Bhowmik Roni, Wang Shouyang
School of Economics and Management, Jiujiang University, Jiujiang 322227, China.
Department of Business Administration, Daffodil International University, Dhaka 1207, Bangladesh.
Entropy (Basel). 2020 May 4;22(5):522. doi: 10.3390/e22050522.
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.
在商业研究方法领域，文献综述比以往任何时候都更具相关性。尽管传统文献综述存在缺乏完整性和灵活性的问题，人们对这类综述的质量和可信度也提出了质疑。本研究使用系统数据库进行文献综述，以检验和交叉引用滚雪球法。本文还回顾了以往以广义自回归条件异方差（GARCH）族模型为基础的股票市场回报与波动性的研究。股票市场在当今世界经济活动中发挥着关键作用，被称为一个国家或地区经济和金融活动的“晴雨表”和“报警器”。为了防范股票市场中的不确定性和风险，有效衡量股票指数回报的波动性尤为重要。然而，本综述的主要目的是检验推荐用于进行市场回报与波动性分析的有效GARCH模型。本综述研究的次要目的是对12年（2008 - 2019年）期间50篇不同论文中的回报与波动性文献综述进行内容分析。研究发现，在过去10年里研究工作发生了重大变化，大多数研究人员致力于发展股票市场。
