European sovereign bond spreads: financial integration and market conditions
Dimitris Georgoutsos and
Petros Migiakis
Applied Financial Economics, 2013, vol. 23, issue 20, 1609-1621
Abstract:
In the present article, we examine the dynamics of euro-area sovereign bond yield spreads focusing on issues related to financial integration and market conditions. The property of a root falling near the unity threshold, in the data generation process of the underlying bond yields, marks the necessity for careful econometric specification. Thus, we formulate the sovereign bond yield spreads, for 10 Economic and Monetary Union (EMU), countries against the Bund as autoregressive processes with nonlinear properties, with the use of both Markov switching and smooth transition autoregression techniques. This way we examine, rather than assume, whether stable parity conditions existed in the underling bond yields and the effects of various events, for a period extending to early 1990s and the Maastricht Treaty. The results validate the presence of nonlinear characteristics in the stochastic processes of the series and that the case of a slow mean reverting process cannot be rejected irrespective of the regime we examine.
Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://hdl.handle.net/10.1080/09603107.2013.842637 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:apfiec:v:23:y:2013:i:20:p:1609-1621
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAFE20
DOI: 10.1080/09603107.2013.842637
Access Statistics for this article
Applied Financial Economics is currently edited by Anita Phillips
More articles in Applied Financial Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().