Contagion in the Euro Area Sovereign Bond Market

Muratori, Umberto

doi:10.3390/socsci4010066

Cited by 17 publications

(7 citation statements)

References 33 publications

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“…By separating countries into peripheral and core groups and then using a spatial panel model, Muratori () investigates contagion among selected EMU countries over the period between January 2007 and the end of September 2013; contagion is once again based on the movement in 10‐year government bond yield spreads. He reports evidence of contagion especially among peripheral countries, with there being little change in the magnitude of contagion over sub periods.…”

Section: Crises In Greece and Contagion: The Literaturementioning

confidence: 99%

Was it different the second time? An empirical analysis of contagion during the crises in Greece 2009–15

Bird

Pentecost

et al. 2017

World Economy

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Over the period between end‐2009 and end‐2015, Greece experienced two discernible financial crises. This paper undertakes a correlation analysis of risk premia to investigate the nature and extent of contagion from these crises to other selected Eurozone countries. A commonly expressed view is that the effects of the second crisis were more muted since the systemic risks were seen by markets as being lower. However, using a rolling correlation model, a Dynamic Conditional Correlation GARCH (DCC‐GARCH) model and a t‐copula model we find that this is not the case. Broadly speaking, the contagion effects of the second crisis were at least as large as those associated with the first one.

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Section: Crises In Greece and Contagion: The Literaturementioning

confidence: 99%

Was it different the second time? An empirical analysis of contagion during the crises in Greece 2009–15

Bird

Pentecost

et al. 2017

World Economy

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“…The starting point for determining the elements w ij,t consisted in the determination of the economic distance between pairs of objects (economies/countries) according to the following formula (and other formulas, see e.g. : Claeys, Moreno, Suriñach, 2012;Asgharian, Hess, Liu, 2013;Asgharian, Larsson, Liu, 2014;Muratori, 2015…”

Section: Methodsmentioning

confidence: 99%

Spatio‑Temporal Analysis of the Impact of Credit Rating Agency Announcements on the Government Bond Yield in the World in the Period of 2008–2017

Szulc

Wleklińska

2019

Folia Oeconomica

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The paper concerns the impact of announcements published by rating agencies on the government bond yield in selected countries of the world. Ratings assigned to debt securities on account of the issuer’s financial standing are an important determinant of their yield. Factors that affect the rate of return of a given traded debt, in addition to idiosyncratic factors, i.e. those related to the issuer’s economy, and global factors, also include the ratings of connected countries. Moreover, empirical studies carried out in this area prove that the relationship is asymmetrical. This allows us to suppose that favourable information concerning the improvement of government bond ratings is not reflected in the decrease in their yield. The aim of the paper is the analysis of interactions between the yields of 10‑year government bonds issued by selected economies. A subject that is of particular interest is the evaluation of the impact of positive and negative changes in credit rating assessments made by international agencies on the yield of bonds issued by other economies than the country concerned in the assessment. The spatial scope of the analysis concerns 10‑year government bonds issued by 40 countries in the period of 2008-2017. In the study, dynamic spatial models for pooled time series and cross‑sectional data and dynamic spatial panel data models were used.

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“…The role of fundamentals is particularly strong for high-debt and low-growth countries (e.g., Portugal, Italy, Ireland, Greece, Spain, also called PIIGS Countries). For both descriptive purposes and quality picture representation, Figure 1 The sovereign debt crisis in the Eurozone has increased the attention to country risk and generated a large and rich literature (e.g., Dieckmann & Plank, 2012;Fontana & Scheicher, 2010;Lucas et al, 2014;Muratori, 2015). The main issue is that, quite often, the statistics used to evaluate risk-dependence among countries are based on covariance or correlation measures (ECB, 2016).…”

Section: The Cds Market and Systemic Risk Transmission Mechanism In Tmentioning

confidence: 99%

Modeling inter-country spatial financial interactions with Graphical Lasso: An application to sovereign co-risk evaluation

Arbia

Bramante

Facchinetti

et al. 2018

Regional Science and Urban Economics

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We propose a model to extract significant risk spatial interactions between countries adopting the Graphical Lasso algorithm, used in graph theory to sort out spurious conditional correlations. In this context, the major issue is the definition of the penalization parameter. We propose a search algorithm aimed at the best separation of the variables (expressed in terms of conditional dependence) given an a priori desired partition. The case study focuses on Credit Default Swap (CDS) returns over the period 2009-2017. The proposed algorithm is used to estimate the spatial systemic risk relationship between Peripheral and Core Countries in the Euro Area.

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Contagion in the Euro Area Sovereign Bond Market

Cited by 17 publications

References 33 publications

Was it different the second time? An empirical analysis of contagion during the crises in Greece 2009–15

Was it different the second time? An empirical analysis of contagion during the crises in Greece 2009–15

Spatio‑Temporal Analysis of the Impact of Credit Rating Agency Announcements on the Government Bond Yield in the World in the Period of 2008–2017

Modeling inter-country spatial financial interactions with Graphical Lasso: An application to sovereign co-risk evaluation

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