Hiremath, Gourishankar S and Bandi, Kamaiah (2011): Testing Long Memory in Stock Returns of Emerging Markets: Some Further Evidence. Published in: Economics, Management, and Financial Markets , Vol. 6, No. 3 (2011): pp. 136-147.
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Abstract
The paper examines the long memory in stock returns of emerging markets. Unlike earlier studies, present study carries out a biased reduced semi-parametric test to detect long memory in mean process and uses diverse and updated data set. The test results finds no strong evidence of long memory in mean process of stock returns both in emerging and developed markets. This is in contract with earlier studies, which conclude that emerging markets in general characterized by long memory process. Hence, long memory is not a peculiar characteristic of emerging markets but appear to be stylized fact of asset returns irrespective of stage of development of the market. Short memory models are thus sufficient to forecast the future returns.
Item Type: | MPRA Paper |
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Original Title: | Testing Long Memory in Stock Returns of Emerging Markets: Some Further Evidence |
English Title: | Testing Long Memory in Stock Returns of Emerging Markets: Some Further Evidence |
Language: | English |
Keywords: | Long memory, volatility persistence, mean-reversion, semi-parametric test, hyperbolic decay, market efficiency, Indian Stock Market, NSE, BSE. |
Subjects: | G - Financial Economics > G0 - General G - Financial Economics > G1 - General Financial Markets G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation |
Item ID: | 48517 |
Depositing User: | Gourishankar S. Hiremath |
Date Deposited: | 26 Jul 2013 05:01 |
Last Modified: | 29 Sep 2019 01:46 |
References: | Agiakloglou, C., Newbold P and Wohar, M (1993), “Bias in Estimator of the Fractional Difference Parameter” Journal of Time Series Analysis, Vol. 14, pp. 235-246. Andrews, DWK and Guggenberger P (2003), “A bias-reduced log-periodogram regression estimator for the long-memory parameter”, Econometrica, Vol. 71, pp. 675-712. Baillie, R. T (1996), “Long memory and fractional integration in econometrics”, Journal of Econometrics, Vol. 73, pp. 5-59. Baillie, R. T, Bollerslev, T and Mikkelsen H. O (1996), ‘Fractionally integrated generalized autoregressive conditional heteroskedasticity’, Journal of Econometrics vol. 74, pp. 3-30. Cheung, Y. W and Lai, K. S (1995), “A search for long memory in international stock market returns”, Journal of International Money and Finance, Vol. 14, pp. 597-615. Choudhry, T (2001), “The long memory of time-varying beta: examination of three emerging Asian stock markets”, Managerial Finance, Vol. 27, pp. 5-23. Fama, E. F (1970), “Efficient capital markets: A review of theory and empirical work”, Journal of Finance, Vol. 25, pp.383-417 Floros, C, Jaffry Y and Lima, G. V (2007), “Long memory in Portuguese stock market”, Studies in Economics and Finance, Vol. 24, pp. 220-232 Geweke, J and Porter-Hudak, S (1983), “The estimation and application of long memory time series models”, Journal of Time Series Analysis, Vol. 4, pp. 221-238. Granger, C.W.J and Joyeux, R (1980), “An introduction to long-memory time series models and fractional differencing”, Journal of Time Series Analysis, Vol. 1, pp. 15-29. Greene, M. T and Fielitz, B (1977), “Long-term dependence in common stock returns”, Journal of Financial Economics, Vol.4, pp.339-349. Hosking, J. R. M (1981), ‘Fractional differencing’. Biometrika, Vol. 68, pp. 165-176. Hurst H. E 1951, “Long-term storage capacity of reservoirs’, Transactions of the American Society of Civil Engineers’, Vol. 116, pp. 770-799. Kasman, A. and Torun, E (2007), “Long Memory in the Turkish stock market return and volatility”, Central Bank Review, Vol. 2, pp. 13-27. Lo, A. W (1991), Long term memory in stock market prices, Econometrica, Vol. 59, pp.1270-1313. Lobato, I and Savin N. E (1998), “Real and spurious long-memory properties of stock-market data”, Journal of Business and Economic Statistics, Vol. 16, pp. 261-268. Mandelbrot, B (1971), “When can price be arbitraged efficiently? A limit to the validity of the random walk and martingale models”, Review of Economics and Statistics, Vol. 53 pp. 225-236. Mandelbrot, B. B and Van Ness J. W (1968), “Fractional brownian motions, fractional brownian noise and applications”, SIAM Review, Vol. 10, pp.422-437. Mandelbrot, B. B (1972), ‘Statistical methodology for non periodic cycles: From the covariance to R/S analysis’, Annals of Economic and Social Measurement, Vol.1, pp. 259-290. McLeod, A. I & Hipel, K. W (1978), “Preservation of the rescaled adjusted range 1: A re assessment of Hurst phenomenon” Water Resources Research, Vol. 14, pp. 491-508. McMillan, D., and Thupayagale, P, “Measuring volatility persistence and long memory in the presence of structural breaks: Evidence from African stock markets”, Managerial Finance, Vol. 37, pp. 220 - 232 Crato, N., and Lima, J.F (1994), “Long-memory and Nonlinearity: A Time Series Analysis of Stock Returns and Volatilities”, Managerial Finance, Vol, 20, pp. 219 - 241 Nielsen, M. O and Frederiksen, P. H (2005), “Finite sample comparison of parametric semiparametric and Wavelet estimates of fractional integration”, Econometrics Review, vol. 24, pp. 405-443. Palma, W, 2007, ‘Long-memory time series’, New Jersey: John Wiley & Sons, Inc. Robison, P.M, (1991), “Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression”, Journal of Econometrics, Vol. 47, pp. 67-84. Sadique, S and Silvapulle, P (2001), “Long-term memory in stock market returns: International evidence”, International Journal of Finance and Economics, Vol. 6, 59-67. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/48517 |