Correlation impulse response functions

Christian M. Hafner () and Helmut Herwartz ()
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Christian M. Hafner: Université catholique de Louvain, LIDAM/ISBA, Belgium
Helmut Herwartz: University of Gottingen

No 2023030, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: Volatility impulse response functions are a widely used tool for analyzing the temporal impact of shocks on (co-)volatilities of financial time series. This paper proposes an extension to correlation impulse response functions (CIRF), based on a multivariate GARCH modeling framework. As we show, CIRF and corresponding covariance impulse response functions can react quite differently to a given shock and even move in opposite directions. Due to the inherent nonlinearity, no analytical form is available for CIRF, but we propose a straightforward algorithm to estimate the CIRF numerically. In an empirical application we focus on the change of the consensus protocol of Ethereum in 2022 and its effect on the correlation with Bitcoin.

Keywords: Dependence; causality; multivariate GARCH; conditional correlation; cryptocurrencies (search for similar items in EconPapers)
JEL-codes: C32 G15 (search for similar items in EconPapers)
Pages: 13
Date: 2023-11-30
Note: In: Finance Research Letters, 2023, vol. 57, 104176
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2023030

DOI: 10.1016/j.frl.2023.104176

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