Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach

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.

Examining the Spillover Effects from Global Stock Markets to Indian Stock Market

“…The integration among Chinese, US, and UK stock markets was also present but not as distinct as the interconnection between the Asian markets. There was a direct relationship between the Chinese market and the Japanese and Indian stock markets whether it a was positive or negative information spillover [15][16][17][18][19].…”

Forecasting the Pharmaceutical Stock Prices in China in the Context of the Coronavirus Crisis Based on ARIMA-GARCH Model

“…Under normal distribution assumption, Frimpong and Oteng-Abayie (2006) showed that the GARCH (1, 1) model performs better in predicting [4]. However, though the GARCH model can capture characteristics of financial time series and is widely used for stock volatility analysis, its hypothesis ignores the symbol of new information [15]. Furthermore, the high-dimensional volatility modeling problem cannot be overlooked since the interconnected stock markets are more than three for volatility analysis.…”

“…But many researchers showed the GARCH model is limited from solving high-dimensional (over three-dimensional) problems [6,8,14,16,19]. Mixed models such as the ARMA-GARCH/ARIMA-GARCH models have been investigated [10,15]. In sum, the optimal forecasting model is various for different focuses of the research object, such as longterm or short-term, different sizes of the companies, and different types of securities [17].…”