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Article . 2020
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Journal of Forecasting
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Journal of Forecasting
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Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms

the role of volatility transmission mechanisms
Authors: David Gabauer;

Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms

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.

Countries
Austria, New Zealand
Related Organizations
Keywords

dynamic connectedness, 330, volatility spillovers, volatility impulse response functions, ANZSRC::3802 Econometrics, ANZSRC::4905 Statistics, exchange rates, variance decomposition

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
134
Top 1%
Top 10%
Top 1%
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