🤖 AI Summary
This study addresses the limitation of traditional risk contribution measures, which fail to distinguish whether an asset’s risk stems from its own volatility or its correlation with other assets. The authors propose a novel decomposition—based on a leave-one-out approach—that, for the first time, rigorously preserves additivity while separating total risk contribution into two economically interpretable components: intrinsic risk (capturing idiosyncratic volatility) and correlation risk (reflecting co-movement with the rest of the portfolio). Integrated with time-series analysis, this framework enables dynamic tracking of both risk components across varying market regimes. Empirical results demonstrate that the method reliably and transparently disentangles the effects of volatility shocks and correlation shifts on portfolio risk, offering practical utility for risk reporting, stress testing, performance attribution, and the identification of effective hedging instruments.
📝 Abstract
This paper develops a decomposition of standard Risk Contribution (RC) into two economically interpretable components: inherent risk and correlation risk. Using a leave-one-out representation, each position's RC separates into a term reflecting its own volatility contribution independent of the portfolio and a term capturing its covariance with the remainder of the portfolio. The inherent component is always positive, arising from the intrinsic volatility of the position, while the correlation component may amplify or mitigate total portfolio risk depending on how the position moves relative to other holdings. Because the decomposition operates within standard RC, it preserves the property of strict additivity. This separation provides diagnostic insight not visible from aggregate risk contributions alone. It distinguishes whether a position contributes risk because it is volatile in isolation or because it is highly correlated with the rest of the portfolio, and it clarifies when a negatively correlated position functions as an effective hedge. Two approaches to time-series analysis are presented to track how inherent and correlation risk evolve across market regimes, revealing whether changes in portfolio risk during stress periods are driven by volatility shocks, correlation shifts, or both. Empirical illustrations suggest that the decomposition provides stable, transparent, and easily implementable risk diagnostics that can support portfolio risk reporting, stress testing, and performance attribution.