A Vector-Autoregression Analysis of Credit and Liquidity Factor Dynamics in US LIBOR and Euribor Swap Markets

By Murphy, Finbarr; Murphy, Bernard | Journal of Economics and Finance, April 2012 | Go to article overview
Save to active project

A Vector-Autoregression Analysis of Credit and Liquidity Factor Dynamics in US LIBOR and Euribor Swap Markets


Murphy, Finbarr, Murphy, Bernard, Journal of Economics and Finance


Abstract We use a vector autoregressive approach to investigate the determinants of US Dollar LIBOR and Euribor swap spread variation during the 2007-2009 crisis in global credit and money markets. Using market-quoted yield and spread data from the highly liquid credit default swap (CDS) and overnight index swap (OIS) markets, we provide compelling empirical evidence that liquidity risk factor shocks have been the dominant drivers of the variation in swap spreads over this period. Our findings provide an explanation for the temporal differences that liquidity shocks have on swap spreads and provide a contemporary perspective on the dynamical interplay between credit-default and liquidity risk-factors in these markets. As all our risk-factor proxies are traded in liquid derivatives markets, our findings have implications for proprietary hedge fund traders hedging an exposure to swap-spread risk, for bank treasurers managing their liquidity requirements and for central bankers seeking to better understand the response of markets to their macroeconomic policy implementation and liquidity management actions. Indeed our markets-based analysis suggests that the European Central Bank (ECB) has underperformed relative to the Federal Reserve in terms of the differing levels of market confidence placed in its macroeconomic policy actions and remedial liquidity interventions during the period.

Keywords Vector Autoregression . OIS Spreads . Credit . Liquidity

JEL Classification C32 . G01 . G15

(ProQuest: ... denotes formula omitted.)

1 Introduction and motivation

US Corporate debt exceeded US$6 Trillion in 20081 and remains one of the largest traded asset classes. Key to understanding and managing the risk of this asset class is the additional risk associated with the debt over and above perceived, default-free paper such as US sovereign debt. The determinants of this additional risk are of particular interest to fixed-income portfolio managers, and have key implications for bank treasurers, proprietary swap traders and indeed government agencies charged with responsibility for the structuring and risk-management of sovereign debt programmes. Our focus in this paper is aimed at understanding the variation in the spreads on default and liquidity risk prone, fixed for floating interest rate swaps. Moreover, the perspective we adopt is very much influenced by the intuition and marketbased practices adopted by a proprietary swap trader, or bank treasurer charged with managing the bank's liquidity requirements in the interbank term deposit and swap markets.

Using a vector autoregressive (VAR) analysis, we determine the contribution of both liquidity and credit-default risk-factors to swap spreads using highly-liquid market-traded proxies, the LIBOR-OIS spread and the spread on CDS contracts. The impact of the credit crisis from 2007 is examined and analyzed using both Granger Causality testing and impulse-response analytics. The analysis contributes to the growing literature in this area by providing empirical evidence that liquidity and default risk have considerably stronger effects on swap spreads than previously estimated. Our analysis further extends the contributions of earlier work (Duffie and Singleton 1997) by providing an inter-temporal perspective on the dynamical interplay between credit-default and liquidity risk-factors, and hence between the credit and money markets. Moreover, given the severity of the credit crisis that commenced in 2007, the relative impacts of credit and liquidity shocks on swap spreads have undoubtedly changed. Based on our use of highly-liquid credit default swap and overnight-index swap data, possible new scenarios are presented.

There is an abundance of literature (Longstaffand Schwartz 1995; Grinblatt 1995; Duffie and Singleton 1997; Duffee 1999; Collin-Dufresne et al. 2001; Elton et al. 2001; Huang and Huang 2003) demonstrating that the difference in yields between corporate bonds and Treasury debt cannot be fully explained by structural models.

The rest of this article is only available to active members of Questia

Sign up now for a free, 1-day trial and receive full access to:

  • Questia's entire collection
  • Automatic bibliography creation
  • More helpful research tools like notes, citations, and highlights
  • Ad-free environment

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
Loading One moment ...
Project items
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

Cited article

A Vector-Autoregression Analysis of Credit and Liquidity Factor Dynamics in US LIBOR and Euribor Swap Markets
Settings

Settings

Typeface
Text size Smaller Larger
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

While we understand printed pages are helpful to our users, this limitation is necessary to help protect our publishers' copyrighted material and prevent its unlawful distribution. We are sorry for any inconvenience.
Full screen

matching results for page

Cited passage

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

Cited passage

Welcome to the new Questia Reader

The Questia Reader has been updated to provide you with an even better online reading experience.  It is now 100% Responsive, which means you can read our books and articles on any sized device you wish.  All of your favorite tools like notes, highlights, and citations are still here, but the way you select text has been updated to be easier to use, especially on touchscreen devices.  Here's how:

1. Click or tap the first word you want to select.
2. Click or tap the last word you want to select.

OK, got it!

Thanks for trying Questia!

Please continue trying out our research tools, but please note, full functionality is available only to our active members.

Your work will be lost once you leave this Web page.

For full access in an ad-free environment, sign up now for a FREE, 1-day trial.

Already a member? Log in now.

Are you sure you want to delete this highlight?