Systemic risk in European sovereign debt markets: A CoVaR-copula approach

Reboredo, Juan C.; Ugolini, Andrea

doi:10.1016/j.jimonfin.2014.12.002

Cited by 218 publications

(117 citation statements)

References 23 publications

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“…Suh (2014) proposes a new contagion measure based on CoVaR and concludes that its magnitude is large even for stable economies in the Eurozone. Reboredo and Ugolini (2015) construct CoVAR measures based on copulas to estimate systemic risk in the European sovereign debt market.…”

Section: Introductionmentioning

confidence: 99%

Assessing systemic risk and its determinants for advanced and major emerging economies: the case of ΔCoVaR

Stolbov

2015

Int Econ Econ Policy

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Section: Introductionmentioning

confidence: 99%

Assessing systemic risk and its determinants for advanced and major emerging economies: the case of ΔCoVaR

Stolbov

2015

Int Econ Econ Policy

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“…Second, ARCH (autoregressive conditional heteroskedasticity)-type models such as dynamic conditional correlation (DCC) [3] and their related models for copulas [11,17] permit the time-varying parameters to vary according to the functions of the lagged observables. One advantage of the second approach is that it avoids the need to "integrate out" the innovation terms driving the latent time series processes [25,26]. In addition, as pointed out by McAleer [27], DCC may suffer from the problem of the derivation of asymptotic properties of the Quasi-Maximum Likelihood Estimators.…”

Section: A Copula With Gas Dynamicsmentioning

confidence: 99%

Dependence Structures and Systemic Risk of Government Securities Markets in Central and Eastern Europe: A CoVaR-Copula Approach

2018

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Abstract:In this study, we proposed a new empirical method by combining generalized autoregressive score functions and a copula model with high-frequency data to model the conditional time-varying joint distribution of the government bond yields between Poland/Czech Republic/Hungary, and Germany. Capturing the conditional time-varying joint distribution of these bond yields allowed us to precisely measure the dependence of the government securities markets. In particular, we found a high dependence of these government securities markets in the long term, but a low dependence in the short term. In addition, we report that the Czech Republic showed the highest dependence with Germany, while Hungary showed the lowest. Moreover, we found that the systemic risk dynamics were consistent with the idea that the global financial crisis not only had spillover effects on countries with weak economic fundamentals (e.g., Hungary, which had the highest systemic risk), but also had contagion effects for both CEEC-3 countries and Germany. Finally, we confirm that three major market events, namely the EU accession, the global financial crisis, and the European debt crisis, caused structural changes to the dynamic correlation.

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“…CoVaR values can be represented and computed from copulas (Reboredo, 2015;Reboredo & Ugolini, 2015a, 2015b as follows. Note that Eq.…”

Section: Downside and Upside Precious Metal Price Spilloversmentioning

confidence: 99%

Downside/upside price spillovers between precious metals: A vine copula approach

Reboredo

Ugolini

2015

The North American Journal of Economics and Finance

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Systemic risk in European sovereign debt markets: A CoVaR-copula approach

Cited by 218 publications

References 23 publications

Assessing systemic risk and its determinants for advanced and major emerging economies: the case of ΔCoVaR

Assessing systemic risk and its determinants for advanced and major emerging economies: the case of ΔCoVaR

Dependence Structures and Systemic Risk of Government Securities Markets in Central and Eastern Europe: A CoVaR-Copula Approach

Downside/upside price spillovers between precious metals: A vine copula approach

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