Testing for Long Memory in Stock Market Returns: Evidence from Sri Lanka: A Fractional Integration Approach

Volume 5, Issue 1, February 2020     |     PP. 1-20      |     PDF (923 K)    |     Pub. Date: June 20, 2020
DOI:    192 Downloads     5630 Views  

Author(s)

Alfred, M., Department of Business Finance, Faculty of Management, University of Peradeniya, Peradeniya, Sri Lanka
Sivarajasingham, S., Dept of Economocs & Statistics, University of Peradeniya, Peradeniya, Sri Lanka

Abstract
Long memory of stock price return has not received its due attention from researchers in Sri Lanka. This study employs fractional integration approach to explain the behavior of stock price return of All Share Price Index (ASPI) in Sri Lanka. The study covers the period from January 02, 1985 to September 28, 2018, consisting of 8803 observations. The return of the ASPI is defined as . The Autoregressive Fractionally Integrated Moving Average model(ARFIMA) is used to examine the presence of fractional integration in the return series. The time domain exact maximum likelihood is used to estimate the ARFIMA model. The Volatility of ASPI return series are proxied by absolute return, squared return and conditional variance derived from fractionally integrated GARCH (FIGARCH) model. The autocorrelation function of volatility decays hyperbolically for lags 1 through 200. The results show that return series does not have long memory, while the volatility series have long memory. The findings indicate that stock market in Sri Lanka is not efficient and, the results provide information to the investors, regulators, practitioners, derivative market participants, traders and government policy makers to incorporate some risk in their strategies. Keywords: ARFIMA, exchange rate, fractional integration, Long memory, Sri Lanka

Keywords
ARFIMA, exchange rate, fractional integration, Long memory, Sri Lanka

Cite this paper
Alfred, M., Sivarajasingham, S., Testing for Long Memory in Stock Market Returns: Evidence from Sri Lanka: A Fractional Integration Approach , SCIREA Journal of Economics. Volume 5, Issue 1, February 2020 | PP. 1-20.

References

[ 1 ] Bachelier, L., (1900). Theorie de la speculation (Gauthier-Villars, Paris)
[ 2 ] Baillie, R T, Bollerslev, T and H-O Mikkelsen (1996). “Fractionally integrated generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics, 74, 3-30.
[ 3 ] Barkoula S, J.T, Christopher F. Baum M, C.F and Travlos, N (2000). Long memory in the Greek stock market. Applied Financial Economics, 10, 177-184
[ 4 ] Bhattacharya, S.N and Bhattacharya, M (2012). Long Memory in Stock Returns: A Study of Emerging Markets, Indian Journal of Management Studies, 5(2), 67-88.
[ 5 ] Cheung, Yi-Wong and Lai, S.K (1995). A search for Long memory in international stock market returns. Journal of International Monetary and Finance 14(4) 597-615.
[ 6 ] Ding,Z , Granger, C.W.J and Engle, R.F (1993) A long memory property of stock market returns and a new model. Journal of Empirical finance. 1(1), 83-106.
[ 7 ] Engle,R.F and Bollerslev, T. (1986). Modeling the persistence of conditional variance. Journal of Econometric Reviews, 5(1) 1-50.
[ 8 ] Fama, E.F. (1965) The Behavior of Stock-Market Prices. The Journal of Business 38(1), 34-105.
[ 9 ] Geweke, J. and Porter‐Hudak, S. (1983). The estimation and application of long memory time series models. Journal of time series analysis, 4(4), 221-238.
[ 10 ] Granger, C. W. and Joyeux, R. (1980). An introduction to long‐memory time series models and fractional differencing. Journal of time series analysis, 1(1), 15-29.
[ 11 ] Greene, M.T. and Fielitz, B.D. (1977). Long Term Dependence in Common Stock Returns. Journal of Financial Economics, 4, 339-349.
[ 12 ] Hiemstra,C and Jones, J.D., (1997). Another look at long memory in common stock returns. Journal of Empirical Finance, 4(4), 373-401.
[ 13 ] Hosking, J. R. (1981). Fractional differencing. Biometrika, 68(1), 165-176. http://doi: 10.1093/biomet/68.1.165.
[ 14 ] Lee, D.K.C and Robinson, P.M., (1996). Semiparametric exploration of long memory in stock prices. Journal of Statistical Planning and inference, 50(2), 155-174
[ 15 ] Lo, A.W., (1991). Long –Term memory in Stock market prices, Econometrica, 59, 1279-1313.
[ 16 ] Lux, T. (1995), ‘Herd behaviour, bubbles and crashes’, Economic Journal 105, 881–896.
[ 17 ] Mandelbrot, B (1971). When can price be arbitrages efficiently ? A limit to the validity of the random walk and martingale models. The Review of Economics and Statistics, 53(3), 225-236.
[ 18 ] Osborne, M.F.M., (1959)., Brownian motion in the Stock market. Operations research, 7(2), 145-173
[ 19 ] Peters, E. (1991). Chaos and order in the capital markets: A new view of cycles, prices, and market volatility. New York: John Wiley & Sons.
[ 20 ] Robinson, P. M. (1995). “Gaussian Semi parametric estimation of Long Range Dependence”, the Annals of Statistics, 23(5), 1630-1661.
[ 21 ] Sharad Nath Bhattacharya and Mousumi Bhattacharya (2012) Long memory in Stock Returns: A Study of Emerging Markets. Indian Journal of Management Studies, 5(2)67-88.
[ 22 ] Sadique, S and Sivapulle, P (2001). Long-Term memory in stock market returns: International evidence, International Journal of Finance and Economics, 6(1):50-67.
[ 23 ] Vaga T. (1990), The coherent market hypothesis. Financial Analysts Journal, November–December: 36–49.