Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1684
Title: TESTING FOR LONG MEMORY IN THE LKR/USD EXCHANGE RATE: EVIDENCE FROM SRI LANKA
Authors: Sivarajasingham, S.
Balamurali, N.
Keywords: ARFIMA;exchange rate;GPH;Local Whittle Estimator;Long Memory;Hurst Exponent
Issue Date: 2017
Publisher: University of Jaffna
Citation: Sivarajasingham, S., & Balamurali, N. (2017). Testing for long memory in the LKR/USD exchange rate: Evidence from Sri Lanka. Journal of Business Studies, 4(1).
Abstract: The question of whether exchange rate markets are efficient or not, is directly related to whether or not long memory is pr esent in the exchange rate changes. Therefore, this paper explores the nature of the data generating processes of foreign exchange rate LKR against the US Dollar (USD), (LKR/USD) by examining the long memory properties of the LKR/USD return series based on econophysics models. In this study, autocorrelation function and spectral density function are used as visual test to inspect long memory of exchange rate returns. Further, parametric-ARFIMA model, Semi-parametric test proposed by Geweke and Porter-Hudak, Local Whittle estimator and non-parametric (R/S) test are employed as inferential tests to examine the long memory properties of the LKR/USD using daily data for the period from 2005-01-03 to 2016-12-30. Kernel density of LKR/USD return series show peak and fat tail postures. Visual inspection and inferential results reveal strong evidence of long memory property in the daily LKR/USD exchange rate return. It indicates that pricing by the market participants is not efficient. The results of this study have policy implications for traders and investors in designing and implementing trading strategies. It can also be helpful in predicting expected future return. Thus, the results of this study should be useful to regulators, practitioners and investors.
URI: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1684
ISSN: 2362-0269
Appears in Collections:JSB 2017

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