Stock Market Volatility and Return Analysis: A Systematic Literature Review

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.

Stock Return, Growth and Inflation in India: Analysis of Stochastic Seasonality, Impulse Response and Multivariate GARCH

“…We extend the empirical exercise by considering that the conditional variances of the variables in the VAR system follow an ARMA process. Bhowmik and Wang (2020) reviewed extensively the literature on the application of GARCH model in analyzing volatility in stock market return. Forecasting stock market volatility in a perfect way is indeed a difficult task and a large variety of GARCH model appears in the literature.…”

How to Promote the Performance of Parametric Volatility Forecasts in the Stock Market? A Neural Networks Approach

“…Thus, how to accurately predict the volatility of an asset is a very important issue in the actual investment process in the financial field. As to the issue of volatility forecasting, most of literatures used the generalized autoregressive conditional heteroskedasticity (GARCH) family models, a parametric volatility forecasting approach, to predict the volatility of an asset [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Because this type of model can capture most common features of financial assets such as both the linear dependence and strong autoregressive conditional heteroskedasticity (ARCH) effect subsisting on the return series, and both the volatility clustering and leverage effect usually existing at the volatility of financial asset returns series [ 8 , 20 , 21 , 22 , 23 ] (Volatility clustering means that large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes [ 23 ].…”

“…Second, the empirical model may be a model with a flexible volatility specification [ 12 , 14 ], generalized return distribution [ 15 ] or with both flexible volatility specification and generalized return distribution [ 16 , 17 , 18 ]. Moreover, among the flexible volatility specifications the GJR–GARCH model has the best performance of volatility forecasting [ 13 , 14 , 16 , 18 ]. In addition, the GJR-GARCH-SGED model in Su [ 18 ] is the most flexible model among the models with both flexible volatility specification and generalized return distribution [ 16 , 17 , 18 ].…”

Estimating Short-Term Returns with Volatilities for High Frequency Stock Trades in Emerging Economies Using Gaussian Processes (GPs)

“…This is directly related to the successful application of the traditional (LSTM) to address the problem of volatility prediction in the stock market, Xiong et al [17] and Sardelicha and Manandhar (n.d) [18]. Bhowmik and Wang [19] on the other hand provides a literature review using a systematic database to examine and cross-reference snowballing where previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility are reviewed. They also conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008-2019) and in 50 different papers to see the trends and concentration of volatility linked studies.…”