Abstract
AbstractThis 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 lmaz (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.
Highlights
Recent global economic developments have revived interest in propagation mechanisms that explain how economic shocks spread internationally
This paper is the first to provide volatility impulse response functions (VIRF) for DCC-GARCH and proposes an alternative to the volatility transmission mechanism estimated via the dynamic connectedness approach of Diebold and Yilmaz (2014) without using a rolling-window framework
Based on the VIRF, the generalized forecast error variance decomposition (GFEVD) is computed, which can be interpreted as the variance share one variable explains on others
Summary
Recent global economic developments have revived interest in propagation mechanisms that explain how economic shocks spread internationally. The volatility transmission mechanism has been investigated as a two-step procedure, whereas in the first step a multivariate generalized autoregressive heteroskedasticity (GARCH) procedure is utilized to receive time-varying volatilities which are used in the second step as fundamentals of a rolling-window VAR estimation procedure Among others, this procedure is employed by (Beirne, Caporale, Schulze-Ghattas, and Spagnolo; 2013, Hoesli and Reka; 2013), and Antonakakis (2012). This paper is the first to provide VIRFs for DCC-GARCH and proposes an alternative to the volatility transmission mechanism estimated via the dynamic connectedness approach of Diebold and Yilmaz (2014) without using a rolling-window framework.
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