Impact of return on long-memory data set of volatility of Dhaka Stock Exchange market with the role of financial institutions: an empirical analysis

  • Released On
    Tuesday, 29 August 2017
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  • DOI
    http://dx.doi.org/10.21511/bbs.12(3).2017.04
  • Article Info
    Volume 12 2017, Issue #3, pp. 48-60
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The current study intends to empirically test a relationship between long-memory features in returns and volatility of Dhaka Stock Exchange market. As such, the study uses the ARFIMA-FIGARCH and FIPARCH structure for the daily data ranging from 15 December 2003 to July 31, 2013 of Dhaka Stock Exchange market index, i.e., DSE General Index (DGEN). The observed indication assembled from long-memory tests supports the occurrence of long memory in Bangladesh stock returns. The study aims at doing research work with long-memory data set, as it provides a superior strategy, as well as gives real picture with short-memory data set. Moreover, the backup indication for existence of long memory in both return and volatility denies the efficient market hypothesis of Fama (1970) that the future return and volatility values are unpredictable. Extra measures ought to be given for the smooth functioning of the Dhaka Stock Exchange market so that both individual and institutional investors can get congenial atmosphere to invest. Authors’ suggested that Bangladesh Bank must play vital role as share market of Bangladesh is dominated by banking shares and in case of other listed shares of the Dhaka Stock Exchange, market authority should deal with transparently and fairly so that the market can be transformed into strong efficient market. This requires suitable directives, groundwork, removing malpractices and also implementation of investors’ friendly decisions. Further, fiscal policy of the country should be pro investor friendly, as well as monetary policy should work as complementary towards investment at stock exchange market as suggested by the authors.

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    • Figure 1. Dhaka Stock Exchange performance from 1990 to 2017
    • Figure 2. Frequent volatilities with extensive amplitude at DSE
    • Figure 3. Quantile-Quantile plot for CASPI
    • Figure 4. ACF for CASPI and PACF for CASPI
    • Table 1. Descriptive statistics and stationarity analysis
    • Table 2. Estimation results of the ARFIMA models
    • Table 3. Estimated results of ARFIMA-FIGARCH and ARFIMA-FIAPARCH