The impact of unpredictable resource prices and equity volatility in advanced and emerging economies: An econometric and machine learning approach

Abstract

Global stock markets are incredibly unpredictable. Resource prices have a significant market impact on varying securities. With the use of cutting-edge technology like artificial intelligence, analysts and researchers are employing various machine learning techniques and econometrics methodologies to anticipate stock price trends in order to better comprehend stock market volatility. Volatility is the degree of variation in a time sequence of market rates. Stock market equity returns depend on the business output where the investor has trust in high and low equity. This research explores the interaction between industrialized and developing economies' market volatility relationships between 2000 and 2020 as well as the aforementioned impacts taking place on developing financial prudence worldwide. The aim of the study is to integrate an appropriate GARCH framework to estimate the uncertainty dependent on market conditions in the European Union, the Pacific, South America, Latin America, East Asia, West Asia and South Asia stock return indices. The Generalized Auto-Regressive Conditional Heteroscedasticity method is used for analyzing the effect of updates from the USA that influences the returns of S&P 500 globally as well as European Union, Pacific, South American, Latin American, East Asian, West Asian and South Asian indices returns. For capital markets of the world, there is a significant gap in equity return uncertainty. Such results have major effects on investors looking to diversify their portfolios. For international and domestic institutional shareholders, this paper is significant. The impact of international institutional investors' investments and effects of the growth of the equity market return may be omitted as the analysis is restricted exclusively to the European Union, the Pacific, South America, Latin America, East Asia, West Asia, and South Asia.