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Measuring Persistence in Volatility Spillovers

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  • Conrad, Christian
  • Weber, Enzo

Abstract

This paper analyzes volatility spillovers in multivariate GARCH-type models. We show that the cross-effects between the conditional variances determine the persistence of the transmitted volatility innovations. In particular, the influence of a foreign volatility innovation on a conditional variance is even more persistent than an own innovation unless this effect is offset by an according negative variance spillover of sufficient size. Moreover, ignoring the latter causes a downward bias in the estimate of the initial impact of foreign volatility innovations. Applying the concept to portfolios of small and large firms, we find that shocks to small firm returns affect the large firm conditional variance once we allow for (negative) spillovers between the conditional variances themselves.

Suggested Citation

  • Conrad, Christian & Weber, Enzo, 2013. "Measuring Persistence in Volatility Spillovers," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79850, Verein für Socialpolitik / German Economic Association.
    • Handle: RePEc:zbw:vfsc13:79850
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    Cited by:

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    3. Karanasos, Menelaos & Paraskevopoulos, Alexandros G. & Menla Ali, Faek & Karoglou, Michail & Yfanti, Stavroula, 2014. "Modelling stock volatilities during financial crises: A time varying coefficient approach," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 113-128.
    4. Pedersen, Rasmus Søndergaard, 2017. "Inference and testing on the boundary in extended constant conditional correlation GARCH models," Journal of Econometrics, Elsevier, vol. 196(1), pages 23-36.
    5. Karanasos, Menelaos & Menla Ali, Faek & Margaronis, Zannis & Nath, Rajat, 2018. "Modelling time varying volatility spillovers and conditional correlations across commodity metal futures," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 246-256.
    6. Xiaochun Liu, 2018. "Structural Volatility Impulse Response Function and Asymptotic Inference," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 316-339.

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    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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