Navigating median and extreme volatility in stock markets: Implications for portfolio strategies

This study explores the interdependencies among developed stock markets using the LASSO technique with quantile regression within a network analysis framework. Traditional forecasting methods often fail during volatile market conditions, necessitating innovative approaches that blend interconnectedness and factor modeling. By employing quantile regression, which examines financial assets across various distribution quantiles, this study addresses tail risk, a critical aspect of market behavior during crises. The network analysis framework provides insights into the relationships between stock markets, highlighting how variables interact within a complex system. The study assesses market behaviors at different quantile levels, considering clustering coefficients to analyze cycles, middlemen, ins, and outs. Additionally, it examines the impact of several factors on market interconnectedness, offering insights into the interplay of individual stocks with broader market conditions. Key findings demonstrate that incorporating interconnectedness factors into forecasting models enhances accuracy and informs better decision-making, leading to portfolios that can withstand extreme market conditions and provide superior risk-adjusted returns.