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What is the risk of European sovereign debt defaults?Fiscal space,CDS spreads and market pricing of risk q
Joshua Aizenman a ,*,Michael Hutchison b ,Yothin Jinjarak c
a
Robert R.and Katheryn A.Dockson Chair in Economics and International Relations,USC,Los Angeles,CA 90089,USA b
Department of Economics,UC Santa Cruz,Santa Cruz,CA 95064,USA c
DeFiMS,SOAS,University of London,London WC1H0XG,UK
JEL classi ?cations:E43F30G01H631
Keywords:CDS spreads Sovereign risk Fiscal space Default risk Eurozone
a b s t r a c t
We estimate the pricing of sovereign risk for ?fty countries based on ?scal space (debt/tax;de ?cits/tax)and other economic funda-mentals over 2005–10.We focus in particular on ?ve countries in the South-West Eurozone Periphery,Greece,Ireland,Italy,Portugal and Spain.Dynamic panel estimates show that ?scal space and other macroeconomic factors are statistically and economically important determinants of sovereign risk.However,risk-pricing of the Eurozone Periphery countries is not predicted accurately either in-sample or out-of-sample:unpredicted high spreads are evident during global crisis period,especially in 2010when the sovereign debt crisis swept over the periphery area.We match the periphery group with ?ve middle income countries outside Europe that were closest in terms of ?scal space during the European ?scal crisis.Eurozone Periphery default risk is priced much higher than the matched countries in 2010,even allowing for differences in fundamentals.One interpretation is that these economies switched to a “pessimistic ”self-ful ?lling expectational equilibrium.An alternative interpretation is that the market prices not on current
q We thank seminar participants at the Bank for International Settlements,Danmarks Nationalbank,the Bank of Canada,Columbia –Tsinghua international economics workshop,Chulalongkorn University (Sasin),Association for Public Economic Theory 2011Conference,and the National Institute for Public Finance and Policy 9th Research Meeting (New Delhi,India,2012)for very helpful comments.We also thank participants at the conference “The European Sovereign Debt Crisis:Background and Perspectives ”(Bank of Denmark,Copenhagen,April 3–4,2012),and especially our discussant,Marcus Miller,for helpful comments.
*Corresponding author.
E-mail address:aizenman@https://www.doczj.com/doc/1615508861.html, (J.
Aizenman).
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Journal of International Money
and Finance
journal homepage :www.else https://www.doczj.com/doc/1615508861.html,
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0261-5606/$–see front matter ó2012Elsevier Ltd.All rights reserved.https://www.doczj.com/doc/1615508861.html,/10.1016/j.jimon ?n.2012.11.011
Journal of International Money and Finance 34(2013)37–59
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but future fundamentals,expecting adjustment challenges in the Eurozone periphery to be more dif ?cult for than the matched group of middle-income countries because of exchange rate and monetary constraints.
ó2012Elsevier Ltd.All rights reserved.
1.Introduction
During 2000–2006,the OECD and most emerging markets experienced a remarkable decline in macroeconomic volatility and the price of risk.This period turned out to be the tail-end of the Great Moderation ,a precursor of the turbulences leading to the global ?nancial crisis of 2008–09,the consequent increase in risk premia,and the focus on ?scal challenges and the importance of ?scal space in navigating future economic challenges.The latter stages of the crisis,unfolding in 2010,focused attention on the heterogeneity of the Euro block,and the unique challenges facing the ?ve South-West Eurozone Periphery countries,or SWEAP group (Greece,Ireland,Italy,Portugal,and Spain),in adjusting to ?scal fragility in the context of a ten-year old currency union.1
This paper investigates the pricing of risk associated with the sovereign debt crisis that escalated during 2010in several European countries,and culminating in the selective default on Greek sovereign debt in early 2012.Our objective is to determine whether the perception of relatively high sovereign default risk of the ?scally distressed Euro area countries,as seen in market pricing of credit default swap (CDS)spreads,may be explained by existing past or current fundamentals of debt and de ?cits relative to tax revenues –which we term de facto ?scal space –and other economic determinants.2Our analysis allows us to address several questions.Does ?scal space help systematically explain the evolution of the market pricing of risk beyond that embedded in other macroeconomic indicators?Was risk in some markets (e.g.SWEAP)“overpriced ”in 2010judging by model predictions using the pre-vailing values of ?scal space other macro variables?
Our objectives for the empirical work are three-fold.Firstly,we determine whether CDS spreads (in a panel regression setting)are related to ?scal space measures and other economic determinants.Secondly,we address whether there is an identi ?able dynamic pattern to CDS spreads during the crisis period.Thirdly,we investigate pricing differentials of CDS spreads in the Euro area,and the SWEAP in particular,compared to other countries.We seek to answer the question of whether Euro area and SWEAP CDS spreads follow the same pattern as the rest of the world or may they considered “mis-priced ”in some sense,especially during the 2010European debt crisis.
To this end,we develop a pricing model of sovereign risk for a large number of countries (50)within and outside of Europe,before and after the global ?nancial crisis,based on ?scal space and other economic fundamentals including the foreign interest rate,external debt,trade openness,nominal depreciation,in ?ation,GDP/Capita and economic growth.We use this dynamic panel model to explain CDS spreads and determine whether the market pricing of risk is comparable in the affected European countries and elsewhere in the world.By this methodology,and using in-sample and out-of-sample predictions,we can determine whether there are systematically large prediction errors for the CDS spreads during the global ?nancial crisis and in 2010when the sovereign debt crisis in Europe intensi ?ed.Systematically large prediction errors may be due to mispricing of risk or may be attrib-utable to expectations of a future decline in fundamentals that lead to higher default risk.We also “match,”on the basis of similar ?scal space,each SWEAP country with a corresponding Middle Income country.This provides additional information on the market pricing of risk between SWEAP and countries with similar ?scal conditions but,unlike SWEAP,with histories of debt restructuring.
1
The SWEAP acronym for these ?ve countries is used in Buiter and Rahbari (2010).
2
Our measure of ?scal space is from Aizenman and Jinjarak (2010).They propose a stock and ?ow measure of de facto ?scal space.The stock variable is de ?ned as the inverse of the tax-years it would take to repay the public debt.In this paper,?scal space is measured both as outstanding government debt and government de ?cits,relative to the de facto tax base.The de ?cits measure is the realized tax collection,averaged across several years to smooth for business cycle ?uctuations.
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J.Aizenman et al./Journal of International Money and Finance34(2013)37–5939 Our investigation reveals a complex and time-varying environment in the market for sovereign default risk.Speci?cally,we?nd empirically a key role of?scal space in pricing sovereign risk, controlling for other relevant macro variables.The link is economically and statistically strong,and robust to the time dimension of the CDS premium,sample period,included control variables and estimation technique.We?nd that risk of default in the SWEAP group appeared to be somewhat “underpriced”relative to international norms in the period prior to the global?nancial crisis and substantially“overpriced”countries during and after the crisis,especially in2010,with actual CDS values much higher than the model would predict given fundamentals.In addition,compared to the matched group,controlling for?scal space and macroeconomic conditions,all of the SWEAP countries have much higher default risk assessments measured by CDS premiums.One potential explanation for the switch from under-to over-pricing of default risk is that markets were forward looking,not pricing entirely on current fundamentals but on expected further deterioration in future SWEAP fundamentals, especially in the realm of?scal space.Alternatively,the results are consistent with multiple equilib-rium with an abrupt switch from a“good”(optimistic)expectations equilibrium in the Euro Area–with low expected default rates and low interest rates where?scal positions are sustainable–to a“bad”(pessimistic)expectations equilibrium in these same countries–with high expected default rates and high interest rates where?scal positions are not sustainable.
The next section discusses the data.The third section provides a preliminary statistical analysis.The fourth section presents the empirical results.We close the paper with a discussion on possible inter-pretation of the emerging SWEAP risk premia,including the handicapping effect of being a member of a currency union,which reduces the country’s scope of adjustment via exchange rate and monetary policy.
2.CDS spreads as a measure of sovereign default risk
2.1.CDS spreads on sovereign bonds
We measure the market perception of sovereign default risk by the spreads on sovereign credit default swaps(CDS)at various maturities(tenors).3CDS instruments are mainly transacted in over-the-counter(OTC)derivative markets.The spreads represent the quarterly payments that must be paid by the buyer of CDS to the seller for the contingent claim in the case of a credit event,in this case non-payment(or forced restructuring)of sovereign debt,and is therefore an excellent proxy for market-based default risk pricing.4The total CDS market grew from about10trillion USD in2004,when statistics were?rst systematically reported,to a peak prior to the global?nancial crisis of almost60 trillion USD in mid-2008,and then fell sharply.The estimated gross(net)notional amount of sovereign CDS outstanding was2447(233)billion USD in2010,compared to about2196billion USD in government-issued international debt securities(BIS,2010).Fig.1shows the outstanding notional amounts of CDS instruments on sovereign bonds across countries in late February2011.Italy has the largest outstanding CDS notional amounts,at almost USD300billion,followed by Brazil,Spain and Turkey with notional amounts outstanding of over USD150billion.
Sovereign CDS provide a market-based realtime indicator of sovereign credit quality and default risk.We consider sovereign CDS spreads at several maturities d three,?ve and ten-year maturities, across industrial countries and emerging markets.Despite the low probability of a credit event in most advanced economies,CDS markets are still active in most markets as buyers can use the sovereign CDS
3See Packer and Suthiphongchai(2003)and Fontana and Scheicher(2010)for discussions of sovereign CDS markets.
4An alternative proxy for default risk is the interest rate spread of sovereign debt.From an empirical standpoint,there are three main advantages of using CDS spreads rather than interest rate spreads.Firstly,CDS statistics provide timelier market-based pricing with larger coverage of industrial and developing countries than sources for national bond market rates. Secondly,using CDS spreads avoids the dif?culty in dealing with time to maturity as in the case of using interest rate spreads(of which the zero yields would be preferred).Recent estimates from the Bank for International Settlements suggest that the average original and the remaining maturities of government debt instruments vary markedly across countries(BIS,2010). Thirdly,interest rate spreads embed in?ation expectations and demand/supply for credit conditions as well as default risk.We are only interested in default risk.
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as a hedge and for mark-to-market response.5Buyers of the sovereign CDS may or may not own the underlying government bonds.The latter case is termed ‘naked ’sovereign CDS,and frequently labeled as a speculation.
CDS prices in our study are taken from CMA Datavision,a platform that is based on quotes collected from a consortium of over thirty independent swap market participants.CMA aggregates the most recent quotes and delivers the data intraday.The quoting convention for CDSs is the annual premium payment as a percentage of the notional amount of the reference obligation.The sovereign CDS spreads are reported in basis points,with a basis point equals to $1000to insure $10million of debt.6CDS spreads are London closing values.While CMA is not the sole provider of CDS prices,Mayordomo et al.(2010)?nd that,in a comparison of six major providers,CMA quotes are most consistent with a price discovery process.The majority of sovereign CDS in the market are denominated in the US dollar;in our sample,about one-third of the CDS is Euro-denominated.The CMA data set provides a broad coverage of CDS pricing over countries and years.
Appendix A provides data sources and Appendix B a list of countries in the entire sample,the subset of countries included in the empirical estimation,and means of 3,5and 10-year CDS spreads (in basis points),?scal space and other macroeconomic indicators during the sample (2005–10).2.2.Empirical studies on CDS spreads
Empirical studies of CDS (corporate and sovereign)are relatively new and usually deal with market microstructure issues.Our study,by contrast,is in line with the macro/international ?nance literature which considers macroeconomic determinants of country risk assessments and ?nancial crises.
Several ?ndings are particularly relevant to our analysis.As noted by Packer and Suthiphongchai (2003)and others,the CDS premium should in principle be approximately equal to the credit spread
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A R G c d s A U S c d s A U T c d s
B E L c d s B G R c d s B R A c d s
C H L c d s C H N c d s C O L c d s C Z E c d s
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L B N c d s L T U c d s L V A c d s
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U N c d s T U R c d s U K R c d s U S A c d s
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Fig.1.Global sovereign CDS positions in early 2011.This ?gure provides the gross notional amount of outstanding sovereign CDS positions (billion US$)as of February 25,2011.Source:Depository Trust &Clearing Corporation (DTCC).
5
Sovereign CDS can also be used to supplement corporate CDS to hedge for country risk.
6For example,if the spread is 197basis points,meaning 197,000USD to insure against 10,000,000in sovereign debt for 10years;1.97%of notional amount needs to be paid each year,so 0.0197?10million ?$197,000per year.
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J.Aizenman et al./Journal of International Money and Finance34(2013)37–5941 of the reference bond of the same maturity under certain conditions.However,Fontana and Scheicher (2010)demonstrate that the“basis”,i.e.difference between CDS spreads and the spreads on the underlying government bonds,was not zero in Eurozone CDS markets during late2010.They suggest that sizable deviations are attributable to limits to arbitrage and slow moving capital.Secondly,at high frequency(intraday),differences in credit quality(measured by CDS prices)are found to explain sovereign yield spreads of the Euro area governments(Beber et al.,2009).7Fontana and Scheicher (2010)argue that high CDS premium in late2010during the Eurozone debt crisis may be partly attributable to a decline in the appetite for credit-risky instruments and falling market liquidity rather than entirely due concerns about principle losses on outstanding sovereign debt.In addition,empirical research?nds that daily sovereign interest spreads are more likely to lead sovereign CDS spreads in emerging markets(Ammer and Cai,2007).8Taken together,both studies suggest that sovereign interest rates and CDS spreads have common underlying causes,rather than one driving the other.This is consistent with other work where some studies indicate that price discovery?rst occurs in the CDS market and follows in the bond market,and vice versa.9
There is evidence that CDS spreads may be driven by macroeconomic conditions.Amato(2005) decomposes CDS spreads into risk premium and risk aversion and?nds that both are in?uenced by macroeconomic conditions.Packer and Zhu(2005)?nd that contractual terms in?uence CDS spreads, but that signi?cant“regional effects”(differential pricing across regions)is also evident.Micu et al. (2006)also?nd that credit rating announcements have a large in?uence on CDS spreads.In explain-ing recent developments,Berndt and Obreja(2010)suggest that“economic catastrophe risk”rose sharply in2007–08pushing up CDS spreads.Cecchetti et al.(2010)plot several?scal indicators against CDS spreads for advanced economies.They?nd correlations across countries with substantial hetero-geneity.10Longstaff et al.(2011)study sovereign CDS of several emerging markets from October2000to January2010.They found that the sovereign spreads are more associated with global factors(US stock, treasury,and high yield markets)than local factors(stock return,exchange rate,and foreign reserve). Dooley and Hutchison(2009)?nd that?nancial,economic and regulatory“news”emanating from the U.S.during the global?nancial crisis quickly impacted sovereign CDS spreads in emerging markets.
Concerns about price determination,systemic risk to the?nancial system,and the general opacity of the over-the-counter markets(where CDS is now traded)have led to calls more government regulation and for CDS to be traded on organized exchanges.Presently CDSs are not traded on an exchange and there is no required reporting of transactions to a government agency.One reason for the traditionally limited government oversight is that OTC markets are considered wholesale markets for professional participants,rather than retail investors.However,the recent?nancial turmoil has shown that OTC derivative markets can negatively affect other functioning?nancial markets and can be a serious risk to the health of the banking system.These observations have led to increasing calls for the regulation and oversight CDS markets.For example,in the event that regulatory reforms require that CDS be traded and settled via a central exchange/clearing house,there would no longer be‘counter-party risk’,as the risk of the counterparty will be held with the central exchange/clearing house.11
3.Statistical contours
Monthly5-year CDS spreads from2005to2011are plotted for the SWEAP and other selected countries(countries which are matched with the SWEAP group,discussed below)in Fig.2.Judging by
7Beber et al.study microstructure data of bond quotes and transactions from the interdealer markets,covering Austria, Belgium,Finland,France Germany Greece,Italy,Netherlands,Portugal and Spain.Their sample period is April2003–December 2004.
8Ammer and Cai examine daily data from February2001to March2005,covering Brazil,China,Colombia,Mexico, Philippines,Turkey,and Uruguay.On interest rates,CDS spreads,and speculation,see also the discussion of?ndings from the European Commission(Tait and Oakley,2010),which suggests no strong causality between the two.
9See Carboni(2011)for a recent review of the empirical literature on this point.
10For example,they plot the forecast level of general government debt/GDP against January2010CDS spreads for21 advanced economies and?nd a positive correlation.
11Kiff et al.(2009)discuss systemic risk and calls and government regulation policy options in the CDS market.
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these spreads,?nancial stress in global markets emerged in early 2008but became dramatic and widespread in late 2008.The turbulence subsided by mid-2009in most countries,with the notable exception of the SWEAP group:Greece,Ireland,Italy,Portugal,and Spain.For Greece,the unraveling of its ?scal condition in late 2009resulted in a manifold increase in sovereign CDS spreads.For Ireland,the sovereign CDS spreads have widen sharply twice,in early 2009and in late 2010.By 2010,spreads of the SWEAP group were already much higher than those of most emerging market countries (e.g.above the spreads of South Africa,Mexico,Panama,Malaysia and Colombia in Fig.2).On the other hand,sovereign spreads of Germany and the US remained very low throughout the period (not shown).
Table 1reports the annual mean values (standard deviation in parentheses)of sovereign CDS spreads (5-year tenor),?scal space and macroeconomic fundamentals for SWEAP and other country groupings.The table shows average values for the period before crisis (2005–07)and during the crisis (2008–10and individual years).The fall of 2008was the height of the global ?nancial crisis,while the latter part of 2009was a recovery period as ?nancial panic and the liquidity crisis subsided for most countries.However,the SWEAP sovereign debt crisis manifested mainly in 2010and later.Prior to the crisis,SWEAP CDS values were quite low,ranging from 8to 17basis points on average,which are somewhat but not markedly higher than the mean of other Euro countries (11basis points)and lower than the non-Euro OECD group (35basis points).During the early months into the global ?nancial crisis,2008–09,sovereign CDS spreads rose in virtually every country.However,spreads dropped very substantially in all regions by 2010except for the Euro area excluding SWEAP,where it remained roughly unchanged,and SWEAP,where it rose dramatically.SWEAP CDS average values in 2010ranged from 238basis points in Italy to 1027basis points in Greece.By contrast,in 2010,OECD countries (non-Euro members)had an average CDS spread of 118basis points and non-SWEAP Euro members had an average spread of 101basis points.
As our main objective is to investigate the link between ?scal space and the pricing of default risk,we also track the adjustment of ?scal capacity across countries.Table 1shows the ?scal balance to tax base ratio and the public debt to tax base ratio.OECD (excluding the Euro countries)and non-SWEAP Euro countries experienced an increase in debt/tax ratios by 0.2percent and 0.3percent,respectively,
100200300400ESP
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Fig.2.Evolution of sovereign CDS prices.This ?gure plots CDS 5-year tenor (basis points)for selected middle-income countries and Eurozone members.
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T a b l e 1S u m m a r y s t a t i s t i c s o f s o v e r e i g n C D S v a l u e s a n d f u n d a m e n t a l s .
C o u n t r y S o v e r e i g n C
D S 5-y r t e n o r (b a s i s p o i n t s )
F i s c a l b a l a n c e /t a x b a s e
P u b l i c d e b t /t a x b a s e
05–070809
10
08–1005–0708091008–1005–070809
10
08–10
S p a i n 16.7100.7113.5347.7187.30.05à0.12à0.31à0.27à0.231.11.11.51.71.4G r e e c e 15.0232.1283.41026.5514.0à0.18à0.29à0.48à0.33à0.373.23.54.04.54.0I r e l a n d 8.6181.0158.0619.2319.40.05à0.24à0.47à1.08à0.600.91.52.23.22.3I t a l y 13.4156.9109.2238.0168.0à0.07à0.07à0.13à0.11à0.102.52.52.82.82.7P o r t u g a l 10.496.391.7497.3228.4à0.13à0.11à0.30à0.27à0.221.91.92.22.42.2M i d d l e -i n c o m e g r o u p 124.2(102.8)829.9(1069.4)295.2(331.3)
233.2(221.5)
452.8(540.7)à0.06(0.2)à0.08(0.2)à0.22(0.2)à0.19(0.1)à0.16(0.2)2.7(2.6)2.2(2.1)
2.3(1.9)
2.3(1.9)
2.3(2.0)
H i g h -i n c o m e (n o n O E C D )32.1(13.9)330.7(158.9)164.0(90.5)
172.3(118.4)
222.3(122.6)0.13(0.4)0.24(0.5)0.10(0.4)0.16(0.5)0.17(0.5)
1.2(0.8)
1.0(0.6)
1.7(0.1)
1.4(0.8)
1.4(0.5)
O E C D (n o n E u r o )34.8(41.5)260.3(238.8)120.3(103.5)117.8(96.4)166.2(146.2)0.04(0.2)0.01(0.2)à0.12(0.1)à0.08(0.1)à0.06(0.1)1.7(1.7)1.7(1.7)1.9(1.8)1.9(1.8)1.8(1.8)E u r o (e x c l u d i n g S W E A P )
11.0(13.3)95.1(40.2)54.2(24.6)
101.3(55.2)83.5(40.0)à0.03(0.0)à0.02(0.0)à0.15(0.1)à0.14(0.1)à0.10(0.1)1.4(0.5)
1.4(0.5)
1.6(0.5)
1.7(0.4)
1.6(0.5)
T r a d e /G D P I n ?a t i o n (%)
E x t e r n a l d e b t /G D P
05–070809
10
08–1005–07080910
08–10
05–07
08
09
10
08–10
S p a i n 0.60.60.50.50.53.551.400.802.781.661.41.51.71.51.6G r e e c e 0.60.60.50.50.53.322.642.9717.347.651.31.41.81.71.7I r e l a n d 1.51.61.60.91.43.754.05à4.481.300.297.88.910.711.010.2I t a l y 0.60.60.50.60.52.302.261.022.101.791.11.01.21.11.1P o r t u g a l 0.70.80.60.70.72.600.710.002.521.081.91.92.32.42.2M i d d l e -i n c o m e g r o u p 0.9(0.5)0.9(0.5)0.8(0.4)
0.9(0.5)
0.9(0.4)6.74(4.7)9.73(6.8)5.24(5.9)6.61(5.4)
7.19(6.0)
0.5(0.3)
0.4(0.3)
0.4(0.3)
0.4(0.2)
0.4(0.3)
H i g h -i n c o m e (n o n O E C D )0.9(0.0)0.9(0.1)0.8(0.0)
0.8(0.0)
0.8(0.0)8.20(6.2)8.02(7.3)
à0.90(3.9)
1.37(0.7)
2.83(4.0)
0.6(0.2)
0.7(0.2)
0.9(0.2)
0.8(0.2)
0.8(0.2)
O E C D (n o n E u r o )0.8(0.3)0.9(0.4)0.8(0.3)0.8(0.4)0.8(0.4)3.38(2.3)4.60(3.4)3.02(3.5)3.08(1.4)3.57(2.8)1.0(1.1)1.3(2.0)1.5(2.5)1.5(2.4)1.4(2.3)E u r o (e x c l u d i n g S W E A P )
1.2(0.4)1.3(0.4)1.2(0.5)
1.2(0.4)1.2(0.4)
2.56(1.0)2.13(1.1)0.94(0.5)
2.05(0.6)
1.71(0.7)
1.8(1.0)
1.8(0.9)
2.0(0.9)
1.9(0.9)
1.9(0.9)
T h i s t a b l e r e p o r t s m e a n a n d s t a n d a r d d e v i a t i o n (i n p a r e n t h e s i s )o f s o v e r e i g n C D S s p r e a d s (b a s i s p o i n t s )a n d m a c r o f u n d a m e n t a l s .T a x b a s e i s a n a v e r a g e t a x /G D P o v e r a p e r i o d o f p r e v i o u s 5y e a r s .A p p e n d i x A p r o v i d e s d a t a s o u r c e s a n d A p p e n d i x B d e t a i l s t h e c o u n t r y g r o u p s .
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between 2005and 07and 2010.For Ireland and Greece,the deterioration in ?scal circumstances was much more drastic:the government debt of Ireland climbed from 25%of GDP in 2007to almost 100%of GDP in 2010,while the government debt of Greece grew from 95%to over 140%.As a result,the debt/tax ratio of Ireland jumped from 0.9to 3.2and that of Greece from 3.2to 4.5,leaving both countries with a much less room for a ?exible policy response on the ?scal side.The large increase of debt/tax ratios in both countries captures a high degree of distress in their economic fundamentals,including the government assumption of banking sector liabilities in the case of Ireland.Similarly large deteriora-tions in ?scal space for these countries are also evident in the fall in ?scal balance to tax ratios.
We illustrate further the 2005–07?scal preconditions,as measured by debt/tax (and debt/gdp)and de ?cit/tax (de ?cit/gdp),in the two panels of Fig.3by country group.Lower pre-crisis government debt and lower ?scal de ?cits relative to the tax base imply greater ?scal capacity.The ?gure shows that ?scal space was weakest (highest levels of debt and de ?cits relative to the tax base)in the middle-income
countries,although the average debt to gdp ratio was lower than the other groups.This re ?ects
generally lower tax bases in the middle income countries.Generally,the SWEAP had more limited ?scal space during the tranquil period than other high-income groups –higher average debt and de ?cits to the relative to the tax base (despite a signi ?cant budget surplus in Ireland during this period),and a higher level of debt to GDP.4.Methodology and empirical results 4.1.Methodology
The dependent variables in our formal empirical work are sovereign CDS spreads of 5year maturity (regressions with 3and 10-year maturities are shown in the appendix table),12estimated in a panel
regression,y it ?a i tl t tq D y it à1tX 0it b t3it ;where y is the CDS spread,i stands for country and t for
year;X is a vector of controls.The sample covers a panel of 50countries from 2005to 2010.The vector X includes ?scal space (government debt/tax and ?scal de ?cit/tax)d the focus of our work d as well as several other variables that frequently employed in the literature to explain country risk.These variables are the TED spread,external debt (total foreign liabilities/GDP),trade openness (trade/GDP)and in ?ation.
As the conditions for consistent estimation in the dynamic panel are known to be demanding,our solution is to work with both a simple ?xed-effects model and other estimation speci ?cations,including clustered standard errors and the Arellano-Bond dynamic panel estimator.Arellano-Bond is a GMM estimator with instruments (exogenous and lagged values),well suited to the problem at hand,with a substantially larger number of cross-section units (50countries),but requires many period of data as the instruments to account for the correlation of lagged dependent variable and the unobserved error terms.We consider ?xed effects,clustered standard errors,and GMM in several variants of the model as bounding the causal effects of interest.The sovereign spreads are estimated in level.We also consider for GMM the dependent variable in log multiplied by a hundred,allowing the coef ?cients to be interpreted in terms of a percentage change of sovereign default risks (this terminology also aligns with standard practice in the ?nancial sector that discusses the percentage change of CDS spreads).4.2.Dynamics of CDS spreads and Euro/SWEAP pricing differentials
Table 2considers the dynamics and structure of CDS pricing over the 2005–10sample period.Differential pricing for the SWEAP and other Eurozone countries (non-SWEAP)is investigated.To focus on CDS pricing dynamics during the global and European ?nancial turmoil,we include time dummy variables (t 2008–t 2010)for three crisis years:2008is identi ?ed as the central part of the global ?nancial crisis,2009is identi ?ed as a partial recovery period,and 2010is identi ?ed with the SWEAP
12
Our CDS data set contains 1–10year maturities.We focus on the 5-year maturity since this is the deepest and most actively traded CDS market.While there is no precise international account of government debt maturity,recent statistics suggest that the average original maturity of central government debts is around 10years for both emerging markets and industrial countries (BIS,2010).Hence,we also report results for 10-year maturities in the appendix,as well as 3-year maturities for a robustness test.
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debt crisis and post-global ?nancial crisis.We also include interaction terms between the three time dummy variables and the SWEAP countries (dummy variables denoting a SWEAP country)and between the three dummy variables and the non-SWEAP Euro countries.
In all of the CDS spread regressions,the ?scal space measure (higher value is equivalent to lower ?scal capacity)is positive and statistically signi ?cant at the 1percent level –higher levels of sovereign debt and ?scal positions (de ?cit or debt)relative to the tax base signi ?cantly increase market pricing of sovereign default risk.Speci ?cally,a percentage point rise in the debt/tax ratio is estimated to increase the 5-year CDS spread by between 15and 81basis points,while a one percentage point rise in the ?scal
1
2
3
4
5
P u b l i c D e b t /T a x (r a t i o )
20.40.
60.80.P u b l i c D e b t /G D P (%)
?2
?1
1
2
3
1234
5
Middle-Income
SWEAP
OECD (non Euro)
High-Income (non OECD)
Euro
(excluding SWEAP)
Middle-Income
SWEAP
OECD (non Euro)
(non OECD)
(excluding SWEAP)
High-Income Euro
Fig.3.Fiscal space in 2005–07.Public debt/GDP divided by tax base (an average tax/GDP over a period of previous 5years).Fiscal balance/GDP divided by tax base (an average tax/GDP over a period of previous 5years).
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balance to tax base ratio is estimated to lower the spread by 194–829basis points.The wide variation in the estimates,all highly statistically signi ?cant,varies with the speci ?c estimation methodology d the low estimates are associated with estimation with clustered standard errors and the high estimates associated with ?xed effects estimation.Of the control variables,only in ?ation is systematically and robustly correlated with CDS spreads (higher in ?ation leads to higher spreads).Appendices C.1and C.2provide the robustness checks,supporting the effect of ?scal conditions on sovereign spreads in different econometric speci ?cations.13
All of the coef ?cients on the 2008–10year dummy variables are economically large and statistically signi ?cant.Controlling for other factors,sovereign spreads in 2008were 295–349points higher than average rates over the 2005–10period for full sample of countries.Spreads were much lower in 2009,by some estimates somewhat lower than sample average spreads,but rose somewhat on average in 2010.For the full sample,the ?nancial crisis impact on CDS spreads was concentrated in 2008.
For Euro countries,including the SWEAP group,sovereign spreads were substantially less than the international average in 2008.SWEAP CDS were spreads were 159–249points lower than average spreads prevailing in 2008.SWEAP spreads were somewhat above the average in 2009and then rose sharply in 2010,recording levels 174–324points above average.It is evident that sovereign default risk in the SWEAP was differentially priced much higher than the average of other countries,and moved in
Table 2
Dynamics of CDS spreads.
Balanced (whole)sample:2005–10(1)
(2)
(3)
(4)
coeff.(s.e.)
coeff.(s.e.)coeff.(s.e.)coeff.(s.e.)t 2008295.6(78.3)***334.1(102.3)***328.0(78.0)***349.3(97.5)***t 200935.4(27.7)à4.7(21.0)à36.8(33.7)à35.5(16.7)**t 2010
92.9(27.1)***58.8(12.7)*** 2.5(32.6)
42.5(14.0)***t 2008?Euro dummy à209.5(80.7)***à216.0(84.5)**à225.3(82.3)***à238.6(80.3)***t 2009?Euro dummy à15.0(30.8)80.5(30.4)***14.6(30.1)84.2(29.4)***t 2010?Euro dummy à29.1(28.0)56.2(24.3)** 5.2(26.6)
48.0(26.5)*
t 2008?SWEAP à159.3(82.7)*à194.4(93.2)**à249.5(98.2)**à235.6(86.6)***t 2009?SWEAP 73.4(36.1)**136.1(30.8)***18.7(58.6)113.8(34.1)***t 2010?SWEAP 261.9(63.7)***324.4(58.1)***174.4(107.9)287.7(53.6)***TED spread 7.3(27.8)à0.8(13.6) 3.2(27.3) 1.3(11.3)y (t à1)0.3(0.1)***0.3(0.1)***0.2(0.1)***0.3(0.0)***Trade/GDP à118.0(128.8)à24.9(29.2)à86.1(150.7)à41.9(28.1)In ?ation
19.8(10.3)*24.6(6.2)***24.5(11.9)**23.8(6.0)***External debt/GDP à1.9(17.9)7.9(2.3)***à36.6(30.1) 3.7(2.3)Public debt/tax base 81.0(29.9)***14.7(3.9)***Fiscal balance/tax base à829.4(302.0)***à194.1(55.9)***Constant term à710.7(347.2)**à87.7(25.6)***286.0(271.7)à37.3(26.0)R 2
0.460.480.520.48Observations 300300300300Countries (i )50505050Fixed effects (i )Yes
No
Yes
No
Serial correlation y (t à1)y (t à1)y (t à1)y (t à1)Clustered s.e.(i )
No
Yes
No
Yes
The dependent variable (y )is sovereign CDS 5-year tenor in basis points.South-West Euro Area Periphery (SWEAP)includes Greece,Ireland,Italy,Portugal,and Spain.Tax base is an average tax/GDP over a period of previous 5years.TED spread (3-month US$LIBOR à3-month US treasury)and In ?ation are in percent.All variables are in realtime (t ),except the lagged CDS,y (t à1).Standard errors are in parentheses,with ***(**,*)denoting statistical signi ?cance at 1(5,10)level.
13
For GMM estimation,the test statistics (p -values reported)indicate that these dynamic panel regressions perform well on the whole sample.The Sargan test of over-identifying restrictions has a null hypothesis of exogenous instruments;in all cases,cor-responding p -values of the Sargan test cannot reject the null.The AR(1)test has a null of no autocorrelation in ?rst differences and the AR(2)test has a null of no autocorrelation in levels;in most cases,the test cannot reject that average autocovariance in residuals of order 1[AR(1)]is 0,whereas the results from AR(2)test are inconclusive (given that time dimension of the panels in GMM is constrained to 6at most,2005–10,subject to the construction of the lagged instrument values).The Sargan test provides some level of con ?dence that the residuals are uncorrelated with a group of explanatory variables.
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J.Aizenman et al./Journal of International Money and Finance34(2013)37–5947 the opposite direction of the international trend in2010.Risk assessments were falling around most of the world in2010but rising sharply in the SWEAP group.
4.3.CDS spreads,fundamentals and structural change
The focus on dynamics highlight how SWEAP experienced much higher CDS spreads than most of the world in2010,even controlling for their deteriorating?scal situation and other factors.Table3 considers stability issues by estimating the model over the full balanced sample,2005–10(columns 1–4),pre-crisis sample(2005–07,columns5–8)and crisis sample(2008–10,columns9–12).The sub-samples are symmetric by including the same set of countries over a period of three years.The same set of control variables is also included in these regressions,with a focus on?scal space,but the time dummies and interactive terms are excluded.
The estimates for the full sample in Table3are included for comparison with the results in Table2 which include time dummies and interactive terms.The results,not surprisingly,are very similar.And the results are robust to whether actual or“structural”(cyclically adjusted)?scal space measure(?scal balance divided by tax base)are used in the regressions,shown in Appendix C.3.However,important differences are evident in the estimates from the two sub-samples.In particular,all of the?scal space estimates are highly statistically and economically important in the pre-crisis“tranquil”2005–07 sample,shown in columns5–8.Debt and de?cits relative to the tax base clearly lead to much higher risk assessments and CDS spreads.
As noted,a structural break appears to have occurred during the turbulent2008–10crisis episode, shown in columns9–12.During the crisis,pricing of risk is largely decoupled from our two?scal space measures.The ability of the model to explain CDS spreads drops from around70–80%in the tranquil period to45–60%during the crisis.Although explanatory power of?scal space measures drop during the crisis,the TED spread,trade openness,external debt and in?ation play a larger role.Given turbulent conditions during the crisis period,markets apparently did not focus on current?scal fundamentals. One interpretation is that the markets simply overreacted and mispriced risk of default.Another interpretation is that markets may have emphasized more on expectations of future deterioration in ?scal space that were not fully re?ected in current economic conditions.14The emergence of the TED spread as a key pricing factor in the crisis also suggests that expectations of market volatility jumped during the crisis and that this pushed up CDS spreads.In particular,possible default implies that the payoff to creditors is weakly concave(?xed payoff in good times,declining with an adverse shock above a threshold in bad times),suggesting that higher volatility will reduce the expected payoff in countries exposed to higher volatility during a crisis for a given debt/tax or debt/gdp and thereby increasing CDS spreads.15This also explains the impact of the end of the Great Moderation d countries with greater exposure to volatility,other things equal,are facing higher spreads.
4.4.SWEAP and the euro-area CDS pricing before and during the crisis
In order to determine how the balanced sample(2005–10)and pre-crisis sample(2005–07)models of Table3predict for various country groups and the SWEAP countries,we report the in-sample and out-of-sample prediction errors over various years in Table4.Our objective is to determine whether prediction errors demonstrate a discernable pattern.The in-sample prediction errors are estimated
14We also investigated whether the pricing of CDS spreads amongst the SWEAP and the non-SWEAP Euro countries(Euro-SWEAP)respond differently to fundamentals than the rest of world when the full set of fundamental explanatory variables is included.We estimated the model over2005–10,re?ecting the full sample and consisting of both the tranquil and turbulent periods.We focused on10-year CDS spreads and considered interaction terms of SWEAP and Euro-SWEAP with all of the fundamental variables.The point estimates of interaction terms on government debt/tax suggest that the non-SWEAP Euro area countries have much narrower spreads than the sample average and the SWEAP area have much larger spreads.However,these differences are not statistically signi?cant.The same result holds for the other fundamental factors.One exception is the trade openness variable:on average,trade openness is positively associated with CDS spreads,but less than average for non-SWEAP Euro area and more than average for SWEAP.We omit these results for brevity.They are available upon request.
15A related point is made by Aizenman and Marion(2002).
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from the full sample model estimates reported in column 3of Table 3and the out-of-sample errors are estimated from the pre-crisis sample estimates reported in column 9of Table 3.
For exposition purposes,we calculate the prediction errors in Table 4as a ratio of the actual relative to the predicted CDS spreads as:
Prediction Error ?
y it y p it
?Actual 5-yr :CDS spread Predicted 5-yr :CDS spread Hence,if the prediction error is greater than 1,we have a case of under-prediction which provides
supportive evidence that the CDS is over-priced.Table 4reports the prediction errors by individual SWEAP countries and country grouping for the 2008–10and a breakdown for years 2008,2009and 2010.
Table 3
CDS spreads,fundamentals and structural change.Sample
(1)
(2)
(3)
(4)
(5)(6)
Years 2005–10Years 2005–10Years 2005–10Years 2005–10Years 2005–07Years 2005–07coeff.(s.e.)coeff.(s.e.)coeff.(s.e.)coeff.(s.e.)coeff.(s.e.)coeff.(s.e.)TED spread à29.6(38.7)17.5(12.1)64.1(42.2)43.6(13.2)***34.5(10.2)***23.2(7.7)***y (t à1)0.3(0.1)***0.3(0.0)***0.2(0.1)***0.3(0.0)***0.0(0.1)
0.4(0.1)***Trade/GDP à186.7(168.9) 1.2(21.9)182.7(182.2)à12.4(25.3)à92.8(50.8)*à14.0(9.3)In ?ation 38.6(12.3)***28.1(3.0)***35.7(12.9)***29.9(3.5)***9.8(2.2)***8.7(1.3)***External debt/GDP 57.0(32.9)*17.0(3.5)***à53.9(37.2)
9.8(4.7)**
à13.7(11.2)
0.6(1.3)
Public debt/tax base 48.2(48.7)
13.8(4.6)***
123.4(33.1)***18.3(3.0)***
Fiscal balance/tax base à910.3(236.2)***à418.7(87.1)***
Constant term à389.5(541.5)à87.3(20.7)***à501.7(267.5)*à82.5(30.0)***81.9(100.1)à44.1(10.9)***R 2
0.190.350.180.350.840.76Observations 300300300300150150Countries (i )505050505050Fixed effects (i )Yes
No
Yes
No
Yes
No
Serial
correlation y (t à1)y (t à1)y (t à1)y (t à1)y (t à1)y (t à1)Clustered s.e.(i )
No
Yes
No
Yes
No
Yes
(7)
(8)
(9)
(10)
(11)(12)
Years 2005–07Years 2005–07Years 2008–10Years 2008–10Years 2008–10Years 2008–10coeff.(s.e.)
coeff.(s.e.)coeff.(s.e.)coeff.(s.e.)coeff.(s.e.)coeff.(s.e.)TED spread 21.7(7.7)***24.9(6.6)***150.2(42.1)***190.6(65.7)***186.7(45.1)***197.3(62.8)***y (t à1)0.2(0.1)***0.5(0.1)***à0.1(0.1)
0.2(0.0)***à0.1(0.1)
0.2(0.0)***Trade/GDP à58.9(37.5)à24.6(9.1)***à489.7(244.5)**à94.5(63.7)à191.4(199.6)à96.4(65.2)In ?ation 6.5(1.7)***7.5(1.4)***29.5(11.6)**52.2(5.3)***27.2(9.0)***52.9(5.2)***External debt/GDP à5.2(9.6)
1.4(1.4)
189.3(138.7)28.6(13.0)**33.0(102.2)
21.5(12.2)*
Public debt/tax base à182.1(218.7)
14.4(13.1)
Fiscal balance/tax base à291.7(86.5)***à202.9(40.2)***à567.4(606.0)à150.7(163.8)Constant term 107.4(78.7)à6.8(12.4)à621.4(1134.7)à82.0(64.0)à200.3(801.8)à61.9(59.9)R 2
0.890.680.580.450.610.45Observations 150150150150150150Countries (i )505050505050Fixed effects (i )Yes
No
Yes
No
Yes
No
Serial
correlation y (t à1)y (t à1)y (t à1)y (t à1)y (t à1)y (t à1)Clustered s.e.(i )
No
Yes
No
Yes
No
Yes
The dependent variable (y )is sovereign CDS 5-year tenor in basis points.Tax base is an average tax/GDP over a period of previous 5years.TED spread (3-month US$LIBOR à3-month US treasury)and In ?ation are in percent.All variables are in realtime (t ),except the lagged CDS,y (t à1).Standard errors are in parentheses,with ***(**,*)denoting statistical signi ?cance at 1(5,10)level.
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The table shows that in-sample and out-of-sample prediction errors were very substantial across countries and country-groups during the crisis period,with out-of-sample errors particularly large.Average errors over 2008–10,for example,indicate that actual CDS spreads exceeded out-of-sample forecast spreads with multiples ranging from 2.3(Middle Income countries)to 12.9(non-Euro OECD).Under predictions of CDS spreads of this magnitude are extraordinary.Under predictions (out-of-sample)for the SWEAP countries are not particularly high in relative terms,ranging from 2.6(Italy)to 5.3(Ireland).
However,the pattern across years of the crisis sample shows a divergence between the SWEAP and other country groups.In particular,the SWEAP prediction errors were relatively low in 2008but climbed marked in 2010compared to other regions (except the non-SWEAP Euro area).The ?nancial crisis that emerged in 2008struck the OECD (non-Euro)area particularly hard,with very high out-of-sample prediction errors (31.6).These errors were only 1.6in 2009,and climbed to 5.4in 2010.By contrast,the SWEAP area did not experience such dramatic prediction errors in 2008but these errors rose marked in 2009and especially 2010.Perhaps re ?ecting some contagious effect,the Euro area (excluding SWEAP),also experience a dramatic climb in prediction errors in 2010d much larger than the SWEAP group.4.5.Preconditions:?scal positions prior to the ?nancial crisis
Fig.3suggests that,in the run up to the global ?nancial crisis,?scal positions in the Euro area were relatively strong,and the SWEAP area roughly in line with other OECD countries.The ?gure shows the ?scal balance and public debt positions by country group –middle income,high income (non-OECD),SWEAP,OECD (non-Euro area)and the Euro area (non-SWEAP)–before the global ?nancial crisis:the 2005–07average for ?scal balance to GDP and ?scal balance to tax base in the ?rst part of the ?gure,and the 2005–07average public debt to GDP and public debt to tax base ratio in the second part of the ?gure.
Fiscal conditions in Euro countries less the SWEAP were in line with other countries prior to the global ?nancial crisis.The average debt/tax ratio (1.38)was lower than the average of other OECD countries (1.66).The Euro area debt/GDP (55%)was slightly higher than other OECD countries (51%).The SWEAP group had a somewhat worse ?scal position,but not markedly so,with an average debt/tax
Table 4
Prediction errors of sovereign CDS spreads.Country/group
Out (in)sample prediction Prediction error ?actual CDS values divided by predicted CDS values 2008
200920102008–10Spain out 3.9 3.6 6.5 4.7in 3.5 4.0 4.1 3.8Greece out 2.7 2.2 3.3 2.7in 2.3 1.8 1.7 1.9Ireland out 4.2 3.87.9 5.3in 0.8 1.7 2.6 1.7Italy out 2.6 1.6 3.6 2.6in 2.3 1.9 3.3 2.5Portugal
out 2.8 2.9 6.9 4.2in 3.2 4.0 4.5 3.9Middle-income group out 3.9 1.0 1.8 2.3in 2.7 1.2 1.2 1.7OECD (non Euro)out 31.6 1.6 5.412.9in 2.6 1.2 1.4 1.7Euro (excluding SWEAP)
out 5.0 5.813.78.2in
2.1
4.2
3.2
3.2
Based on the predicted values from estimation in Table 3(eqs.(3)and (9))using CDS 5-year tenor.The out-sample prediction uses 2005–07(pre-crisis period)observations.The in-sample prediction uses 2005–10(whole sample)observations.Where actual CDS >predicted CDS,there is a signal of under-prediction (i.e.the sovereign default risk is over-priced).The min and max provide lower and upper bounds of prediction errors extracted from the all the speci ?cations with ?scal balance/tax base and macro controls.The bold numbers indicate in-sample prediction errors,as opposed to out-of-sample errors.
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ratio of 1.94over the period,somewhat above the non-Euro OECD group average (1.66).This is much less than the average for the middle income group (2.75).However,the SWEAP debt to GDP ratio was higher than the other groups at an average 68%over the period running up to the ?nancial crisis.A similar pattern is seen for ?scal balance measures,shown in the second part of Fig.3.
Are ?scal conditions prior to the crisis linked to market responses during the crisis episode?To recall,in the years leading to the Euro,prominent US economists were skeptical about the Euro project,raising questions about the logic of monetary uni ?cation without deep ?scal uni ?cation.Arguably,the ?rst ten years of the Euro,celebrated in 2008,were taken by the market and key observers as an illustration that the earlier ?scal concerns were overblown.This view was supported by the remarkable switch of the initial depreciation of the Euro against the US dollar,into a solid appreciation.In his Keynote at Frankfurt am Main 30May 2008,“The Eurosystem and its Prospects –History in the Making ”Professor Axel Weber,President of the Deutsche Bundesbank,noted
“In 1998,however,the kick-off of the Eurosystem was regarded not only with curiosity and opti-mism,but also with concern and skepticism.On the one hand,pessimists predicted that the euro would be short-lived.They did not want to part with their respective national currencies,which –as in the case of the D-Mark –had often evolved into a symbol of national identity.On the other hand,the euro ’s proponents believed that the single currency would become a major catalyst for structural reform,thereby signi ?cantly boosting economic growth in the euro area.Today,with the bene ?t of hindsight,we know that neither excessive pessimism nor exuberant optimism was justi ?ed.Nonetheless,the Eurosystem has delivered a remarkable performance over its ?rst decade.”“Now,what are the determinants of the Eurosystem ’s success?Why did a currency area with no track record of its own attain such a high degree of credibility in so short a time?As I have already indicated,the bulk of con ?dence in the ?edgling European single currency was gener-ated by the Eurosystem ’s institutional framework,which encompasses its independence,its mandate and its monetary policy strategy,plus an institutional framework geared at commanding the support of equally stability-oriented ?scal policies,as embedded in the Stability and Growth Pact (SGP).Key elements of this institutional framework have been transferred to the Eurosystem from the national central banks (NCBs),including the Deutsche Bundesbank.Consequently,with the transfer of parts of the NCBs ’structure and ethos,the reputation of the currencies that were stable prior to EMU has lived on in the euro.”
With the bene ?t of hindsight,in the years prior to the 2008–09crisis hitting SWEAP,the market may have underestimated the ?scal challenges facing the Euro block,believing that Weber ’s position “with the t ransfer of parts of the national central banks ’structure and ethos,the reputation of the currencies that were stable prior to EMU has lived on in the euro ”was accurate.
Has the SWEAP crisis has been a wake-up call regarding the ?scal vulnerabilities of the EuroZone?Fig.4shows a scatter plot of government ?scal space in 2005–07prior to the ?nancial crisis against 2008–10prediction errors in Euro and non-Eurozone countries (using equations (6)and (8)from Table 3).The left panels show the debt/tax revenue measure and the right panels show the de ?cit/tax revenue ?scal space measure.The correlation in the Eurozone (non-Euro area)between government debt/tax revenues and prediction error is à0.72(à0.28).And the correlation in the Eurozone (non-Euro area)between ?scal balances to tax revenues and prediction error is 0.59(0.09)for other (non Euro)countries in the sample.Euro area countries with high debt and de ?cits (surpluses)during the pre-crisis period experienced lower (higher )CDS spreads (relative to predicted values)than did the non-Euro area countries.
Overall,these data do not support the argument that the market was particularly sanguine about Euro area sovereign risk during the tranquil period and that this led to an over-reaction (over pricing risk)during the crisis period.CDS spreads relative to out-of-sample predicted values in the SWEAP group are high,but not out of line with other country groups.Moreover,there is no evidence that debt and de ?cits in the SWEAP countries prior to the crisis led to high prediction errors during the crisis.4.6.SWEAP compared to “matched ”middle income countries
To gain further insight,we “match ”the SWEAP countries with 5middle-income countries (MI)that in 2010,at the peak of the European crisis,were closest in terms of ?scal space (2010debt/tax).The
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objective is to see if the pricing of risk in the SWEAP was different than corresponding MI countries.The matches (SWEAP to MI),shown in Table 5,are Spain –South Africa,Greece –Panama,Ireland –Malaysia,Italy –Mexico and Portugal –Colombia.These countries had similar debt to tax base ratios in 2010.The question is whether they had other similar economic characteristics,especially the price of sovereign default risk.
Fig.5shows a cluster diagram of the prediction errors during these two periods,depicting the size of debt/tax by circles.The prediction errors are based on the in-sample prediction errors from equa-tions (2)and (4)in Table 3.The circle size is proportional to the 2005–07pre-crisis public debt to tax base ratio.There is a negative correlation (à0.62)between 2005and 07pre-crisis and 2008–10crisis prediction errors,https://www.doczj.com/doc/1615508861.html,rge (small)prediction errors in the tranquil period tend to be followed by small (large)prediction errors in the crisis period.However,there is a wide asymmetry between the clusters of errors of the SWEAP and the clusters of errors of their matched MI countries.In particular,the relatively small prediction errors of the SWEAP countries in the pre-crisis period are followed by quite large prediction errors in the crisis period.By contrast,little relation is seen in the error clusters of the MI countries d a wide variation among the prediction errors in this group in the pre-crisis period is not seen in the crisis period,during which time all of the matched MI country predictions were quite close to realized CDS spreads.
The data in Fig.5suggest that sovereign risk in SWEAP countries is over-priced in comparison with corresponding MI countries.Pursuing this further,Table 5summarizes in more detail the character-istics of the SWEAP with the matched countries.The table shows,before and during the crisis,the evolution of CDS spreads,?scal space,the tax base,in ?ation,currency depreciation,in ?ation external debt,foreign reserves,trade openness and real GDP growth.
This table allows a detailed comparison of the matched countries.In terms of initial conditions,for example,Italy and Mexico had very similar debt/tax ratios in 2005–07(2.2–2.5),but Mexico had much
23456
E s t i m a t e d P r e d i c t i o n E r r o r 2008?10
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E s t i m a t e d P r e d i c t i o n E r r o r 2008?10Public Debt/Tax Base 2005?07
2
34
5
6
2
4
6
8
Fiscal Balance/Tax Base 2005?07
Fig.4.Market overreaction to ?scal pre-conditions.Based on the out-sample predictions eqs.(6)and (8)in Table 3(as summarized in Table 4).The prediction error is the actual CDS divided by the predicted CDS;when >1,there is a signal of under-prediction (i.e.the sovereign default risk is over-priced).This ?gure plots 2008–10prediction errors on CDS 5-year tenor on the vertical axis.The 2005–07?scal space is on the horizontal axis.
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higher borrowing costs and sovereign CDS premium which were more than four times higher than Italy (57points versus 13points).Differential borrowing costs,however,were consistent with a weaker currency and higher in ?ation rates in Mexico.The difference in CDS spreads between the two countries at this time appears is in line with relative ?scal space d similar debt and de ?cit positions –and
Table 5
Matching middle-income countries to SWEAP group.Variable Year Spain South
Africa Greece Panama Ireland Malaysia Italy
Mexico Portugal Colombia Sovereign CDS 5-year tenor
2005–0716.753.215.0115.18.629.213.457.210.4138.02008100.7395.7232.1306.8181.0225.1156.9291.896.3307.52009113.5143.3283.4133.4158.089.6109.2133.391.7142.82010347.7124.31026.599.5619.272.7238.0112.8497.3113.02008–10187.3221.1514.0179.9319.4129.1168.0179.3228.4187.8Public debt/tax base
2005–07 1.1 1.3 3.2 5.70.9 2.7 2.5 2.2 1.9 1.92008 1.1 1.0 3.5 3.9 1.5 2.8 2.5 2.4 1.9 1.72009 1.5 1.1 4.0 3.8 2.2 3.7 2.8 2.4 2.2 1.92010 1.7 1.3 4.5 3.8 3.2 3.6 2.8 2.3 2.4 2.02008–10 1.4 1.2 4.0 3.9 2.3 3.3 2.7 2.4 2.2 1.9Fiscal balance/tax base
2005–070.00.0à0.20.00.1à0.2à0.10.0à0.10.02008à0.10.0à0.30.0à0.2à0.3à0.10.0à0.10.02009à0.3à0.2à0.5à0.1à0.5à0.5à0.1à0.1à0.3à0.12010à0.3à0.2à0.3à0.2à1.1à0.4à0.1à0.2à0.3à0.12008–10à0.2à0.1à0.4à0.1à0.6à0.4à0.1à0.1à0.2à0.1Tax base ?avg.(t à1,.t à5)
2005–0734.625.132.39.829.416.041.517.233.318.9200835.726.731.810.530.315.241.817.733.918.7200935.527.431.910.530.315.242.118.534.318.6201034.727.531.610.529.915.242.718.534.618.32008–1035.327.231.710.530.215.242.218.234.318.5In ?ation (%)
2005–07 3.6 2.7 3.3 4.0 3.8 2.9 2.3 3.7 2.6 5.02008 1.48.2 2.6 6.8 4.1 4.5 2.3 6.50.77.720090.88.6 3.0 1.9à4.5 1.0 1.0 3.60.0 2.02010 2.8 5.617.3 4.9 1.3 2.1 2.1 4.4 2.5 3.22008–10
1.7
7.5
7.7 4.50.3 2.5 1.8 4.8 1.1 4.3Nominal depreciation (%)(against US$)2005–07à1.8 3.0à1.80.0à1.8à3.3à1.8à1.1à1.8à7.32008 5.017.3 5.00.0 5.0à3.0 5.0 1.8 5.0à5.32009
à2.9 2.6à2.90.0à2.9 5.7à2.921.4à2.910.12010 5.4à13.6 5.40.0 5.4à8.6 5.4à6.5 5.4à12.42008–10 2.5 2.1 2.50.0 2.5à2.0 2.5 5.6 2.5à2.5External debt/GDP
2005–07 1.40.1 1.30.67.80.4 1.10.2 1.90.22008 1.50.2 1.40.58.90.3 1.00.2 1.90.22009 1.70.1 1.80.510.70.3 1.20.2 2.30.22010 1.50.1 1.70.511.00.3 1.10.2 2.40.22008–10 1.60.1 1.70.510.20.3 1.10.2 2.20.2Foreign reserves/GDP (%)
2005–07 1.59.9 1.18.50.452.9 4.18.4 5.19.92008 1.312.3 1.010.50.441.6 4.68.7 4.89.82009 1.913.9 1.712.3 1.050.1 6.211.4 6.810.72010 2.312.0 2.110.1 1.044.87.711.69.29.82008–10 1.812.7 1.610.90.845.5 6.210.6 6.910.1Trade/GDP
2005–070.60.60.6 1.5 1.5 2.10.60.60.70.420080.60.70.6 1.6 1.6 1.80.60.60.80.420090.50.60.5 1.4 1.6 1.70.50.60.60.320100.50.50.5 1.50.9 1.80.60.60.70.32008–100.50.60.5 1.5 1.4 1.80.50.60.70.4Real GDP
growth (%)
2005–07 3.7 5.5 3.79.3 5.9 5.9 1.4 3.8 1.5 6.120080.9 3.7 2.010.7à3.0 4.7à1.3 1.50.0 2.72009à3.6à1.8à2.0 2.4à7.1à1.7à5.0à6.5à2.60.82010à0.2 2.8à4.57.5à1.07.2 1.3 5.5 1.3 4.32008–10
à1.0
1.6
à1.5
6.9
à3.7
3.4
à1.7
0.2
à0.4
2.6
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economic performance.By 2010,however,the roles were reversed:Italy had a CDS spread of 238and Mexico 113,despite still having very similar debt/tax and de ?cit/tax ratios and Mexico maintaining higher rates of in ?ation.Pessimism about Europe in 2010appears to have led to higher risk perceptions in Italy compared to Mexico than would be justi ?ed by fundamentals.This observation is seen as well in Fig.5,where the prediction errors for 2008–10averaged about 1.0in Mexico and 2.6in Italy.(It should be noted,however,that real GDP growth was stronger in Mexico than Italy at this time,and its currency stronger.So not all fundamentals pointed to more equal CDS spreads.)
Another illustrative case is Spain and South Africa.The 2005–07(debt/tax)ratios were very similar,but South Africa was subject to higher government borrowing costs and had a substantially higher CDS spread (53points versus 17points).Partly this re ?ected the respective political situations but also that South Africa had higher in ?ation and a higher rate of currency depreciation.Again,the market pricing of risk was reversed in the two countries by 2010with the CDS spread in Spain averaging 348points compared to 124in South Africa.This difference may be partly due to fundamentals d real GDP growth was higher and the debt/tax ratio lower in South Africa.On the other hand,the in ?ation rate in South Africa was almost 6%in 2010,compared to less than 3%in Spain.The suspicion that default risk in Spain is mispriced compared to South Africa is also suggested by the prediction error given in Fig.5d the average CDS spread to predicted spread for Spain during 2008–10is almost 4but about 1in South Africa,where the later indicates no under-or over-pricing.
On the other hand,Table 5also shows that SWEAP had lower foreign reserves than the matched MI countries.Given the Euro status of SWEAP,however,it is uncertain what would be the role of the reserves (compared to a precautionary rationale for reserves hoarding of the emerging markets).Nonetheless,in all cases SWEAP countries had larger external debt (%GDP)and the government bond markets that performed worse in 2010than the matched MI countries.
In summary,there is strong evidence that high market default risk assessments in the SWEAP are partly attributable to deteriorating fundamentals but that a large component is unpredicted.Actual CDS spreads in the SWEAP much higher than what the model predicts,given the actual realization of fundamentals.In terms of the model,these spreads may be “mispriced ”due to excessive pessimism on the part of market participants about the SWEAP or attributable to expectations of the further dete-rioration of fundamentals.This point is well illustrated by a comparison of SWEAP with MI countries with similar ?scal conditions.In every case,risk pricing of the SWEAP is comparatively high given
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4
P r e d i c t i o n E r r o r 2008?10Prediction Error 2005?07
Fig.5.Fiscal space and prediction error on the sovereign CDS 5-year tenor –SWEAP countries and matched middle-income Group.Based on the in-sample predictions eqs.(2)and (4)in Table 3(as summarized in Table 4).The circle size is proportional to 2005–07public debt/tax base.The prediction error is the actual CDS divided by the predicted CDS;when >1,there is a signal of under-prediction (i.e.the sovereign default risk is over-priced).This ?gure plots 2008–10prediction errors on CDS 5-year tenor on the vertical axis.The 2005–07prediction errors are on the horizontal axis.
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current economic conditions.However,a comparison with other country groupings,outside of the Middle Income countries,provides less support for relative over-pricing of risk in SWEAP.In particular,other OECD countries (outside of the Eurozone)and non-SWEAP Eurozone countries have also experienced very high CDS premiums d much higher than model predictions d during the crisis period.The early 2012selective default on Greek sovereign bonds,recognized as a “credit event ”and triggering payments of CDS,suggests that “mispricing ”was not the issue but rather expectations of a high likelihood of future deterioration in fundamentals triggering a default.
5.Conclusion
We develop a model of pricing of sovereign risk for a large number of countries within and outside of Europe,before and after the global ?nancial crisis,based on ?scal space and other economic fundamentals.We use this model to explain CDS spreads and determine whether the market pricing of risk is comparable in the affected European countries and elsewhere in the world.By this methodology and using out-of-sample predictions,we determine whether there are systematically large prediction errors for the CDS spreads during the global ?nancial crisis period 2008–10and especially 2010when the sovereign debt crisis in Europe surfaced.
We ?nd that market-priced risk of sovereign debt as measured by CDS spreads is partly explained by ?scal space and other economic determinants.Fiscal space is an economically important and robust predictor of CDS spreads using a data set of more than ?fty countries over 2005–10,measured either by government debt/tax base or government de ?cits/tax base.In addition to validating that ?scal space is an important determinant of market-based sovereign risk,we ?nd evidence of large prediction errors in pricing SWEAP risk given current ?scal space and other current fundamentals:unpredicted low CDS in tranquil period and unpredicted high during global crisis period,especially 2010when sovereign debt crisis swept over Euro area.We also matched the SWEAP with 5middle-income countries outside Europe that were closest in terms of ?scal space (debt/tax)during the crisis period.We ?nd that SWEAP default risk is priced much higher than the “matched ”countries in 2010,even allowing for differentials in ?scal space and other fundamentals.
On the other hand,outside of the Middle Income countries,several country groups experienced similarly large prediction errors for CDS spreads during the crisis period,though the dynamics are different than with SWEAP.Other OECD countries tended to have very high prediction errors d high CDS spreads compared with model predictions d during the ?rst year of the international ?nancial and liquidity crisis (2008),while SWEAP experienced very substantial prediction errors in 2010when the sovereign debt crisis initially swept over Europe.
One interpretation of these ?ndings is that the CDS market is pricing default risk not primarily on current fundamentals but future fundamentals,expecting the SWEAP ?scal space to deteriorate markedly.This interpretation is consistent with the selective default on Greek sovereign bonds in early 2012.The adjustment challenges of the SWEAP may be viewed as economically and politically more dif ?cult due to exchange rate in ?exibility associated with participation in the Euro area that is not a constraint in the matched group of the middle income countries.
An alternative explanation,consistent with the multiple equilibrium model of Calvo (1988)and the Euro Area picture painted by De Grauwe and Ji (2012),is that market-priced risk of sovereign default follows waves of contagion,overreacting and mispricing risk of sovereign default over a period of several years.In this view,sovereign risk was initially underpriced as contagious optimism,somewhat divorced from fundamentals,predominated market expectations in the Euro Area,especially at the time of the 10-year celebration of the success of the Euro Area.However,the “good ”(optimistic)expectational equilibrium quickly switched to a “bad ”(pessimistic)expectational equilibrium in the SWEAP countries in 2010as markets overreacted to ?scal deterioration and generated extraordinarily high CDS.Accordingly,persistent pessimism,manifested in high risk permia,induces further deteri-orations of the balance sheet of a country,leading a self-ful ?lling prophecy crisis.16
16
See also Obstfeld (1996)and Diamond and Dybvig (1983)for models of multiple equilibria.
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J.Aizenman et al./Journal of International Money and Finance34(2013)37–5955 Both of these explanations are consistent with our empirical results and both have similar policy implications.In particular,enforceable and binding rules on?scal policy,perhaps combined with greater centralization of Euro Area counter-cyclical?scal responsibilities,would counter either market expectations of future deterioration of future fundamentals or a switch from an“optimistic”to a?scally unsustainable“pessimistic”equilibrium.
Appendix A.Data sources
Sovereign CDS spreads The CDS pricing is based on London closing values collected from a consortium of over
thirty swap market participants.
CDS are denominated in US$,except for the following in Euro:ct,cz,dk,hn,ic,ln,mr,
po,rm,sx,sj,es,sd,tk,and ur.
Source:CMA Datavision.
Tax base The ratio of tax divided by GDP,averaged over the period of previous?ve years to
account for business cycle?uctuations.
Source:World Bank’s WDI,IMF Article IV Consultation documents,OECD,
and Eurostat.
Fiscal balance/tax base The ratio of?scal balance/GDP divided by tax base;positive?scal balance means
?scal surplus.
Source:World Bank’s WDI,IMF Article IV Consultation documents,and Economist
Intelligence Unit(EIU).
Structural balance/tax base Adjusted for nonstructural elements,including temporary?nancial sector,asset
price movements,revenue or expenditure items.
Source:IMF World Economic Outlook.
Public debt/tax base The ratio of public debt/GDP divided by tax base.
Source:IMF Fiscal Affairs Department,Article IV Consultation documents,
and World Economic Outlook.
Trade/GDP The ratio of(exportstimports)divided by GDP.
Source:WDI and EIU.
In?ation Annual consumer price in?ation(%).
Source:WDI and EIU.
External debt/GDP The ratio of total private and public external debt divided by GDP.
Source:WDI and EIU.
Nominal depreciation Annual depreciation of local currency against US$(%).
Source:WDI and EIU.
REER Real effective exchange rate(2005?100).
Source:WDI.
Foreign reserves/GDP The ratio of foreign reserves divided by GDP.
Source:WDI and EIU.
Real GDP growth Annual growth of real GDP.
Source:WDI and EIU.
GDP per capita Real GDP per capita in US$.
Source:WDI and EIU.
TED spread The difference between the3-month US$LIBOR and the3-month US
treasury bill(%).
Source:Datastream.
欧洲债务危机的影响及三大启示(张昀；5月27日) 文章作者：张昀作者单位：央行上海总部文章出处：《上海证券报》随着欧元区主权债务危机的不断蔓延，欧元区国家主权债务问题，已经当前成为全球热门话题。笔者认为，欧洲主权债务危机，对欧元区经济及全球金融稳定和经济复苏有四大不利影响。 第一，影响欧元币值稳定。受希腊等国家面临的主权债务问题影响，欧元汇率从2009年12月起一路下跌，至2010年4月27日，欧元兑美元汇率报收一年来的最低点，较2009年12月初下跌了12.8％。如果不能够有效解决希腊等国家的债务问题，市场信心难以恢复，会进一步打压欧元汇率。国际社会普遍认为，欧元已经被希腊等国的债务问题拖入自诞生以来的最困难时期。 第二，拖累欧元区经济发展。希腊、西班牙、葡萄牙等国采取的激进财政紧缩政策，可能使其经济重新陷入衰退。根据IMF最新预估，2010年，希腊、西班牙、爱尔兰和葡萄牙四国实际GDP增长率为-2％、-0.4％、-1.5％和0.3％，是欧元区成员国中表现最差的几个国家。目前，尽管主权债务问题集中在希腊一国，但也存在继续扩散至其他欧元区国家的风险，标普公司在4月27日、28日接连降低希腊、葡萄牙和西班牙的主权信用评级更是助长了这种恐慌。 第三，延长欧元区宽松货币政策的时间。作为单一货币区，欧元区内部经济失衡给执行统一货币政策制造了障碍，欧元区各成员国经济复苏的步调不一致，使欧央行在实施宽松货币政策“退出”策略时很难确定一份适合所有成员的时间表。而希腊和其他一些欧元区国家暴露出来的债务问题，拖累了欧元区经济复苏的步伐，也使欧洲央行可能不得不在更长的时间内把基准利率维持在历史低点。 第四，威胁全球经济金融稳定。IMF在4月20日和21日先后发布的《全球金融稳定报告》和《世界经济展望报告》中，都表达了对发达国家主权债务风险的担忧。IMF在《世界经济展望报告》中说，短期内的主要风险是，如果不加控制，市场对希腊主权债券流动性和偿还能力的担忧，可能会演变成一次充分的主权债务危机，并形成某种蔓延之势。 IMF在《全球金融稳定报告》中指出，到2010年年底，全球政府的负债额将高达37万亿美元，到2011年则将突破45万亿美元。发达国家如果不能充分改善金融业及个人家庭资产负债状况，债务问题恶化将影响银行系统并波及其他国家，削弱全球金融系统的稳定，可能导致全球金融危机新阶段的开始。 尽管在希腊债务危机初期，由于危机救助机制的缺乏而使危机有加重趋势，但从最新公布的7500亿欧元的救助机制和欧洲国家领导人的承诺等来看，危机可以限定在少数几个经济规模较小、影响较小的国家，并不会大规模蔓延而使全球经济二次探底，也不会导致欧元区的分崩离析，
欧洲债务危机分析论文 希腊主权债务危机自xx年末起，经多轮谈判协商，历程艰难。 那么希腊危机根源何在？ 宏观失衡是一个重要因素。作为欧盟成员国中规模较小、较不富裕的国家，1999-xx年间希腊GDP年均增长率为3.9%，高于欧盟平均水平。然而希腊经济过度依赖消费，同一时期经济增长对消费依赖程度平均为90.4%，导致外部逆差扩大、政府债台高筑。过度债务容易导致违约和危机。 简言之，希腊处于一个困境：债权人知道，希腊缺乏信誉度，不可能以其可负担的利率借到资金。希腊仍将依赖数量越来越大的官方融资。而这又创造了一个更深的陷阱。 爱尔兰是欧元区第二个接受救助的国家。 __之前，爱尔兰吸引了大量的资本流入，带来了国内经济的繁荣，但全球 __使得资本流入嘎然而止，银行坏账累积。爱尔兰政府在xx年9月为银行债务提供了担保。但由于经济陷入衰退，加上纾困银行，爱尔兰政府的财政成本上升，爆发了公共债务危机。
如果没有外部融资，爱尔兰政府将会违约。但欧盟提供了救助，爱尔兰的债务水平近一步上升。 xx年3月份，葡萄牙成了第三个申请资金援助的欧元区国家。 3月份，葡萄牙以将近6%的利率借入10亿欧元的一年期贷款（作为比较，德国能以1.3%的利率借到同样的贷款）；五年期贷款利率 则高达10%。只有一个经济高速增长的国家才能负担这么高的利率。但葡萄牙的经济增长在欧元区极为迟缓,。不景气的经济、沉重的利 息支出、艰巨的财政调整措施，都让葡萄牙举步维艰。 纾困的困境： ①由于希腊，爱尔兰和葡萄牙无力偿付过高的债务，人们建立 了一种机制，为它们提供偿债所需融资。按照设想，提供这种融资是为了换取它们采取相关措施，让它们的债务负担在未来变得可持续。但用来解决欧洲外围国家债务问题的这种模式，本身就提高了它们的债务水平。按照人们的设想，流动性问题的出现只是暂时的，官方融资能够帮助相关国家进行改革，让它们能够以正常状态回归自愿市场。但事实是，第一个接受救助的希腊，恢复的状况远不及预期，现在必须有新增资金不断流入，目前的局面才可以继续下去。只有得到公共资金的支持，外围国家的债务才可以无限制地增长下去。
风险控制案例分析Revised on November 25, 2020
风险控制案例讲座 主讲：王士俊先生 一、风险概述 风险是关乎金融企业生死存亡的关键。那么，什么是风险呢不确定性就是风险，看不清就是风险。而时间是检验风险的最严格的审判官，风险管理必须要经历得起时间和经济周期的考验。 王行长主管过信贷、财务、安全保卫等多个银行部门，认为银行在两项工作上不能高调。一是资产质量。这十几年来，我们的银行尚未真正经历过经济周期的考验，即便08年的金融危机也因为我们的有限开放而没有造成很大的影响，所以资产质量的问题还远未经受考验。比如在周正毅案件中，当时王行长所在的上海分行表现出色，招行总行欲进行表彰，王行长却要求只陈述事实而不介绍经验，保持低调。二是安全保卫。我们只能说尚未发现安全事故，却不能说能够杜绝它的发生。比如93年交行分行长会议上，北京分行介绍安保的成功经验，可不久便发生大案，分行长心急如焚，突然去世。所以说：没有发现，不等于没有风险。 在本世纪初，花旗银行曾开发了一套风险组合管理工具，似乎可以取代人的管理和判断，但实践证明，再先进的模型和工具也不能取代人的作用，特别是审贷。花旗历史上也经历过数次差点倒闭的危机。比如，上世纪七十年代起，石油开采进入高峰期，石油输出国积累了大量的石油资本，资金需要出路，于是投向华尔街。华尔街的银行家们设计了无数的金融工具，并把这些资金投向拉美等地区。当拉美爆发债务危机后，美国银行业遭遇巨大打击。又
如，美国在80年代以前推行（存贷）固定利率，当利率市场化后，存款利率上升而原来固定的长期的低息贷款却不能调整，很多银行因此倒闭，花旗的股价也降低到十美分一股。包括08年的次贷危机中，若不是政府出资救助，花旗也可能倒下。所以说，银行的风险平时看不出来，只有在经历突发事件中才能看出平时的理念和实践如何。 因此，风险管理能力是一家金融机构可持续发展的关键。 我们万丰融资租赁公司和银行大同小异，通过经营风险获取收益，但融资租赁公司经营成本更高，客户质量不如银行好，期限相对更长，风险肯定比银行高，所以在风险管理上要比银行更努力、更审慎、做的更好。 王行长有四句话希望和大家共勉。一是对公司负责，这是天经地义的；二是对未来负责，做金融不能追求短期行为，如民生银行的高发展、高压力、高激励和业务私有化很容易导致短期行为而出问题，于是他们通过采取授信独立审批、支行会计主管由分行派驻不受支行约束等措施，有效保证了不出和少出大问题；三是对员工负责，这是一名管理者的职责和义务，要对得起员工的家庭和个人；四是对自己负责，重视自己的声誉，长期坚持自律，打造个人品牌。 风控的最高境界是问心无愧。公司评审部员工在风控岗责任大，业务部和评审部要相互配合，做好业务和风险的平衡。评审部要充分理解业务部门市场开拓的不容易，业务部门也要在拓展业务项目时认真识别风险，让项目能经受住时间的考验。 二、案例分析 王行长介绍了一些具体的案例，来揭示项目在实际运作中的风险。
美国债务危机对全球经济的影响 一、美国债务危机对全球经济的影响： 1、增速放缓，风险增加 国际货币基金组织6月发布的《世界经济展望报告》下调今年世界经济增长预期至4.3%，比该组织4月份的预测低0.1个百分点。2008年后，得益于各国政府的财政和货币刺激政策，世界经济在较短时间内走向复苏。刺激政策一方面使金融市场领先于实体经济出现“V”形反转；另一方面，各国央行大规模释放流动性，推动大宗商品，尤其是黄金、石油的价格一路攀升，在全球范围造成了事实上的通胀压力，成为世界经济的现实威胁。 2、美国经济有可能出现再次衰退 彭博社7月24日预计，美国二季度国内生产总值可能仅增长1.8%，低于一季度的1.9%。而且，美国目前房产销售下滑，消费者信心低落，经济扩张的步伐已经放缓至1年以来的新低。美国经济学者戈德伯格在接受中新社记者采访时说，目前出台的美债法案所起到的作用仅仅是令美国勉强渡过了眼下的债务危机，充其量只是民主、共和两党的政治恶斗告一段落。法案对美国经济的影响才刚刚开始。 3、欧元区经济和日本前景黯淡 欧元地区经济增长已经降至两年来新低。而日本在遭遇大地震、海啸以及核泄漏事故的冲击之后，经济复苏前景更加暗淡。 4、新型经济体遭受通胀风险 作为本轮世界经济复苏的引擎，新兴经济体普遍面临不断升温的通胀风险。为了抵御通胀，大部分新兴经济体不断加息或采取其他紧缩政策给经济降温，这在一定程度上限制了其经济的强劲复苏。 二、对中国经济的影响 1、外储经营压力有所增加 作为全球最大外汇储备国，目前我国拥有着3.2万亿美元的巨额外汇储备。在这些资产中，70%以上为美元资产。此次美国主权信用评级的下调，给中国外汇储备管理带来一定压力。降级之后，可能会带来投资者对美债的抛售行为，不排除美元贬值的可能，这可能会让中国外汇储备遭遇一定的损失。 2、引发热钱压境之忧 由于此次降级是美国主权信用评级的“史上首次”，因此其给全球金融市场带来的震荡不言而喻。这一事件将引发全球金融市场掀起一股“震荡波”，同时也将加快国际游资冲击新兴市场的步伐。相比于欧美等国，由于中国等新兴市场国家经济持续高增长，利率水平较高，汇率升值空间大，因此中国市场可能会成为部分国际游资的觊觎之地。 3、输入性通胀压力上升 美国长期主权信用评级的下调，会带来美元走势继续疲软，人民币会面临一定升值压力。由于美元是全球最主要贸易结算货币，美元的贬值将导致以美元计价的大宗商品价格持续上涨，这对进口大量原材料的我国来说，输入性通胀压力将有所增加。 4、我国货币政策操作空间受制约 美国长期主权信用评级的下调，给全球经济的复苏增添了更多不确定性，一定程度上制约着我国货币政策的操作空间。美国主权信用评级的下调，令央行加息前景充满了不确定性 三、中国的对策 连日来，美国国债危机、美股大跌、美国主权信用被降级等连串事件不断发酵，美国经济一片“愁云惨雾”，引来老百姓诸多疑问：这对中国经济影响几何？如何应对？7日，接受羊城晚报记者采访的广东专家认为，这意味着中国经济加快转型升级迫在眉睫，值得重点关注。 1.被通胀压力增大，拖累股市 长期以来，美国经济称霸全球，美元是事实的国际货币，美国国债则是全球债券市场最主要的投资交易品种。近日很多分析认为，这次事件对全球第二大经济体中国的潜在影响不言而喻，很多与老百姓关系还相当“贴身”： ———外汇储备可能缩水。数据显示，今年上半年，我国外汇储备逼近3.2万亿美元，当中约有1.16万亿美元是持有美国国债。有分析认为，美国再次提高了债务上限，意味着将继续维持宽松的货币政策，美元走势将继续走弱；而主权信用遭降级，投资者减持美债意欲增大，将进一步冲击美元，直接后果是会使我国外汇储备缩水。 ———“被通胀”压力增大。国家工信部数据显示，上半年我国以美元计价的大宗商品如原油、铁矿砂等进口均价大幅上升。长远看美元将走弱，大宗商品价格很可能暴涨，我国输入性通胀将更为突出。 ———出口增长可能受阻。由于全球经济复苏缓慢，海关数据显示，上半年我国外贸增速呈明显回落趋势。有分析认为，一方面，美国信用评级下调将增大融资成本，打击投资者信心，美国经济可能“二次探底”；另一方面，美国国债市场大幅波动，将影响国际金融体系稳定，影响全球经济，进而影响中国出口。 ———股市受拖累。上周美国股市经历了金融危机以来最大一次重挫，三大股指跌幅均超5%，中国股市也不能幸免，周五沪指下跌2.15%，深成指下跌1.95%。市场预计，评级下降的影响周一后还会逐步显现，可能继续拖累全球股市。 不过，也有中山大学岭南学院教授王曦等专家认为，这次更多的是信心问题，实际影响还有待观察。 2.外汇储备的格局，短期难改 目前更受关注的是，如何减少这些外围因素对中国经济的影响。 AA＋评级，现在美国国债信用水平还不如英国、德国、法国、加拿大，最直接的问题，是如何减少其对我国巨额外汇储备的影响。对此各界已有共识，央行行长周小川此前就表示，我国外汇储备管理将继续坚持多元化投资原则，加强风险管理。事实上，今年上半年我国外汇储备同比大增30%，但前5个月却减持了3亿美元美国国债。 广东经济学会副会长、暨南大学教授郑英隆认为，更重要的启示，是中国经济加快转型升级迫在眉睫。中国长期依靠出口拉动经济，形成了巨额外汇储备，既存在突出的保值增值问题，也造成了外汇占款形成的人民币基础货币膨胀。美国国债信用下降，但全球却找不到同样容量和信用的国债市场；美国经济数据不佳，但欧洲债务危机更加严重；发达国家复苏缓慢，但新兴经济体也存在通胀、经济结构不合理等问题。因此，要短期改变我国外汇储备格局不容易。
我国商业银行国别风险防范研究 【摘要】：在经济金融全球化背景下，由于金融衍生产品的放大效应，2007年爆发的美国次贷危机迅速扩散开来成为全球金融危机。各国大规模的经济拯救计划暂时缓和了危机，但是由于大规模政府债券的发行，使得很多国家面临着低迷的经济及庞大的债务双重困境，这就加大了主权违约风险。金融危机通过全球金融体系系统性风险渠道、国际资本流动渠道及国际贸易渠道使得众多国家主权信用风险上升，主权信用评级屡遭下调。例如欧洲一些国家出现的主权信用危机的实质就是这些西方发达国家面临的庞大的债务危机，欧元也因此处于一个岌岌可危的状态。一些国家金融体系存在固有结构性问题、经济过度开放、经济结构中存在脆弱性因素以及经济及政治上的弱点使其更易于吸收危机的能力更弱，更易于暴露于危急之下从而扩大主权信用风险。本文从商业银行国别风险层面入手，首先阐述了国别风险的相关概念与理论；其次，分析国外商业银行对国别风险的防范方法，并对上世纪九十年代以来影响范围较大的几次金融危机进行分析，以此作为我国商业银行开展国别风险防范研究的借鉴；然后对我国的国别风险现状做较清晰的认识；最后文章最后提出了加强我国商业银行跨国经营国别风险管理的政策建议，设定国别风险信贷限额，加强国别风险的外部监督环境，从而有效避免国别风险。【关键词】：商业银行国别风险评估防范 【学位授予单位】：山西财经大学
【学位级别】：硕士 【学位授予年份】：2013 【分类号】：F832.33 【目录】：摘要6-7Abstract7-101导论10-141.1研究背景和意义10-111.2国内外文献综述11-121.3研究思路及研究方法12-131.3.1研究思路12-131.3.2研究方法131.4论文框架13-142国别风险研究的理论基础14-182.1国别风险的定义142.2国别风险的产生因素14-152.3国别风险的类型15-162.3.1主权风险152.3.2转移风险152.3.3货币风险152.3.4宏观经济风险15-162.3.5间接国别风险162.3.6政治风险162.4国别风险的特点16-183国别风险对我国商业银行的影响18-223.1对我国商业银行经营环境的影响18-203.1.1国际环境18-193.1.2国内环境19-203.2对我国银行业经营理念的影响20-223.2.1对我国银行业跨国业务的影响20-213.2.2对我国银行业监管结构的影响213.2.3对我国银行业员工素质的影响21-224国外商业银行对国别风险防范的实践及启示22-314.12011年全球国别风险分析22-254.1.1北美22-234.1.2欧洲234.1.3亚洲23-244.1.4非洲244.1.5拉丁美洲24-254.2美国商业银行国别风险防范的实践25-264.3瑞士银行国别风险防范模式26-274.4国外商业银行国别危机的防范对我国的启示27-314.4.1国别风险评估284.4.2国别风险预警28-294.4.3国别风险规避29-304.4.4国别风险善后30-315我国商业银行对国别
债务危机拖累经济衰退 本报记者梁婷婷北京报道 11月14、15两日，欧元区多个国家GDP统计数据悉数“亮相”，欧洲经济形势继续不容乐观。 糟糕的成绩单 根据法国统计及经济研究局公布的数据显示，在经历了第一和第二季度连续0.1％的负增长后，法国第三季度经济呈现稍许好转迹象，国内生产总值（GDP）季率初值上升至0.2％。其中，家庭消费支出从第二季度0.2％的负增长恢复至0.3％的正增长，反弹较为明显。 然而，除了法国传来稍许利好外，欧洲其他多国的GDP统计数据却着实让人担忧。 德国联邦统计局统计数据显示，虽然得益于国外持续需求所贡献的小幅贸易顺差，德国第三季度GDP勉强得以维持0.2％的增长，但从第一季度的0.5%，到第二季度的0.3％，再到第三季的0.2％，德国经济增速已经显现出了明显的放缓趋势。 意大利国家统计局随后也公布了统计结果。数据显示，意大利第三季度GDP继续延续了自2011年第三季度以来的负增长局面，季率初值下降0.2%，同比萎缩2.4％。 西班牙国家统计局公布的统计结果也颇为负面。数据显示，西班牙第三季度GDP季率初值为-0.3％，同比缩水1.6个百分点。 而于这几大经济体前一天发布第三季度GDP数据的希腊，也继续扩大着走衰态势。希腊官方统计机构ELSTAT的数据显示，希腊第三季度GDP继续萎缩7.2个百分点，经济衰退较第二季度的-6.8％愈加明显。 如果说，欧洲这几大经济体公布的GDP相关数据只是局部地交代了欧洲经济第三季度下行的现状。那么，最后由欧元区官方统计机构Eurostat公布的欧元区17国第三季度GDP
季率初值，则是彻底清晰地勾勒出了一路下行的欧洲第三季度经济全貌。数据显示，继第二季度0.2％的负增长之后，欧元区第三季度GDP继续萎缩0.1个百分点，仍然步履蹒跚地行进在经济衰退的道路上。 按照广义的经济衰退定义，即连续两个季度GDP的萎缩，在过去的7－9月份，欧元区国家经济已经滑入了自2009年金融危机以来的第二个衰退期。 受GDP下行影响，11月15日，除西班牙IBEX35指数上涨0.47％之外，欧元区各主要经济体股市走低。德国DAX指数下跌54.06点，跌幅0.82%；法国巴黎CAC指数下跌17.62点，跌幅0.52%；意大利FTMIB指数下跌56.91点，跌幅0.37％；希腊ASE指数下跌6.66点，跌幅0.84%。 继续看衰 欧盟委员会今年早些时候曾发布了一份《春季经济展望报告》，对欧洲2012年和2013年经济形势做出预期。报告预测，欧盟国家和欧元区国家2012年全年经济增长率将分别为0％和-0.3％，2013年为1.3％和1.0％。 然而，就在11月15日欧洲多国GDP统计数据公布的前一个星期，欧盟委员会发布了其继春季报告之后的《秋季经济展望报告》。报告中，欧盟委员会下调了对欧洲经济的预期。其中，报告分别下调欧盟国家和欧元区17国2012年全年经济增长预期至-0.3％和-0.4％，下调欧盟国家和欧元区17国2013年全年经济增长预期为0.4％和0.1％。如果说欧盟委员会在今年春季对欧洲经济衰退程度的判断还略显“轻松”，那么，在经过近一年的观察后，欧盟委员会对于接下来的欧洲经济态势不得不秉持更为保守的态度。 “欧洲正在经历一个宏观经济再平衡的艰难过程，这一过程还将持续一段时间。”欧盟经济暨货币事务执委雷恩在秋季经济报告的新闻发布会上表示。
专业：物流管理1101班 姓名：袁天祥 学号：201124141 欧洲债务危机的成因及其影响分析 作为一名外行人看‘欧洲债务危机的成因及其影响分析’，刚看到这份作业倍感压力，但经过有限时间学习之后渐渐地对欧债危机事件略知皮毛。长话短说，让我们直接进入文章主题——欧洲债务危机的成因及其影响分析。 在了解欧洲债务危机之前，先向读者们介绍一下主权债务这个名词，主权债务是指一国以自己的主权为担保向外，不管是向国际货币基金组织还是向世界银行，还是向其他国家借来的债务。现在很多国家，随着救市规模不断的扩大，债务的比重也在大幅度的增加。当这个危机爆发到一定阶段的时候可能会出现主权违约，当一国不能偿付其主权债务时发生的违约 那么整天在财经频道或网络出现的欧洲债务危机新闻，那么到底什么是欧洲债务危机呢？在这里可以向大家简单的阐述一下，简单点讲，就是欧洲许多国家的债务都要到期了，但它们拿不出那么多钱还债，就像企业的资金链要断了！当一个国家的收入无法偿还到期本息就会破产，欧猪五国都到了边缘，而且因为如果一国破产违约其他国家持有的该国债券无法收入，形成连锁反应，纷纷破产，所以称为危机。。欧债危机是由于欧元区一些国家，比如希腊，还有西班牙，葡萄牙，意大利等，政府面临着巨额的债务，这些债务是政府发行了大量的债券融资来应付财政支出，本来在发债时根据预测是能按时还本付息的，但是受到经济危机的冲击，各国经济疲软，失业率激增，政府收入减少，支出增加，导致政府无力偿还债务。爆发了债务危机。危机拖累了欧盟成员，因为，欧盟内如法德，需不断想这些国家注资，以保住欧元，这些救援国流动性减少，国内经济也受连累。整个欧元曲陷入危机。就是政府举债经营失败。一国财政支出是有一定底线需求的，政府公务、社会保障、卫生医疗、基础设施维护等等，但财政收入却来自商品市场主体的经营成果中的税收分割，如果一国市场主体在共同市场中因为技术、成本控制、销售、售后服务、经营管理等方面处于劣势，国家财政收入就失去保障，被迫赤字运行，运行期间国家市场主体在共同市场的竞争力得不到相对优势的提升，国家财政收入得不到改善，最终导致政府债务危机。这是不同生产力水平国家采取共同市场的必然结果。就如同在我们中国国内，如果没有中央政府对落后省份政府的财政转移支付，落后省份不但市场主体被兼并、挤垮，而且地方政府正常财政支出也无法应付一样的道理。共同市场，要求对落后区域的政府公务、社会保障、卫生医疗、教育环保等方面必须予以转移支付的支持，否则共同市场的事实，对落后区域是不堪重负的，因为财富向先进区域流动的太多太快，而非共同市场国家之间要好一些，毕竟汇率、进出口管制还可以对落后生产力国家有一定的自我保护。
万方数据
成员国处于景气的经济周期，各国都能遵守务约维持较好的赤字水 平和债务水平； ２．随着９．１１美国的经济急剧减速，欧洲经济也开始转寒，２０００年后为抑制经济滑坡和老龄化危机，欧元区乃至欧盟国家都纷纷违约，通过扩大政府支出减税来刺激经济复苏，处于欧元区经济核心地位的德国和法国的赤字率（如表１所示）也连续三年持高不下，最终通过德国、法国的政治博弈，违约惩罚暂缓。许多小国也纷纷“搭 便车”避开惩罚继续通过赤字来扩大支出，避开惩罚后各国更加主动通过债务和赤字来刺激消费和投资，进一步造成财务状况恶化。 表１２００２—２００５年德国、法国赤字率 ２００２（％）２００３（％）２００４（％）２００５（％）德国３．７４．０３．７３．３ 法国３．２４．２３．７２９ 数据来源：裘元伦．欧元区财政状况恶化及前景分析．国际金融研究 长久以来政策搭配的极度失衡，在２００８年金融危机的强烈影响下，进一步加剧了成员国政府的激进式财政扩张，财政赤字和债务比例急速上升，最后融资困难无法还债弥补赤字，酿成偿付危机。 （三）欧元区成员国资质：经济结构、财务状况差异大 １．加入欧元区时：标准放宽，导致成员国的财务状况有好有坏。虽然在对成员国的准入标准规定中做出了严格的比例控制，但妈约》中模糊词汇以及事实表明欧元区成员国中有极大部分国家在被批准加入欧元区时，并没有真正达到马约的趋同性标准，这里就包括当初粉饰自己的财务“挤”入欧元区，而最近已经爆发主权债务危机的希腊。 ２．加入欧元区后：标准松弛。惩罚机制没有落实，成员国牺牲主权利益大规模赤字，比加入前更甚，同时影响欧元区整体经济。在（＿－）的分析中得知，欧元区各国的激进式财政扩张最终在经济滑坡中融资失败，导致资不抵债，偿付危机出现。说明欧元区成员国早已违背忪约》而迟迟未受惩罚，其赤字率和债务率早已严重超标，下面的两组数据分别是欧元区数据对比图３所示： 图３欧洲主要国家财政状况 数据来源：根据欧盟执行委员会２００９年秋季预测整理 作为欧元区国家，德国是欧元区第一大经济体，他们的赤字率和债务率都已超出托马约》规定。而在对０９年秋的比率预测中意大利的债务比率最高，希腊紧随其后。 ３．债务危机来临时：重债国有违约风险，与同盟国和国际货币组织进行利益博弈。以取得政治经济援助；债务健康国隔岸观火，迟迟无法达成一致，拖延产生的时间成本加重了危机蔓延和损失，直接导致博弈双方将来的偿付成本更大。危机更严重。 三、欧洲债务危机的启示 欧洲债务危机一方面严重挫伤了欧盟国家的经济，使其从金融危机后复苏的速度放慢，同时也使全球经济复苏产生负面影响。通过分析看出，欧元区相对于最优货币区理论中的描述而言，尚显稚嫩，此次主权债务危机的根源在于整个货币区仍缺乏一种有效的对称性政策搭配来平衡成员国内外经济均衡。但如今，并非仅有欧洲国家有主权债务问题，全球相当多的经济体都在此次主权债务危机中暴露出其自身的或多或少的财务问题，所以说，主权债务危机是一个帮助各个经济体重新认识自身政策制定、反省自身财务状况的好时机。 （一）对亚洲的启示 １．审慎选择货币联盟的形式。亚洲经济不同于欧洲、美洲区，亚洲的经济中，国家地位两极分化严重：日本作为发达国家，其ＧＤＰ主导整个亚洲ＧＤＰ的三分之二，但从９０年代开始其经济泡沫后一直未有回升的转机，且日本的右翼政治令亚洲乃至国际对其认可度下降；中国作为世界最大的出口国之一，人民币境外流通数量已经以千亿记，但中国仍是发展中国家，人民币的经济实力仍有待提高，且国家“一国多币”、香港的国际自由港地位及其货币局制度使人民币境地尴尬ｔ亚洲其他经济体规模较小，影响力甚微。因此，要实现亚洲货币一体化，首先要解决亚洲国家间经济发展的极度失衡，或者将人民币与中国其他可兑换币种进行全方位统一、整合．为货币区域化或单一货币联盟打基础。 ２．努力平衡亚洲各国的经济发展。亚洲国家在产业结构、货币制度、收入结构等方面仍有较大差异，减少差异制造趋同性才有货币合作的可能，这就需要亚洲各国在进行高度的货币联盟前利用亚太经合组织高度互通经济，实现资本的自由流动，为经济融合打下基础，才有可能实现货币一体。 （二）对中国的启示 我国，乃至亚洲，离欧洲的主权债务危机都有一定距离，但是，关注欧洲债务危机，对我国的经济发展有经验性的启示。 首先，欧洲债务危机短期内将转移国际对人民币的升值的关注。 其次。作为从０９年开始超越德国跃居世界第一出口大国的中国，要审慎对比经济结构，关注自身债务结构，尤其是“临时性负债”变坏张呆账的风险。 最后，欧洲财政扩张的过度对中国激进式的宏观经济政策有经验性警告。中国长期以来实施相对宽松的、积极的货币、财政政策，这一方面奠定了其在国际的出口大国地位，另一方面也为众多投机性需求、过剩产能的创生孕育了温床，尽管中央赤字率、债务率一直低于国际警戒线，但地方财政的赤字率、债务占比是非常高的，这与欧元区成员国的债务情况何其相似：尽管地方财政赤字可以由中央政府买单，但最终会反映在货币扩张和财政扩张上，最终很可能造成本币对内急速贬值，引发衰退。 参考文献 …樊勇明．西方国际政治经济学．上海人民出版社．２００６．８（２）【２】中国社会科学院经济研究所．政治经济学研究报告卜经济全球化的政治经济学分析．社会科学文献出版社．２００１．４．１【３】张继民．美国对华贸易政策的决定——政治经济视角下的均衡．复旦大学出版社．２００９．１（１） 【４】宋世方．国际经贸政策合作的政治经济分析．经济科学出版社．２００７．８（１） 【５】李雪．基于实验经济学的金融危机模型研究综述．商场现代化．２００９．７（上旬刊） 【６】周建平．欧洲一体化政治经济学．复旦大学出版社．２００２．７（１） 吲王亚娟．希腊连遭降级酝酿新一轮主权债务危机．世界知识．２０１０．１ 【８】姜波克，罗德志．最优货币区理论综述兼欧元、亚元问题．世界经济文汇．２００２（１） ｆ９】张锐．希腊主权债务阴霾或将危及欧元区生存．金融经济．２０ｌＯ（３） 【１０】朱芳．最优货币区理论：欧元的启示与东亚货币区的设想．南方金融．２００３（７） 【１１】曲谐．主权债务危机．从迪拜波及欧洲．沪港经济．２０１０（３） 【１２】李迅雷，吕春燕．欧洲经济缓慢上升．主权债务不触发危机．－一２０１０年欧洲经济展望．国泰君安证券宏观研究．２０１０．３（３）【１３】李东荣．关于主权债务危机的若干思考．中国金融．２０１０（５） 【１４】范庆华．如何保护债务重组的国际安排．世界知识．２００２年（２３） 【１５】郭砚莉汤吉君．金融危机的一种存量分析．重庆工商大学学报（西部论坛）．２００９．７（４） 【１６】【美】ＭＩｕｌｄｅｕ．Ｒ＿Ａ．蒙代尔经济学文集第五卷：汇率与最优货币区．中国金融出版社．２００３．１０（１） 【１７】【美ＵｏｈｎＮｅｖｍＫｅｙｎｅｓ，政治经济学的范围与方法．华夏出版社．２００１．１（１） 【１８ｌｆ日】Ｍａｋｏｔ０Ｉｔｏｈ［希】ｃｏｓｔａｓＬａｐａ、，ｉ吼ｓ．货币金融政治经济学——当代西方政治经济学．经济科学出版社．２００１．１２（１）ｆ１９】ＴｏｒＳｔｅｎＰｅｌｓｏｎａ１１ｄＧＩｌｉｄｏＴａｂｅｌｌｉ血ＰｏｌｉｄｃａｌＥｃｏｎｏｒｌｌｉｃｓ：ＥｘｐｌａｉｍｎｇｅｃｏｎｏＩｌｌｉｃｐｏｈｃｙＭ勰ｓａｃｈｕｓｅｔ七ｓＩｎｓ石ｎｌｔｅｏｆＴｅｃｈｎｏｌｏｇｙ２０００【２０Ｊ＾ＪｌｅｎＤｒａｚｅｎ，Ｐｏｈ石ｃａｌＥｃｏｎｏｆｎｙｉｌＩＭａｃｒｏｅｃｏｎｏｒｎｉｃｓ。ＰｒｉｎｃｅｔｏｎＵｎｉｖｅｒｓｉｔｙＰｒｅ骆，２０００ 作者简介：张舒（１９８８一）。女，湖北十堰人，中南财经政法大学金融学院。 Ｔｊｍ∞ＦｉｍＩ∞ｅ １３万方数据
2016年银行从业资格考试知识点《风险管理》：国别风险 国别风险是指由于某一国家或地区经济、政治、社会变化及事件，导致该国家或地区借款人或债务人没有能力或者拒绝偿付商业银行债务，或使商业银行在该国家或地区的商业存在遭受损失，或使商业银行遭受其他损失的风险。 国别风险的概念 国别风险的主要类型包括转移风险、主权风险、传染风险、货币风险、宏观经济风险、政治风险、间接国别风险七类，其中转移风险是国别风险最主要的类型之一。 国别风险管理体系包括以下基本要素：董事会和高级管理层的有效监控；完善的国别风险管理政策和 程序；完善的国别风险识别、计量、监测和控制过程；完善的内部控制和审计。 国别风险的基本做法 1.明确董事会和高级管理层的责任 商业银行董事会承担监控国别风险管理有效性的最终责任。 商业银行高级管理层负责执行董事会批准的国别风险管理政策。 2.建立清晰的国别风险管理政策流程 商业银行应当根据国别风险的特点，识别国别风险并建立国别风险管理政策和流程，一般应当与本机构跨境业务性质、规模和复杂程度相适应。 （1）国别风险识别 商业银行应当充分识别业务经营中面临的潜在国别风险，了解所承担的国别风险类型，确保在单一和并表层面上，按国别识别风险。 （2）国别风险的计量与评估 商业银行应当根据本机构国别风险类型、暴露规模和复杂程度选择适当的计量方法。计量方法应当至少满足以下要求：能够覆盖所有重大风险暴露和不同类型的风险；能够在单一和并表层面按国别计量风险；能够根据有风险转移及无风险转移情况分别计量国别风险。 （3）限额管理、监测与报告 对国别风险应实行限额管理，在综合考虑跨境业务发展战略、国别风险评级和自身风险偏好等因素的基础上，按国别合理设定覆盖表内外项目的国别风险限额。有重大国别风险暴露的商业银行，一般会考虑在总限额下按业务类型、交易对手类型、国别风险类型和期限等设定分类限额。国别风险限额应当经董事会或其授权委员会批准，并传达到相关部门和人员； 国别风险评估的主要指标： 国别风险的评估指标包括三种：数量指标、比例指标、等级指标。
欧洲主权债务危机爆发的原因分析 2009年4月，爱尔兰出现财政困难。10月20日，希腊政府突然宣布，2009年财政赤字占GDP比例将超过12%，是欧盟允许的3%上限的3倍多，其主权债务危机就此浮出水面。11月9日，葡萄牙政府表示2009年财政赤字占GDP 的比例从原来的5.9%上调到8%，也是远高于3%的上限，引起了国际社会的广泛关注。12月8日，惠誉将希腊信贷评级由A-下调至BBB+，前景展望为负面；标准普尔和穆迪也随即宣布下调希腊主权评级。 2010年1月11日，穆迪警告葡萄牙政府，若不采取有效措施控制赤字将调降该国债信评级。之后，整个欧盟都受到了债务危机困扰，包括比利时这些外界认为较稳健的国家，及欧元区内经济实力较强的西班牙，都预报未来三年预算赤字居高不下，导致市场被悲观情绪笼罩，欧元大幅下跌，欧洲股市暴挫。欧洲主权债务危机的爆发固然有全球性金融危机冲击因素，但更重要的还是来自危机国家自身原因。 一、欧洲主权债务危机爆发的深层次原因 欧洲各国发生主权债务危机，从表面上看，是由于政府应对国际经济金融危机而频繁发债，造成财政入不敷出而爆发的“滚雪球”式财政危机，但仔细研究其形成机理，可以发现危机爆发在于诸多更深层次的原因。
（一）对周期性较强产业的过度依赖导致国民经济在危机面前显得异常脆弱 此次欧洲各国希腊、西班牙、爱尔兰等国家过于依赖受危机影响严重的周期性产业。如希腊经济主要靠旅游业、造船业、文化业和农业，其中旅游业占比在20%以上。“国际金融海啸”袭来，经济遭受严重打击，特别是旅游业收入，2009年希腊旅游收入比2008年锐减达12亿欧元之巨。西班牙的支柱产业主要有房地产和旅游业，在2008年国际金融危机爆发后，房地产价格出现大幅下滑，目前房地产空置率居高不下，房地产业风险凸显；其另一支柱产业旅游业，因前几年旅游资源被过度开发，在金融危机强力冲击下迅速萧条，目前一些度假地的酒店入住率还不足50%。爱尔兰是一个以农业为主要产业的国家，国际金融危机对其农产品出口也带来严重冲击。 各国为应对危机，提振经济，纷纷采取大规模的财政刺激措施，加上财政收入锐减，财政赤字和政府债务水平普遍大幅上扬。2009年，上述各国财政赤字占GDP的比重分别达到12.7%、11.4%、11.8%，葡萄牙达到9.3%，就连欧元区以外的英国，其财政赤字占GDP的比重也超过12%。目前，欧盟27国中已有20个国家赤字超标。三国公共债务占国内生产总值的比例估计分别为75.2%、63.7%和59.5%，也均接近或大幅超过欧盟规定的60%的上限。这些国家的不良财政
欧洲主权债务危机的原因及启示 始于美国的次贷危机对全球经济的影响正在逐渐消退，经济复苏迹象越来越明显之时，欧洲的主权债务危机又开始进入人们的视野，并且愈演愈烈且呈蔓延趋势。对欧洲债务危机的原因进行分析及其对我国的启示。 标签：欧洲；主权债务危机；原因；启示 发端于希腊的主权债务危机，已由单一国家演变为欧盟债务危机，进而发展为制约欧洲乃至全球经济复苏的一场债务危机。欧洲主权债务危机的爆发是国际金融危机后果的一种延续，因其产生和发展又带有欧洲的独有特征，成为制约欧洲经济发展的长期痼疾的一种现实表象。目前，欧元区各国正希望通过多方面合作和共同努力来摆脱困境，但步伐却蹒跚和艰难。此次欧洲主权债务危机中期内对我国的直接影响并不大，但危机产生和发展过程中所体现的深层次问题值得我国深思和警醒。 一、主权债务危机的基本概念 主权债务又称政府债务、公共债务，是指一个国家中央政府的债务。财务赤字则是单一财政年度政府支出大于财政收入的差额。因政府的大部分收入来自纳税人，故主权债务间接上是纳税人的债务。主权债务可以分为内债和外债，内债是政府对本国投资者所欠的债务，外债是政府向世界银行等金融机构或他国借来的债务。广义上的主权债务也包括政府未来的养老金支付以及尚未偿付的货物和服务支付〔1〕。主权债务根据债务到期前所经历的持续时间分为短期、中期和长期债务。短期债务是在一年内到期的债务，中期债务时介于短期与长期之间的债务，长期债务是在十年以上的债务。主权债务清算标准是由国际清算银行规定，但主权债务违约则由一系列复杂的随司法管辖区的不同而不同的法律所规定。〔1〕主权债务危机是指一国政府失信、不能及时履行对外债务偿付义务的风险，即主权债务违约风险非常高时就会陷入主权债务危机。一国的主权债务危机甚至会导致“国家破产”，这种破产与企业或部门因不能偿债而破产不同，政府不能停止提供给国民的社会公共服务，所以管理国家主权债务违约需要更加复杂的一套程序和方法。从国际来看，IMF可以应危机国的请求，向其提供暂时的流动性支持，但一般会附加严格的条件，譬如制定一个通胀和经济增长的目标，紧缩货币政策等，这些措施有可能会引发危机国的社会动乱，而且向IMF求助一般被认为是一种对国家管理不善的屈辱。除非万不得已，一般危机国是不会向IMF求助的。 二、欧洲主权债务危机发生的原因 仔细研究欧洲主权债务危机的产生和发展过程发现，此次危机的产生是金融危机冲击、长期的制度性和结构性缺陷等现实因素综合作用的结果。 （一）美国次贷危机是主权债务危机的诱因
银行业专业人员职业资格中级风险管理（国别风险管理）模拟试 卷1 (总分：80.00，做题时间：90分钟) 一、单项选择题(总题数：19，分数：38.00) 1.( )指借款人或债务人由于本国外汇储备不足或外汇管制等原因，无法获得所需外汇以偿还其境外债务的风险。 （分数：2.00） A.转移风险√ B.传染风险 C.货币风险 D.主权风险 解析：解析：转移风险是国别风险的主要类型之一，是指借款人或债务人由于本国外汇储备不足或外汇管制等原因，无法获得所需外汇以偿还其境外债务的风险。 2.下列关于国别风险和主权风险的联系和区别说法不正确的是( )。 （分数：2.00） A.主权风险看风险的角度更宽广，国别风险是主权风险的一部分√ B.国别风险考虑的风险因素更复杂，最基本和最重要的是转移风险 C.一旦出现国别风险，受影响的业务范围更大 D.最基本和最重要的是转移风险 解析：解析：国别风险看风险的角度更宽广，主权风险是国别风险的一部分。 3.某笔1000万美元贷款的借款人在开曼群岛注册成立，经营资产和实际业务均在泰国，主要原材料来自俄罗斯，产品主要销往英国。该笔业务敞口风险主体最终所属国为( )。 （分数：2.00） A.泰国√ B.开曼群岛 C.英国 D.俄罗斯 解析：解析：当直接风险主体是空壳公司或特殊目的公司(SPV)，比如注册在开曼群岛、维京群岛等离岸金融中心或其他金融中心的客户，则其所在地为从事实际经营活动的地区或其管理机构的所在地。 4.下列不属于政治风险的是( )。 （分数：2.00） A.政府更替 B.财产征用 C.主权风险√ D.政治冲突 解析：解析：政治风险指债务人因所在国发生政治冲突、政权更替、战争等情形，或者债务人资产被国有化或被征用等情形而承受的风险。 5.阿根廷2001-2002年发生了严重的金融危机，大量富人和中产阶级基于对阿根廷前途的担忧纷纷将资产转移至国外或将资产转换为美元，这导致了2001年12月1日阿根廷政府不得不宣布严格的资本冻结措施，包括冻结银行存款，限制提取现金及限制兑换美元，这加剧了阿根廷社会的动荡。这说明( )的变动引发了国别风险。 （分数：2.00） A.主权违约 B.银行业危机 C.转移事件√
欧洲债务危机的启示 法学0902缪旖 随着欧洲股市的“滑铁卢”，欧洲金融市场再度强烈震荡，市场恐慌进一步加剧。欧债危机暴露的是美欧经济实体没有好转，美国在金融海啸时对欧洲引起的强烈震荡也在持续的发挥其余温，暴露的是“结构性问题”，全球结构性问题。 从根源上来讲，欧债危机的爆发是经济发展过程中多种矛盾长期积累的后果，欧盟特别是欧元区的形成，对其成员国来说，在降低交易成本、促进各国经济共同发展发挥了积极的作用。但该种制度安排的本身，导致了区域内国家货币政策的独立性丧失。在经济出现较大波动时，由于欧元区各国不具备独立的货币政策，只好更多地倾向于使用财政政策来缓解危机。比如通过紧缩财政、提高税收等压缩总需求的办法增加偿债资金来源，这又使原本就不景气的经济状况雪上加霜。 长期的高福利政策是欧元区诸国财政负担沉重一大诱因。这些国家一直奉行高福利的社会经济政策，执政者出于选举的需要，不敢对经济和社会体制进行大刀阔斧的改革，长期维持高福利的社会保障体系，造成了财政的入不敷出，不得不依靠举债度日。随着区域一体化的日趋深入，以希腊、葡萄牙为代表的一些经济发展水平较低的国家，在工资、社会福利、失业救济等方面逐渐向德国、法国等发达国家看齐，支出超出国内产出的部分越来越大。 除此以外，欧盟各国的创新能力下降，使得经济发展缺乏坚实的基础和新鲜的动力。而经济的萎靡不振，又使得国家税收来源减少，加剧财政的负担。 面对欧洲债务危机，中国作为一个正在迅速崛起的发展中国家，尤其应该引起高度警惕和反思。中国的国情虽然和欧洲各国有很大差别，但其中也有许多可供借鉴和参照之处。从欧洲的困境中，至少可以得到以下几点启示。 首先，应高度重视政府债务问题，必须未雨绸缪，将风险遏制在萌芽状态。其次，保持国内货币政策的独立性，坚持将解决本国问题置于制定货币政策的中心环节。从表面上看，中国的货币政策具有完全的独立性。但其实不然，在国际经济形势波动剧烈时期，国内的货币政策往往容易被国外的政策动向所左右。最后，不断提高自主创新能力，发掘推动经济发展的新的增长点。经济发展是解决包括债务危机在内的各种经济问题的最佳良方。欧洲各国以及美日等国目前的债务危机，究其根源还在于增长乏力甚至停滞不前。中国经济已经保持了30多年的高速增长，寻找未来的发展动力和增长点是当前的一项紧迫任务。 最重要的是扩大内需,转变经济增长模式,提高国家的产业国际竞争力,增强经济的内生增长能力,不能完全走政府主导的投资拉动型经济发展模式。欧洲债务危机发生的根本原因是各国的经济增长发力,国际产业竞争力下降。发生危机的国家经济基础薄弱,加入欧元区后又面临了更大的生存压力,在私人投资不旺的情况下,政府投资成为推动经济发展的主力。而增加政府的债务和财政赤字。希腊借助筹备2004年雅典奥运会的东风,希腊大量举借国内外资金,投向基础设施建设,而当长期投资与短期借款的期限匹配出现问题时,债务危机的爆发在所难免。在拉动经济的三驾马车中,投资也一直是我国政府促进经济发展的杀手锏,特别是在本轮全球性金融危机爆发后,进出口倍受打击、消费市场萎靡不振的情况下,增加固定资产投资屡试不爽。我国也从2007年的财政盈余变成近两年的财政赤字。因此,如何降低经济发展对投资和出口的依赖,即降低资本形成和净出口对经济增长的贡献度、提高最终消费对经济增长的贡献度,是中国经济转型的关键,未来的基建投资应该适度压缩,提高居民收入、提高居民的消费倾向应该成为国内经济工作重点。