An early-warning risk signals framework to capture systematic risk in financial markets

Abstract

Despite extensive research on the relationship between systematic risk and expected returns, there exists limited knowledge of how early-warning risk signals could capture investors’ expectations about changes in systematic risk. Leveraging on graph theory and covariance matrices, this study proposes a novel framework to develop risk signal patterns. Our approach not only discerns high-risk periods from calmer ones but also elucidates the pivotal role of interconnections among securities as indicators of systematic risk. The findings offer actionable insights for timely portfolio management and risk management responses in periods of transitions towards higher systematic risk. Moreover, by leveraging on graph theory, regulators can take timely decisions about how much liquidity to inject into the markets during periods of uncertainty. This study contributes to the literature by establishing a novel framework on linking investors’ expectations and expected changes in systematic risk.