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Did long-memory of liquidity signal the European sovereign debt crisis?

  • Z. Sun
  • P. A. Hamill
  • Y. Li
  • Y. C. Yang
  • S. A. Vigne
S.I.:Application of O. R. to Financial Markets

Abstract

This paper analyses high frequency MTS data to comprehensively evaluate the liquidity of the European sovereign bond markets before and during the European sovereign debt crisis for eleven countries. The Hill index, Generalized Hurst exponent and Dynamic Conditional Score are employed to evaluate the properties of the bid-ask spread. Sovereign bonds exhibit the stylized facts reported for a range of financial markets. The 1-min interval analysis indicates the level of bid-ask spread exhibits long-memory and the change in bid-ask spread experiences volatility clustering. In a dynamic setting, the volatility of bid-ask spread also exhibits long-memory in most European sovereign bond markets across all three maturities. Long-memory effects diminish (disappear) for 5-min (15-min) interval, and for short-term maturity (peripheral countries) is stronger than long-term maturity (core countries). Analysis of sub-periods indicates that long-memory process reached its peak during European sovereign debt crisis from May 2010 to December 2011. This analysis suggests that estimating long-memory parameters for high-frequency data could be a useful tool to monitor market stability.

Keywords

European Sovereign debt Liquidity Long-memory 

JEL Classification

C1 G01 G11 

Notes

Acknowledgements

Youwei Li acknowledges the support of National Natural Science Foundation of China (No. 71571197).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Financial Conduct AuthorityLondonUK
  2. 2.Emirates Institute for Banking and Financial StudiesDubaiUAE
  3. 3.Queen’s Management SchoolQueen’s University BelfastBelfastUK
  4. 4.Hull University Business SchoolUniversity of HullHullUK
  5. 5.Lochlann Quinn School of BusinessUniversity College DublinBelfield, Dublin 4Ireland

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