Annex I: The CIMDO-Copula—Incorporation of Changes in Distress Dependence as Probabilities of Distress (PoDs) of Individual Sovereigns Change
Adrian T. and Moench, E. (2008). “Pricing the Term Structure with Linear Regressions”, Federal Reserve Bank of New York Staff Report No 340.
Afonso A. and Strauch, R. (2004). “Fiscal Policy Events and Interest Rate Swap Spreads: Evidence from the EU”, ECB Working Paper, No. 303.
Bernoth K., von Hagen, J. and Schuknecht, L. (2004), “Sovereign Risk Premia in the European Government Bond Market”, ECB Working Paper, No. 369.
Coudert, V. and Gex, M. (2008). “Does risk aversion drive financial crises? Testing the predictive power of empirical indicators”, Journal of Empirical Finance, 15, 67-184.
Espinoza, R. and Segoviano, M. (2010). “Probabilities of Default and the Market Price of Risk in a Distressed Economy”, IMF Working Paper, (forthcoming).
Geyer, A., Kossmeier, S., and Pichler, S. (2004). “Measuring systematic risk in EMU government yield spreads”, Review of Finance, 8, 171-97.
Hartelius, K., Kashiwase, K., Kodres, L. (2008) “Emerging Market Spread Compression: Is It Real or Is It Liquidity?”, IMF Working Paper, 08/10.
Manganelli S. and Wolswijk, G. (2007). “Market Discipline, Financial Integration and Fiscal Rules: What Drives Spreads in the Euro Area Government Bond Market?”, ECB Working Paper, No. 745.
Mody A. (2009). “From Bear Stearns to Anglo Irish: How Eurozone Sovereign Spreads Related to Financial Sector Vulnerability”, IMF Working Paper, 09/108
Schuknecht, L., von Hagen, J., and Wolswijk, G. (2010). “Government bond risk premiums in the EU revisited. The impact of the financial crisis”, ECB Working Paper, No. 1152.
Segoviano, M. (2006). “Consistent Information Multivariate Density Optimizing Methodology”. Financial Markets Group, Discussion Paper No. 557.
Director General of Risk Analysis and Quantitative Methodologies, Comisión Nacional Bancaria y de Valores (Mexican Financial Authority).
Without any implication, we would like to thank Peter Dattels, Joseph Di Censo, Julio Escolano, Raphael Espinoza, Geoffrey Heenan, Matthew Jones, Laura Kodres, Christian Mulder, Robert Sheehy, and Mark Stone for constructive comments and discussions. We would also like to thank Julia Guerreiro for excellent research assistance. Any errors are solely the authors’ responsibility.
This work focuses on sovereign spreads for advanced markets. Hartelius K. et al (2008) look at the influence of liquidity and fundamentals on sovereign spreads with a focus on emerging markets.
For example, Geyer et al (2004) extract the common factor embedded in EMU spreads based on the use of a Kalman filter. Sgherri and Zoli (2009) apply a Bayesian filtering technique to extract the time-varying common factor from a non-linear model of the sovereign spreads.
For instance, one could think of developments in an individual country affecting all other countries in the sample, without any changes in global risk aversion. In that case, the common trend cannot be considered as a measure of global risk aversion, but rather of contagion.
The sample is made of the first 12 countries that joined EMU, excluding Finland and Luxembourg (due to lack of long and reliable CDS spread series).
Note that while the EMU sovereign bond market became thinner during the most acute phase of the crisis, it was still liquid enough in the case of these sovereign 10-year benchmarks to materialize in actual trades. Bid-ask spreads were definitely wider and volumes fell, yet to levels high enough to allow this type of analysis.
The daily series for the fiscal variables where obtained by using a linear interpolation on the underlying quarterly data. This is based on the assumption that these variables tend to explain the low-frequency movements in the swap spreads, with almost no impact on high frequency (daily) variations.
These probabilities of default are estimated by dividing the level of the Credit Default Swap (CDS) by its Recovery Rate (R). See Luo (2005).
In theory, this is based on the assumption of market completeness, which should hold for all the countries. From a practical viewpoint, however, one could price several countries’ assets using a one factor model (for instance, by performing Fama-MacBeth regressions on the sample of stocks in all the countries under consideration).
VIX is the Chicago Board Options Exchange Volatility Index, a popular measure of implied volatility of S&P 500 index options.
The Fama-MacBeth regression is a method used to estimate parameters for asset pricing models such as the Capital Asset Pricing Model (CAPM).
This is a single, exogenously given, measure of global risk aversion, for all the countries included in the sample. Note that Espinoza and Segoviano (2010) estimate this market price of risk using the US Libor OIS rate and the VIX, thus not relying on any country-specific information from the euro area economies (see Box 1 for details).
We assume a recovery rate of 40% for sovereigns, as commonly used in the literature.
We heuristically introduce the copula approach to characterize dependence structures of random variables and explain the particular advantages of the CIMDO-copula in Annex I.
The presence of the lag dependent variable mitigates the autocorrelation that would otherwise be observed in the residuals from estimating equation  without the lag dependent variable.
A regression analysis that omits the effect of increased risk aversion or contagion might lead to an overestimation of the effects played by country-specific fundamentals.
The estimation of this model for all the countries in the sample was carried out in EViews via Maximum Likelihood Estimation, using the Marquardt Optimization Algorithm.
We follow the European market convention for which swap spread tightens (widens) when bond yields rise (fall) versus swap yields, even in those cases where bond yields are actually above swap yields.
While conditional probabilities do not imply causation, they can provide important insights into interlinkages and the likelihood of contagion.
The last row in each table shows the (weighted)-average contribution to the changes in our stress dependence measure (SC) from each of the column countries. This is a proxy for the source of contagion to the other countries in the euro area, emanating from each of these countries during a particular period.
In assessing these extensions we should be mindful of the specific conditions in which Euro area countries operate. In principle, sovereigns, unlike banks, could prevent excessive funding pressures if they borrowed in their own currency and had a floating exchange rate. The problem is that Euro area countries borrow in a currency that they cannot directly control. This is clearly not the case for the additional set of countries we consider in this section.