This paper by Diebold and Yilmaz proposes a method to measure both total and directional volatility spillovers using a generalized vector autoregressive (VAR) framework. The authors address the limitations of the original Diebold-Yilmaz (DY) framework, which relies on Cholesky factor identification and is sensitive to variable ordering. Their approach uses forecast-error variance decompositions that are invariant to variable ordering, allowing for the measurement of directional spillovers. The study applies this method to analyze daily volatility spillovers across U.S. stock, bond, foreign exchange (FX), and commodity markets from January 1999 to January 2010. Key findings include: 1. **Total Spillovers**: The total volatility spillover index, which measures the contribution of spillovers of volatility shocks across asset classes to the total forecast error variance, is approximately 12.6% on average. 2. **Directional Spillovers**: The directional spillovers, which measure the volatility shocks transmitted from one market to another, show significant variations over time. During tranquil periods, these spillovers are generally below 5%, but they increase to nearly 10% during volatile times. 3. **Net Spillovers**: Net volatility spillovers, which account for both the transmission and reception of volatility shocks, are particularly notable during the recent global financial crisis. The stock market was a significant transmitter of volatility to other markets, especially after the collapse of Lehman Brothers in September 2008. The paper also examines the dynamics of spillovers using rolling-sample analysis, revealing several cycles and bursts in volatility spillovers, particularly during the tech bubble burst in 2000, the 2007-2009 financial crisis, and other significant events. The findings highlight the importance of understanding and monitoring cross-market volatility spillovers, especially in the context of financial crises.This paper by Diebold and Yilmaz proposes a method to measure both total and directional volatility spillovers using a generalized vector autoregressive (VAR) framework. The authors address the limitations of the original Diebold-Yilmaz (DY) framework, which relies on Cholesky factor identification and is sensitive to variable ordering. Their approach uses forecast-error variance decompositions that are invariant to variable ordering, allowing for the measurement of directional spillovers. The study applies this method to analyze daily volatility spillovers across U.S. stock, bond, foreign exchange (FX), and commodity markets from January 1999 to January 2010. Key findings include: 1. **Total Spillovers**: The total volatility spillover index, which measures the contribution of spillovers of volatility shocks across asset classes to the total forecast error variance, is approximately 12.6% on average. 2. **Directional Spillovers**: The directional spillovers, which measure the volatility shocks transmitted from one market to another, show significant variations over time. During tranquil periods, these spillovers are generally below 5%, but they increase to nearly 10% during volatile times. 3. **Net Spillovers**: Net volatility spillovers, which account for both the transmission and reception of volatility shocks, are particularly notable during the recent global financial crisis. The stock market was a significant transmitter of volatility to other markets, especially after the collapse of Lehman Brothers in September 2008. The paper also examines the dynamics of spillovers using rolling-sample analysis, revealing several cycles and bursts in volatility spillovers, particularly during the tech bubble burst in 2000, the 2007-2009 financial crisis, and other significant events. The findings highlight the importance of understanding and monitoring cross-market volatility spillovers, especially in the context of financial crises.