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:    210 Downloads     6153 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.

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