Mikhail Stolbov MGIMO-University HOW ARE INTERBANK AND SOVEREIGN DEBT MARKETS LINKED? EVIDENCE FROM 14 OECD COUNTRIES, THE EURO AREA AND RUSSIA HSE, 2 April 2013

Motivation for research and theoretical underpinnings Financial crises (in particular, banking ones) are reliable predictors of sovereign debt crises (Reinhart, Rogoff 2010, 2011) Contingent liabilities is the main transmission mechanism (Cebotary 2008; Laeven ana Valencia 2008) Interbank lending markets are often the epicenters of banking crises, triggering their amplification due to network effects

Motivation for research and theoretical underpinnings It seems natural to try tracking causal linkages (if any) between interbank lending and sovereign debt market indicators to capture the credit risk transfer between the markets in terms of the crisis and to identify most important countries involved Earlier attempts dealt with CDS spreads of banking institutions and sovereigns (Alter Schuler 2011; Ejsing Lemke 2011; Acharya et al. 2012)

Data and methodology Major economies in focus – 14 OECD countries+Euro area+Russia Indicators (monthly) 3-month interbank interest rates or their equivalents (INTB_RATE) and secondary market yields of 10-year sovereign bonds (PUBDEBT_RATE) Time September 2008 – August 2012

Data and methodology Country-level and sample-wide analysis For individual countries: 1)Constructing VARs 2)Optimal lag length selection 3)Granger causality tests 4) For some countries, non-parametric causality test (Diks Panchenko 2006)

Causality direction for individual countries From INTB_RATE to PUBDEBT_RATE: Canada From PUBDEBT_RATE to INTB_RATE: Denmark, Norway, Poland Bi-directional: UK, Russia, Switzerland, South Korea, Iceland Unobserved linkages: US, Euro area, Japan, Australia, Czech Republic, New Zealand, Sweden

Multiple causality relationships Unobserved linkages are not necessarily absent, as they may emerge in a roundabout way, i.e. country A is characterized by bi-directional causality between the markets and at the same time is closely connected with country B in each of them, say, being Granger–caused by B in the interbank market and Granger–causing B in the public debt one. B has no direct causality between the markets. Then an impulse generated by country B’s interbank market will ultimately affect this country’s public debt rates through several transmission stages.

Multiple causality relationships 1)Construction of multivariate VARs for each of the market 2)Optimal lag length selection 3)Granger causality/Block exogeneity Wald tests 4)Calculation of the Net Spillover Index (NSI) for each of the markets to define most important (systemic) countries

Interbank lending market: multiple causality relationships

Sovereign debt market: multiple causality relationships

Most important countries Interbank lending market: US, UK, Russia, Switzerland, Poland Sovereign debt market: Australia, Canada, Poland, UK

Network structure of the causal linkages The relationships at the markets overlap to a rather moderate extent (29% of linkages) The density of connectedness and clustering ratios at the interbank lending and sovereign debt markets are moderate as well (36 and 38%; 0.41 and 0.44 respectively) These characteristics pertain to discrete incomplete (“thin”) networks (Allen Gale 2000, 2007) which may trigger financial contagion due to fewer incentives to rapidly re-allocate liquidity to bail-out their participants

Timing of the credit transfer 1) Based on dynamic correlation indices (sums of the elements of rolling correlation matrices for 6-, 12- and 18-month windows) 2)October-November 2009 October 2008 (as measured by CDS spreads)