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"Dynamic connectedness between credit and liquidity risks in EMU sovereign debt markets"

Author

Listed:
  • Marta Gómez-Puig

    (Department of Economics and Riskcenter, Universitat de Barcelona. 08034 Barcelona, Spain.)

  • Mary Pieterse-Bloem

    (Section Finance in Business Economics, Erasmus School of Economics, 3062 PA, Rotterdam, and Rabobank**, 3521 CB, Utrecht, the Netherlands. Phone: +316-5136 5132.)

  • Simón Sosvilla-Rivero

    (Complutense Institute for Economic Analysis, Universidad Complutense de Madrid. 28223 Madrid, Spain.)

Abstract

We examine the dynamic interconnection between sovereign credit and liquidity risks in ten euro area countries at the 5-year maturity with high-frequency data from MTS over the period January 2008-December 2018 using the extension of the TVP-VAR connectedness approach of Antonakakis et al. (2020). Our results indicate that for most periods net connectedness is from credit risk to liquidity risk, but this indicator is time-dependent, detecting some episodes where it goes from liquidity risk to credit risk. We set up an event study and find that the latter episodes can be related to several unconventional monetary policy measures of the ECB. Then, we examine the drivers of the connectedness indicator by means of a Probit model. Our results suggest that monetary policy shocks and economic policy uncertainty increase the probability of risk transmission from liquidity to credit, while global funding liquidity, tensions in financial markets and surprises in inflation and GDP are factors that reduce such probability.

Suggested Citation

  • Marta Gómez-Puig & Mary Pieterse-Bloem & Simón Sosvilla-Rivero, 2022. ""Dynamic connectedness between credit and liquidity risks in EMU sovereign debt markets"," IREA Working Papers 202217, University of Barcelona, Research Institute of Applied Economics, revised Oct 2022.
    • Handle: RePEc:ira:wpaper:202217
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    Keywords

    Liquidity risk; Credit risk; Eurozone sovereign bonds; MTS bond market; Dynamic connectedness; Time-varying parameters. JEL classification: C22; C53; G12; G14; G15.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    NEP fields

    This paper has been announced in the following NEP Reports:

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