Breaks. Statistical Papers, 45(4), 465-515.
Raoofi, A. (2014). Data Generation Process for the Tehran stock exchange, Modelling and Forecasting using Soft Computing. Master's Thesis, Faculty of Economics and Social Sciences, University of Ahvaz, Iran. (In Persian)
Robinson, P., (1995). Log-Periodogram Regression of Time Series with Long-Range Dependence. Annals of Statistics, 23(3), 1048-1072.
Salarzehi, H., Kashi, M., Hosseyni, M., & Doniaee, M. (2013). Comparison of Efficiency of ARIMA and ARFIMA Models for Modeling and Prediction of Tehran Price Index. Journal of Investment knowledge, 1(2), 63-80. (In Persian)
Tabak, B. M., & Cajueiro, D. O. (2007). Are the Crude Oil Markets Becoming Weakly Efficient Over Time? A Test for Time-varying Long-range Dependence in Prices and Volatility. Energy Economics, 29(1), 28-36.
Tan, S. H., Cheong, L. L., & Yeap, P. F. (2010). Long Memory Properties in Stock Prices. Evidence from the Malaysian Stock Market. European Journal of Finance and Administrative Sciences Issue, 18(1), 77-84.
Theiler, J., Eubank, S., Longtin, A., Galdrikian. B., & Doyne Farmer, J. (1992). Testing for Nonlinearity in Time Series: The method of Surrogate Data. Physica D: Nonlinear Phenomena, 58(1–4), 77-94.
Tolvi, J. (2003). Long Memory and Outliers in Stock Market Returns. Applied Financial Economics, 13(7), 495-502.
Wright, J. H., (1999). Long Memory in Emerging Market Stock Returns. FRB International Finance Discussion Paper, No. 650.
Xiu, J., & Jin, Y., (2006). Empirical Study of ARFIMA Model Based on Fractional Differencing. Physica A‏: Statistical Mechanics and its Application, 377(1), 138-154.
Zarra-Nezhad, M., Raoofi, A., & Akbarzdeh, M. H. (2016). The Existence of Long Memory Property in OPEC Oil Prices. Asian Journal of Economic Modelling, Asian Economic and Social Society, 4(3), 142-152.
Journal of Risk modeling and Financial Engineering
Volume 2, Issue 3 - Serial Number 5
September 2017
Pages 398-425

Files

Share

How to cite

Statistics