International Journal of Financial Management

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Long Memory of NSE Indices


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1 Associate Professors, Indian Institute of Forest Management (IIFM), Nehru Nagar, Bhopal
     

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Long range memory in share indices show temporal dependence between observations spaced by long intervals of time and has distinct non-periodic cycles. This paper examines the presence of long memory of various indices of National Stock Exchange (NSE). The data consists of closing values of indices over different periods of time. The tests applied to examine long memory are Hurst exponent, Manderbolt-Hurst exponent, Lo's rescaled-range analysis and Geweke and Porter-Hudak (GPH) test. The results of the estimated Hurst exponent, Manderbolt-Hurst exponent and GPH test show that invariably all NSE indices series have long memory. However, the results of Lo's rescaled-range analysis indicate the absence of long memory for all indices.

Keywords

Long Memory, Rescaled Range Analysis, Fractional Dimension, Hurst Exponent, GPH test, NSE Indices
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  • Anis, A. A. and Lloyd, E. H. (1976). The expected value of the adjusted rescaled hurst range of independent normal summands, Biometrika, 63, 111-116.

  • Barkoulas, J. T., Baum, C. F. and Travlos, N. (2000). Long memory in the Greek stock market, Applied Financial Economics, 10, 177-184.

  • Bollerslev, T. and Wright, J. H. (2000). Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data, Journal of Econometrics, 98, 81-106.

  • Cajueiro, D. O. and Benjamin, T. M. (2005). Possible causes of long range dependence in the Brazilian stock market, Physica A : Statistical Mechanics and its Applications, 345, pp. 15-26.

  • Cheung, Y W and Lai, K. S. (1995). A search for long memory in international stock market returns, Journal of International Money and Finance, 14(4), 597-615.

  • Ding, Z., Clive, W. J. and Robert, F. E. (2001). A long memory property of stock market returns and a new model, Journal of Empirical Finance, 1(1), 83-106.

  • Fama, E.F (1970). Efficient Capital Markets: A review of theory and empirical work, Journal of Finance, 25, 383-417.

  • Gang, M. (2003). The anatomy of daily stock turnover’, www.papers.ssrn.com/ sol3/papers. cfm?abstractid=546722

  • Geweke, J. and Porter-Hudak, S. (1983). The estimation and application of long-memory timeseries Models, Journal of Time Series Analysis, 4, 221–237.

  • Gil-Alana, L.A. (2006). Fractional integration in daily stock market indexes, Review of Financial Economics, 15, 28–48.

  • Goalknath, C. (2001). Long memory and Indian stock market – An empirical evidence, Paper presented at UTIICM Conference held at Mumbai.

  • Goalknath, C. and Reddy Y.V. (2002). Long memory in rupee-dollar exchange rate – An empirical study. Paper presented at UTIICM Conference held at Mumbai.

  • Hassler U., Marmol, F., and Velasco, C.. (2006) Residual log-periodogram inference for long-run relationships, Journal of Econometrics, 13(1), 165-207.

  • Henry, O T (2002). Long memory in stock returns: some international evidence, Applied Financial Economics, 12(10), 725-729.

  • Ho, D., Lee, C. and Wang C. (2004). Scaling characteristics in the Taiwan stock market, Physica A 25, 448-460.

  • Howe, J. S, Martin, D. W. and Wood, B. G. (1999). Much ado about nothing: long-term memory in Pacific rim equity markets, International Review of Financial Analysis, 8(2), 139-151.

  • Jiang, J., Ma, K., Cai, X. (2007). Non-linear characteristics and long-range correlations in Asian stock markets, Physica A, 378, 399- 407

  • Klemes, V. (1974). The Hurst Phenomenon: A Puzzle?, Water Resources Research, 10(4), 675-687.

  • Kwiatkowski, D., Phillips, P. C. B., Schmidt P. C. B. and Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit ischolar_main: How sure are we that economic time series have a unit ischolar_main? Journal of Econometrics. 54, 159-178.

  • Kumar, Alok (2006). Long memory in stock trading volume: Evidence from Indian stock market, Working Paper, Indira Gandhi Institute of Development Research, Mumbai.

  • Lillo, F., Farmer, J. D. (2004). The long memory of the efficient market, arXiv: cond-mat/0311053, 2, 178-185.

  • Lo, A. W. (1991). Long-term memory in stock market prices, Econometrica. 59, 1279–1313.

  • Lobato, I. N. and Velasco, C. (2000). Long memory in stock-trading volume, Journal of Business and Economics Statistics, 18(4), 410-427.

  • Lu, S. Q., Ito, T and Voges, K. (2008). An analysis of long memory in the SSE’s component index, Journal of Economics, Banking and Finance, 2 (1), 337-346.

  • Mandelbrot, B. 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, 225-236.

  • Robinson, P. M. (1995). Log-periodogram regression for time-series with long range dependence, Annuals of Statistics, 23, 1048-1072.

  • Skjeltorp, J. A. (2000) Scaling in the Norwegian stock market, Physica A, 283, 486-528.

  • Soofi, A.S., Wang, S and Zhang, Y (2006). Testing for long memory in the Asian foreign exchange rates, Journal of Systems Science and Complexity, 19(2), 234-247.

  • Tolvi, J (2003). Long memory and outliers in stock market returns, Applied Financial Economics, 13, 495-502.

  • Verma, A. (2008). Long memory of the Indian stock market, The ICFAI Univesirty Journal of Financial Economics, 3 Sept., 74-83.


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  • Long Memory of NSE Indices

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Authors

C. V. R. S. Vijaya Kumar
Associate Professors, Indian Institute of Forest Management (IIFM), Nehru Nagar, Bhopal
Ashutosh Verma
Associate Professors, Indian Institute of Forest Management (IIFM), Nehru Nagar, Bhopal

Abstract


Long range memory in share indices show temporal dependence between observations spaced by long intervals of time and has distinct non-periodic cycles. This paper examines the presence of long memory of various indices of National Stock Exchange (NSE). The data consists of closing values of indices over different periods of time. The tests applied to examine long memory are Hurst exponent, Manderbolt-Hurst exponent, Lo's rescaled-range analysis and Geweke and Porter-Hudak (GPH) test. The results of the estimated Hurst exponent, Manderbolt-Hurst exponent and GPH test show that invariably all NSE indices series have long memory. However, the results of Lo's rescaled-range analysis indicate the absence of long memory for all indices.

Keywords


Long Memory, Rescaled Range Analysis, Fractional Dimension, Hurst Exponent, GPH test, NSE Indices

References