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Stock Market Volatility and Return Analysis

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dc.contributor.author Bhowmik, Roni
dc.contributor.author Wang, Shouyang
dc.date.accessioned 2021-11-14T10:38:27Z
dc.date.available 2021-11-14T10:38:27Z
dc.date.issued 2020
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6366
dc.description.abstract In the field of business research method, a literature review is more relevant than ever. Even though there has been lack of integrity and inflexibility in traditional literature reviews with questions being raised about the quality and trustworthiness of these types of reviews. This research provides a literature review using a systematic database to examine and cross-reference snowballing. In this paper, previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility have also been reviewed. The stock market plays a pivotal role in today’s world economic activities, named a “barometer” and “alarm” for economic and financial activities in a country or region. In order to prevent uncertainty and risk in the stock market, it is particularly important to measure effectively the volatility of stock index returns. However, the main purpose of this review is to examine effective GARCH models recommended for performing market returns and volatilities analysis. The secondary purpose of this review study is to conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008–2019) and in 50 different papers. The study found that there has been a significant change in research work within the past 10 years and most of researchers have worked for developing stock markets. en_US
dc.language.iso en_US en_US
dc.publisher Scopus en_US
dc.subject stock returns en_US
dc.subject volatility en_US
dc.subject GARCH family model en_US
dc.subject complexity in market volatility forecasting en_US
dc.title Stock Market Volatility and Return Analysis en_US
dc.title.alternative A Systematic Literature Review en_US
dc.type Article en_US


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