In:
SAGE Open, SAGE Publications, Vol. 12, No. 4 ( 2022-10), p. 215824402211271-
Abstract:
The primary objective of this paper is to empirically examine the nature and statistical significance of the news effect on conditional volatility of unpredictable components of stock returns. Daily stock return data of 12 local and multinational companies on Dhaka Stock Exchange Ltd., Bangladesh, for the period 1990 to 2011 were used in this study. The likelihood of asymmetric effects of news on conditional volatility was tested using a set of diagnostics under the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework. The results fail to reject the null hypothesis of symmetric effects, thereby suggesting that the conditional volatility of unpredictable components of stock returns is affected equally by positive and negative news. The robustness of the results was further checked by using three widely used asymmetric models, namely exponential GARCH (EGARCH), Glosten, Jagannathan & Runkle (GJR)-GARCH, and a partially non-parametric Autoregressive Conditional Heteroscedastic (PNP-ARCH) models. Yet again, the results do not provide any evidence of significant asymmetric effects in the volatility process. In addition, the descriptive results confirm the stylized facts of unpredictable return series such as non-normal distribution, time variant conditional volatility, and persistence in return volatility. Collectively these findings, perhaps, indicate the adequacy of the GARCH (1,1) model in representing the data generating process. A number of regulatory and behavioral factors that are anticipated to be accountable for the absence of asymmetric news effects are underlined. Finally, some policy implications of the results and possible extensions of the present paper are also conveyed. JEL codes: G10, G12, G14
Type of Medium:
Online Resource
ISSN:
2158-2440
,
2158-2440
DOI:
10.1177/21582440221127157
Language:
English
Publisher:
SAGE Publications
Publication Date:
2022
detail.hit.zdb_id:
2628279-3
