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Correlation impulse response functions

Author

Listed:
  • Hafner, Christian M.

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Herwartz, Helmut

    (University of Gottingen)

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.

Suggested Citation

  • Hafner, Christian M. & Herwartz, Helmut, 2023. "Correlation impulse response functions," LIDAM Reprints ISBA 2023030, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2023030
    DOI: https://doi.org/10.1016/j.frl.2023.104176
    Note: In: Finance Research Letters, 2023, vol. 57, 104176
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    More about this item

    Keywords

    Dependence ; causality ; multivariate GARCH ; conditional correlation ; cryptocurrencies;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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