Abstract
Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.
Highlights
IntroductionErefore, the volatility similarity measured by clustering analysis is applied to quantify comovements of stock markets in this study
Given the rising trend of contagion in global financial market, the G20, which was born after the 2008 financial crisis, has become the most important forum of global cooperation to address the crisis [1]. e spillover effects imply that a huge impact on a financial market will increase the returns and relevance of that market and other markets [2]
How do we measure the comovements of the stock markets? Some existing studies [3,4,5] show that the comovements can be measured by the similarity among multiple markets because volatility similarity enhances information flows across markets and lead to comovements among them. at is, we can find that whenever the price of one market drops, its connected markets will go down, and vice versa. erefore, the volatility similarity measured by clustering analysis is applied to quantify comovements of stock markets in this study
Summary
Erefore, the volatility similarity measured by clustering analysis is applied to quantify comovements of stock markets in this study Motivated by this factor, we model on multivariate financial time series as it has long been a standard for studying volatility spillover and comovements [6]. The fact cannot be ignored that there are far more than two or three interconnected financial markets at risk nowadays, which is lack of relevant research in the existing literature. To fill this gap, we intend to address high-dimensional volatility modeling problem in G20 financial markets; a new approach is necessary to deal with such situations
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