This study introduces volatility impulse response functions (VIRF) for dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) models. In addition, the implications with respect to network analysis—using the connectedness approach of Diebold and Y ιlmaz (Journal of Econometrics, 2014, 182(1), 119–134)—is discussed. The main advantages of this framework are (i) that the time‐varying dynamics do not underlie a rolling‐window approach and (ii) that it allows us to test whether the propagation mechanism is time varying or not. An empirical analysis on the volatility transmission mechanism across foreign exchange rate returns is illustrated. The results indicate that the Swiss franc and the euro are net transmitters of shocks, whereas the British pound and the Japanese yen are net volatility receivers of shocks. Finally, the findings suggest a high degree of comovement across European currencies, which has important portfolio and risk management implications.

中文翻译：

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DCC-GARCH 模型的波动率脉冲响应分析：波动率传递机制的作用

本研究为动态条件相关——广义自回归条件异方差（DCC-GARCH）模型引入了波动性脉冲响应函数（VIRF）。此外，还讨论了有关网络分析的影响——使用 Diebold 和 Y ιlmaz 的连通性方法（计量经济学杂志，2014，182（1），119-134）——进行了讨论。该框架的主要优点是（i）时变动力学不是滚动窗口方法的基础；（ii）它允许我们测试传播机制是否随时间变化。对跨汇率收益波动传导机制的实证分析进行了说明。结果表明，瑞士法郎和欧元是冲击的净传递者，而英镑和日元是冲击的净波动率接收者。最后，研究结果表明，欧洲货币之间存在高度联动，这对投资组合和风险管理具有重要意义。