The past few years have witnessed renewed interest in modelling and forecasting asymmetry in financial time series using a variety of approaches. The most intriguing of these strategies is the “asymmetric” or “leverage” volatility model. This study aims to conduct a review of asymmetric GARCH models using bibliometric analysis to identify their key intellectual foundations and evolution, and offers thematic and methodological recommendations for future research to advance the domain. Bibliometric analysis was used to identify patterns in and perform descriptive analysis of articles, including citation, co-authorship, bibliographic coupling, and co-occurrence analysis. The study located 856 research papers from the Scopus database between 1992 and 2021 using key phrase and reference search methods. Publication trends, most influential authors, leading countries, and top journals are described, along with a systematic review of highly cited articles. The study summarises the development, application, and performance evaluation of asymmetric GARCH models, which will help researchers and academicians significantly contribute to this literature by addressing gaps.

In recent years, numerous models with various amounts of variance have been developed to estimate and forecast important characteristics of time series data. While there are many studies on asymmetric volatility and accuracy testing of univariate Generalized Autoregressive Conditional Heteroscedasticity models, there are no parallel studies involving multiple financial assets and different heteroscedastic models and density functions. The objective of this study is to contrast the forecasting accuracy of univariate volatility models with Normal and Student-t distributions in forecasting the volatility of stock, gold futures, crude futures, exchange rate, and bond yield over a 10-year time span from January 2010 through December 2021 in Indian market. The results of exponential, threshold and asymmetric power models show that the volatility stock (–0.12047, 0.17433, 0.74020 for Nifty, and –0.1153, 0.1676, 0.7372 for Sensex), exchange rate (–0.0567, 0.0961,0.9004), crude oil futures (-0.0411, 0.0658, 0.2130), and bond yield (–0.0193, 0.0514 and –0.0663) react asymmetrically to good and bad news. In case of gold futures, an inverse asymmetric effect (0.0537, –0.01217, –0.1898) is discovered; positive news creates higher variance in gold futures than bad news. The Exponential model captures the asymmetric volatility effect in all asset classes better than any other asymmetric models. This opens the door for many studies in Indian financial market.