Stock Market Volatility and Return Analysis: A Systematic Literature Review.

Roni Bhowmik,Shouyang Wang

doi:10.3390/e22050522

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.

Highlights

In the context of economic globalization, especially after the impact of the contemporary international financial crisis, the stock market has experienced unprecedented fluctuations.This volatility increases the uncertainty and risk of the stock market and is detrimental to the normal operation of the stock market
The study is conducted by a systematic based literature review, following suggestions from scholars [14,15]. This manuscript was led by a systematic database search, surveyed by cross-reference snowballing, as demonstrated in Figure 1, which was adapted from Geissdoerfer et al [16]
The result proves that generalized autoregressive conditional heteroskedastic (GARCH) and TGARCH estimations are found to be the most appropriate model to capture symmetric and asymmetric volatility respectively

Summary

Introduction

In the context of economic globalization, especially after the impact of the contemporary international financial crisis, the stock market has experienced unprecedented fluctuations. This volatility increases the uncertainty and risk of the stock market and is detrimental to the normal operation of the stock market. To reduce this uncertainty, it is important to measure accurately the volatility of stock index returns. The knowledge of theoretical and literature significance of volatility are needed to measure the volatility of stock index returns. Volatility is a hot issue in economic and financial research. Forecasting perfect market volatility is difficult work and despite the availability of various models and techniques, not all of them work for all stock

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Conclusion