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A class of DCC asymmetric GARCH models driven by exogenous variables [PDF]
This paper considers Dynamic Conditional Correlations (DCC) GARCH models in which the time-varying coefficients, including the conditional correlation matrix, are functions of the realizations of an exogenous stochastic process. Time series generated by this model are in general nonstationary.
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Purpose: In today's world, tensions and conflicts in international relations can have profound effects on the global economy. The COVID-19 pandemic that began in 2020 and the subsequent Russia-Ukraine war have particularly led to adverse impacts on a global scale, especially through commodity markets.
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Linear time-varying regression with a DCC-GARCH model for volatility
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