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Diapositiva

VIEWS: 50 PAGES: 73

									       Finance and the Crisis
         Umberto Cherubini
        University of Bologna

London Finance Graduate Program
    Birbeck College, 18/03/2011
                             Outline
• How the crisis started
   – Securitization structures
   – Toxic assets?
• How the crisis expanded
   – Counterparty risk
   – Liquidity and the accounting standards
• Where the crisis ended (did it end?)
   – Public debt and banks
   – The Government budget crisis
• What’s next?
                                              2
           Lessons from crises
• September-October, 1998: LTCM, the lesson to
  be learned is liquidity, and the incomplete
  market problem
• November-December 2001: Enron, the theme is
  lack of transparency of balance sheet data,
  problem of incomplete information
• May 2005: the crisis on securitization following
  downgrading of GM to junk. The theme is break
  in correlation. Hedge funds affected.

                                                     3
     The crisis of 2008, 2009, 2010…
• Credit crisis: “subprime” mortgages were the
  trigger of the crisis.
• Liquidity crisis: difficulty to unwind positions has
  exacerbated the crisis, like in the LTCM case
• Accounting transparency crisis: fair-value
  accounting has been a vehicle of contagion,
  Enron’s “lite accounting” has become the practice
  of the banking system. Use of derivative contracts
  for “window dressing”: councils, Greece, Italy??


                                                         4
             How the crisis started
• Credit crisis: the crisis began with fear of insolvency on
  asset-backed-securities (ABS), that is bonds guaranteed
  by pools of assets as collateral.
• Question: bonds guaranteed by collateral, whatever it can
  be, cannot be riskier than bonds guaranteed by no
  collateral at all. So why the crisis sprang from these
  assets, and not from the unsecured ones?
•   Possible answers: unsecured investment are monitored
    more closely that collateralized ones (moral hazard);
    securitized investments are marked-to-market (fair value
    accounting standards)

                                                               5
             Securitization deals

                              Senior Tranche
Originator
                             Junior 1 Tranche
                   Special
       Sale of     Purpose
                   Vehicle
       Assets                Junior 2 Tranche
                    SPV

                              Mezz Tranche


                             Equity Tranche

                                                6
              Synthetic CDOs
                                    Senior Tranche
Originator
                                   Junior 1 Tranche
        Protection       Special
        Sale             Purpose
                         Vehicle
       CDS Premia                  Junior 2 Tranche
                          SPV
         Interest
                      Investment
         Payments                   Mezz. Tranche
         Collateral
           AAA
                                   Equity Tranche

                                                      7
          The economic rationale
• Arbitrage (no more available): by partitioning the
  basket of exposures in a set of tranches the
  originator used to increase the overall value.
• Regulatory Arbitrage: free capital from low-risk/low-
  return to high return/high risk investments.
• Funding: diversification with respect to deposits
• Balance sheet cleaning: writing down non
  performing loans and other assets from the balance
  sheet.
• Providing diversification: allowing mutual funds to
  diversify investment
                                                          8
      Structuring securitization deals
• Securitization deal structures are based on
  three decisions
   – Choice of assets (well diversified)
   – Choice of number and structure of
     tranches (tranching)
   – Definition of the rules by which losses on
     assets are translated into losses for each
     tranches (waterfall scheme)


                                                  9
              Choice of assets
• The choice of the pool of assets to be securitized
  determines the overall scenarios of losses.
• Actually, a CDO tranche is a set of derivatives
  written on an underlying asset which is the overall
  loss on a portfolio
                  L = L1 + L2 +…Ln
• Obviously the choice of the kinds of assets, and
  their dependence structure, would have a deep
  impact on the probability distribution of losses.


                                                        10
                  Tranche
• A tranche is a bond issued by a SPV,
  absorbing losses higher than a level La
  (attachment) and exausting principal when
  losses reach level Lb (detachment).
• The nominal value of a tranche (size) is the
  difference between Lb and La

                Size = Lb – La


                                                 11
           Kinds of tranches
• Equity tranche is defined as La = 0. Its
  value is a put option on tranches.
            v(t,T)EQ[max(Lb – L,0)]
• A senior tranche with attachment La
  absorbs losses beyond La up to the value of
  the entire pool, 100. Its value is then

  v(t,T)(100 – La) – v(t,T)EQ[max(L – La,0)]

                                                12
           Arbitrage relationships
• If tranches are traded and quoted in a liquid market, the
  following no-arbitrage relationships must hold.
• Every intermediate tranche must be worth as the
  difference of two equity tranches
                EL(La, Lb) = EL(0, Lb) – EL(0,La)
• Buyng an equity tranche with detachment La and buyng
  the corresponding senior tranche (attachment La) amounts
  to buy exposure to the overall pool of losses.
  v(t,T)EQ[max(La – L,0)] +
          v(t,T)(100 – La) – v(t,T)EQ[max(L – La,0)] =
                       v(t,T)[100 – EQ (L)]

                                                              13
        Risk of different “tranches”
• Different “tranches” have different risk features.
  “Equity” tranches are more sensitive to
  idiosincratic risk, while “senior” tranches are
  more sensitive to systematic risk factors.
• “Equity” tranches used to be held by the
  “originator” both because it was difficult to place it
  in the market and to signal a good credit standing
  of the pool. In the recent past, this job has been
  done by private equity and hedge funds.


                                                           14
            Securitization zoology
• Cash CDO vs Synthetic CDO: pools of CDS on the asset
  side, issuance of bonds on the liability side
• Funded CDO vs unfunded CDO: CDS both on the asset
  and the liability side of the SPV
• Bespoke CDO vs standard CDO: CDO on a customized
  pool of assets or exchange traded CDO on standardized
  terms
• CDO2: securitization of pools of assets including tranches
• Large CDO (ABS): very large pools of exposures, arising
  from leasing or mortgage deals (CMO)
• Managed vs unmanaged CDO: the asset of the SPV is
  held with an asset manager who can substitute some of
  the assets in the pool.

                                                               15
             Standardized CDOs
• Since June 2003 standardized securitization deals were
  introduced in the market. They are unfunded CDOs
  referred to standard set of “names”, considered
  representative of particular markets.
• The terms of thess contracts are also standardized, which
  makes them particularly liquid. They are used both to
  hedged bespoke contracts and to acquire exposure to
  credit.
   – 125 American names (CDX) o European, Asian or
      Australian (iTraxx), pool changed every 6 months
   – Standardized maturities (5, 7 e 10 anni)
   – Standardized detachment
   – Standardized notional (250 millions)

                                                              16
 i-Traxx and CDX quotes, 5 year, September 27 th 2005

                      i-Traxx                                          CDX


  Tranche                Bid                Ask              Tranche   Bid     Ask


    0-3%                23.5*              24.5*               0-3%    44.5*   45*


    3-6%                 71                  73                3-7%    113     117


    6-9%                 19                  22               7-10%     25     30


   9-12%                 8.5                10.5             10-15%     13     16


  12-22%                 4.5                 5.5             15-30%     4.5    5.5


(*) Amount to be paid “up-front” plus 500 bp on a running basis
Source: Lehman Brothers, Correlation Monitor, September 28th 2005.




                                                                                     17
                Gaussian copula and
                 implied correlation
• The standard technique used in the market is based on Gaussian
  copula
        C(u1, u2,…, uN) = N(N – 1 (u1 ), N – 1 (u2 ), …, N – 1 (uN ); )
  where ui is the probability of event i  T and i is the default time of
  the i-th name.
• The correlation used is the same across all the correlation matrix.The
  value of a tranche can either be quoted in terms of credit spread or in
  term of the correlation figure corresponding to such spread. This
  concept is known as implied correlation.
• Notice that the Gaussian copula plays the same role as the Black and
  Scholes formula in option prices. Since equity tranches are options,
  the concept of implied correlation is only well defined for them. In this
  case, it is called base correlation. The market also use the term
  compound correlation for intermediate tranches, even though it
  does not have mathematical meaning (the function linking the price of
  the intermediate tranche to correlation is NOT invertible!!!)


                                                                              18
19
Base correlation




                   20
             CDO2

Originator
                        Senior Tranche
 Senior
 Tranche
  ABS                  Junior 1 Tranche
             Special
 (AAA)       Purpose
             Vehicle
                       Junior 2 Tranche
 Tranche i   SPV

 Tranche j                … Tranche

Tranche k              Equity Tranche

                                          21
    Gaussian factor model (Basel II)
• Assume a model in which there is a single
  factor driving all losses. The dependence
  structure is gaussian. In terms of
  conditional probabilility
                             N 1 u   m 
      PrDefault M  m  N                 
                                 1  2 
                                            
 where M is the common factor and m is a
 particular scenario of it.
                                                 22
                 Vasicek model
• Vasicek proposed a model in which a large
  number of obligors has similar probability of
  default and same gaussian dependence
  with the common factor M (homogeneous
  portfolio.
• Probability of a percentage of losses L d:
                      1   2 N 1 L   N 1  p  
     PrL  Ld   N                 d               
                                  2                 
                                                     
                                                          23
16

               Vasicek density function
14



12



10


                                                                   Rho = 0.2
8                                                                  Rho = 0.6
                                                                   Rho = 0.8


6



4



2



0
     0   0,1   0,2   0,3   0,4   0,5   0,6   0,7   0,8   0,9   1



                                                                          24
                 Vasicek model
• The mean value of the distribution is p, the value of
  default probability of each individual
• Value of equity tranche with detachment Ld is
   Equity(Ld) = (Ld – N(N-1(p); N-1 (Ld);sqr(1 – 2))
• Value of the senior tranche with attachment equal
  to Ld is
   Senior(Ld) = (p – N(N-1(p); N-1 (Ld);sqr(1 – 2))
  where N(N-1(u); N-1 (v); 2) is the gaussian copula.


                                                          25
   Vampires, zombies, toxic assets…
• We are “fairly” confident that vampires and
  zombies do not exist: what about toxic assets?
• A toxic asset is a creature with 30% attachment.
  Under which conditions can we create a toxic
  asset? We mean an asset that is worth 70% of its
  value.
• Assume a homogeneous portfolio of exposures
  and perfect correlation of the losses in the pool.
  Then, a toxic asset would require a pool with an
  average delinquency rate of 30%. Can it be
  serious? Or is it just another horror movie?
                                                       26
                  Fréchet bound
• Men get reflected in mirrors (if they are not vampires) and
  assets cannot exceed super replication bounds (if they are
  not toxic). According to the Vasicek formula, super-
  replication bounds are given by the bounds admitted for
  copulas (unless you define a new class that      you may
  call vampire copulas).
• Say two risks A and B have joint probability H(A,B) and
  marginal probabilities Ha(A) and Hb(B). We have that
  H(A,B) = C(Ha , Hb), and C is a copula function.
• C(u,v) = uv, independence
  C(u,v) = min(u,v), perfect positive dependence
  The perfect dependence cases (we overlook negative
  dependence here) are called Fréchet bounds.
                                                                27
 Price bounds of a senior tranche
 1


0,9



0,8


0,7


0,6


                                                                    Rho = 0
0,5
                                                                    Rho = 1


0,4


0,3


0,2



0,1


 0
      0   0,1   0,2   0,3   0,4   0,5   0,6   0,7   0,8   0,9   1




                                                                              28
        Toxic assets: the definition
• “Financial assets the value of which has fallen
  significantly and may fall further, especially as the
  market for them has frozen. This may be due to
  hidden risks within the assets becoming visible or
  due to changes in external market environment”
                                        FT lexicon
• It seems then to be a problem of
   – Liquidity (market frozen)
   – Ambiguity (hidden risk becoming visible)


                                                          29
           How the crisis expanded
• “Default losses on US sub-prime mortgages
  about 500 billion dollars.
• But in a mark-to-market world, deadly losses are
  valuation losses
   – Valuation losses as high as 4 trillion
   – Major banks failed without single penny of default
• BIS study of rescue package: EUR 5 trillion in
  committed resources”
Eli Remolona,IV Annual Risk Management Conf., Singapore, July 2010


                                                                     30
              Recipe for contagion
• Ambiguity: assets for which you do not know whether they
  are collateralized or not or the quality of the collateral are
  traded at discount
• Counterparty risk: you do not trust your neighbour, in spite
  of safety nets (netting, collateral). What went wrong?
• Liquidity: funding liquidity (compete for funds from
  everyone except your neighbor) and market liquidity (try to
  unwind positions in assets)
• Accounting: losses due to whatever (included liquidity) are
  marked-to-market and impair the balance sheet.


                                                                   31
                      Ambiguity
• Knightian uncertainty: uncertainty is when you do not
  know the odds. Risk refers to unambiguous bets.
• Ellsberg paradox, 1961: agents prefer unambiguous bets
  over ambiguous bets, that is agents are uncertainty
  averse
• Gilboa Schmeidler (1989): multiple prior approach, Max-
  Min-Expected Utility (MMEU): probabilities are
  represented by intervals, rather than numbers.
• Gilboa (1987), Schmeidler (1982,1989): Choquet utility
  (sub-additive measures represent uncertainty aversion)


                                                            32
          Ambiguity and the crisis
• Toxic assets are ambiguous bets. The effect of
  ambiguity is that:
   – Investors require a premium for uncertainty
   – Bid-ask spread larger
   – Portfolio inertia (people do not participate in the market
     when uncertainty increases)
• Ambiguity reduces market liquidity.
• Changes in ambiguity can be triggered by events
  specific to a single issuer (or issue), or by shocks
  affecting other issuers (or issues). This is called
  information-based contagion (i.e. Enron).
                                                                  33
     Counterparty risk and the crisis
• In 2008 the market was expecting a default of a
  big bank. On March 15 Bear Stearns was
  rescued. On September 15 Lehman Brothers was
  left to his destiny and went bust
• The crisis was a test for the risk mitigating system
  applied in the banking practice. The system was
  severely shaked, but in the end it worked (we do
  not know whether with the help of Governments,
  and what would have happened without).
• It is difficult to say whether the counterparty risk
  emergency is over or not.
                                                         34
     Counterparty risk (long position)
• Assume a forward contract CF.for the long
  party A. The value of credit exposure (CVA)
  is recovered as
      CFA(T) = max[S(T) – F(0),0](1 –1B) +
          max[S(T) – F(0),0]RRB1B –
              – max[F(0) –S(T),0]
                      =
       CF(T) – LgdB1Bmax[S(T) – F(0),0]
                                                35
     Counterparty risk (short position)
• For the short position we have instead
      CFB(T) = max[F(0) – S(T),0](1 –1A) +
          max[F(0) – S(T),0]RRA1A –
             – max[S(T) – F(0),0]
                      =
      – CF(T) – LgdA1Amax[F(0) – S(T),0]




                                             36
  Credit Valuation Adjustment (CVA)

• Counterparty risk requires a correction in
  the valuation of the CF contract, called CVA
  which amounts to a short position in a
  vulnerable option.

    EQ[P(t,T)Lgdi1imax[(S(T) – F(0)),0]]


                                                 37
                  A CVA Algorithm
• In order to allow for default at arbitrary time before
  maturity, consider the following algorithm
• 1. Partition the lifetime of the contract in a grid of times
  {t1,t2,…tn}
• 2. For every time period from ti-1 to ti compute the
  exposure, that is the short position in the option
• 3. For every time period from ti-1 to ti compute the
  expected loss from default of the counterparty
                 [Q(ti-1) – Q(ti)] X LGD X Option
  with Q(ti) the survival probability beyond time Q(t i)
• 4. Sum the expected losses

                                                                 38
      Risk mitigating techniques
• In order to reduce the credit risk in their
  derivative transactions, banks apply risk
  mitigating techniques that are inspired by
  futures market. These are implemented in
  the so called ISDA standard Credit Annex
• The risk mitigating techniques are:
  – Net exposure of the all open contracts (open
    interest account, in futures market jargon)
  – Deposit of collateral of profit and losses every
    week (margin in the language of futures)
                                                       39
              A simple example
• Assume a counterparty A has p forward contracts
  CFi open with counterparty B.
• The value of each exposure is given by
           CFi = max([Si(t) – P(t,Ti)Fi],0)
 where  = 1 represents long positions and  = – 1
  denotes short positions.
• Notice that the exposure is a short position in a
  portfolio of call options for long positions and put
  options for short positions.

                                                         40
                     Netting
• Assume that counterparty B defaults at time . In
  the presence of a netting agreement, exposure in
  this case will be given by a an option of a basket,
  rather than a basket of options

                     p
                                       
              max  S i    A ,0
                   i 1               
                           p
               A    P  , Ti Fi
                          i 1
                                                        41
          Monte Carlo simulation
• Counterparty risk is evaluated by Monte Carlo
  simulation
• Algorithm:
• Choose a set of dates: {t1,t2,…tn} and for each
  one of these evaluate a basket option
  (counterparty risk exposure)
• For each date ti the value of counterparty risk will
  be
      [Q(ti-1) – Q(ti)]Basket (S1, …Sp, ti; A(ti), ti)
  with Q(ti) the survival probability beyond time Q(t i)

                                                           42
                         Collateral
• The impact of collateral amounts to resetting the strike in
  favor of the party that receives the deposit (again as it
  happens in the futures markets).
• Collateral is deposited in cash or very safe securities. In
  come cases, however, the senior tranches were actually
  used as collateral.
• If one accounts for collateral, the CVA amounts to a short
  position in cliquet options.
• If risky collateral is used, it is typical to apply a “haircut” (a
  given amount of collateral provides guarantee for a lower
  amount of exposure)

                                                                       43
           What went wrong?
• Risk mitigating arrangements: the Lehman
  Bros default provided a test. It seems that it
  took about 15 days to compute and notify
  losses, due to negatives externalities:
  shortage of lawyers, difficulty to have third
  party fair valuation.
• Interbank market: the interbank market was
  left outside the risk mitigating arrangement.
  Credit risk haunted to the Euribor/Libor
  rates (difference between 3m Euribor/OIS)
                                                   44
            From credit to liquidity
• If you do not trust your neighbor and do not trust
  your assets, you are in liquidity trouble
• Funding liquidity: you must come up with funding
  for your assets, but the market is dry. Solutions: i)
  chase retail investors ii) rely on quantitative easing
  (won’t last long)
• Market liquidity: you are forced to unwind positions
  in periods of market stress, and you may not be
  able to find counterparts for the deal, unless at a
  deep discount. Solution: quantitative easing (place
  illiquid bonds as collateral)
                                                           45
       From liquidity to accounting
• Fair value accounting: bonds available for sale must be
  evaluated at fair value and profits and losses must be
  reported in the balance sheet.
• What is fair value? The price as close as possible to the
  market evaluation? But: what is a market?
• Types of assets:
• Type 1. Price is available on a transparent market
• Type 2. A variable needed to compute the price can be
  calibrated from a liquid market
• Type 3. Neither the price or market parameters can be
  observed
                                                              46
         Accounting and the crisis I
• What is a market? Two people exchanging one good is a
  market? Or do we need more people to say that we have a
  market? Sorites paradox (how many grains make a heap of
  sand?)
• In a market in which people do not trust their neighbors
  (counterparty risk) and do not trust their assets (ambiguity)
  accounting may have a perverse effect
• Assume counterparty A is in desperate need of cash and is
  obliged to unwind a position worth 100 overnight (say a
  senior tranche). Say no one wants to buy, and finally one
  finds a counterpart for 70. If this is considered a market, all
  institutions in the world will record a loss of 30 on the same
  asset. And tomorrow many others will be in need of cash…

                                                                    47
                “Lite accounting”
• Lite accounting was a term used for Enron to denote the
  fact that much of Enron’s debt and most of Enron
  managers’ bonuses where hidden in about 1 000
  companies controlled by Enron, but not consolidated in its
  balance sheet. Enron crisis was triggered by the request
  of consolidation from the auditing company (Arthur
  Andersen)
• SIV (Structured Investment Vehicle): lite accounting for
  banks. Off-balance sheet institutions, controlled by banks,
  issuing short term liabilities (commercial paper) and
  investing on long term bonds (senior tranches) to earn the
  difference in spread (carry). A receipe to boost leverage.

                                                                48
         Accounting and the crisis II
• Reconsider the modern version of Sorites paradox with
  lite accounting and SIV.
• Financial institution A has SIV , and in an illiquid market
  has difficulty to raise commercial paper to fund the assets.
  Then it is forced to look for a financial institution B to sell
  the assets. But financial institution B should buy the asset
  through its vehicle  which is also struggling to place
  commercial paper to fund his own assets.
• Notice: the first SIV in history were launched by Citigroup
  in 1988 and were given the names Alpha and Beta
  Finance Corporations.

                                                                    49
        Where did the crisis end?
• The crisis could end nowhere but in the only
  balance sheet that is not computed at fair value,
  namely Government and municipal entities
  balance sheets.
• Bail-out from the Government: special purpose
  interventions (see AIG, Fortis, and the like) and
  general purpose committments
• Central bank intervention: quantitative easing, to
  provide liquidity to the system and prevent
  contagion. It is almost over in Europe, still alive in
  the US.
                                                           50
      “Monstruous siamese brotherhood”?
• In the aftermath of the 29 crisis the most famous Italian
  banker, Raffaele Mattioli, founder of COMIT (BCI) denoted
  “mostruosa fratellanza siamese” the evolution of the
  relationship between banks and corporate clients. The
  “physiological symbiosis” typical of “universal banking”
  (that is lending and providing risk capital) had brought, in
  a period of credit crisis, the banks to take control of
  industrial firms.
• Today, the same “monstruous siamese brotherhood” is
  looming in the relationship between Government and the
  banking system.


                                                                 51
        The “siamese brotherhood”
• Banks have exposures to Government. Once monetary
  base was directly created by the central bank by lending
  to Government. Now lending is intermediated by banks.
  Government issue securities that are bought by banks in
  the primary market and placed as collateral with the
  central bank. Default of a Government would severely
  jeopardize the banking system.
• In these days the regulators are designing a new stress
  test of the soundness of the banking system in front of a
  public debt crisis ending with default. The old stress test
  tried in September was only based on the value
  impairment of a crash in the public debt securities market.


                                                                52
                Fail or be rescued?
• The other face of “siamese brotherhood” is the implicit
  guarantee offered by the Government to banks
• Too big to fail (or to big to save?). The debate is about
  whether it is possible to allow big institutions (systemically
  important financial intermediary, SIFI) to go bankrupt
• Taxation on SIFI: they would pay for insurance from the
  public. Pros: makes moral hazard more costly. Cons: who
  is SIFI? Any volunteer?
• Living wills: should (or could) big banks prepare their own
  funeral? Pros: assets are perishable goods. Reduces
  moral hazard because makes default credible. Cons: how
  to plan externalities? Can you be credible if you state that
  you will walk into the grave on your own?
                                                                   53
          Marshall Olkin copula
• Marginal survival probabilities
• P(1 > T) = exp(– (1 + 12)(T – t )) = u1
• P(2 > T) = exp(– (2 + 12)(T – t )) = u2
  P(1 > T, 2 > T) = u1u2 min(u1-1 u2 - 2)
  with -i = 12 /(i + 12)
• This is known as Marshall Olkin copula



                                                54
             Portfolio intensity
• The idea of Marshall Olkin distribution is that
  different shocks bring about defaults of
  subsets of names.
• The problem is that there may exist an
  arbitrarily large number of shocks and this
  makes calibration of the model very difficult.
• Factor model specification
                       n
                    i  123....n
                      i 1


                                                    55
       Filters of common shocks
• Call m the cross-section average intensity
• Given 1/ (average of inverse Kendall’s )
  and 1/ (average of inverse Spearman’s ).

                                                       
                                                       
                    2                         4 1      
    123 ... n             m 123 ... n   
                   1
                        1                     3 1 1    m
                                              3      
                                                       

                                                              56
               0
                                                     0,1
                                                                                             0,2




                   0,02
                          0,04
                                 0,06
                                        0,08
                                                                 0,12
                                                                        0,14
                                                                               0,16
                                                                                      0,18
     01/01/ 2008

     01/03/ 2008

     01/05/ 2008

     01/07/ 2008

     01/09/ 2008

     01/11/ 2008

     01/01/ 2009

     01/03/ 2009

     01/05/ 2009

     01/07/ 2009
                                                                                                   Italy




     01/09/ 2009

     01/11/ 2009

     01/01/ 2010

     01/03/ 2010

     01/05/ 2010

     01/07/ 2010

     01/09/ 2010
                                                                Govt


                                         Financial
                                                     Systemic




57
               0
                          0,1
                                                              0,2
                                                                           0,3




                   0,05
                                            0,15
                                                                    0,25
     01/01/ 2008

     01/03/ 2008

     01/05/ 2008

     01/07/ 2008

     01/09/ 2008

     01/11/ 2008

     01/01/ 2009

     01/03/ 2009

     01/05/ 2009

     01/07/ 2009
                                                                                 Spain




     01/09/ 2009

     01/11/ 2009

     01/01/ 2010

     01/03/ 2010

     01/05/ 2010

     01/07/ 2010

     01/09/ 2010
                                                       Govt


                                Financial
                                            Systemic




58
              0
                         0,1
                                                  0,2
                                                                           0,3
                                                                                        0,4




                  0,05
                               0,15
                                                                    0,25
                                                                                 0,35
     01/01/2008

     01/03/2008

     01/05/2008

     01/07/2008

     01/09/2008

     01/11/2008

     01/01/2009

     01/03/2009

     01/05/2009

     01/07/2009
                                                                                              Portugal




     01/09/2009

     01/11/2009

     01/01/2010

     01/03/2010

     01/05/2010

     01/07/2010

     01/09/2010
                                                             Govt


                                      Financial
                                                  Systemic




59
               0
                   0,1
                         0,2
                               0,3
                                                      0,4
                                                             0,5
                                                                   0,6
                                                                         0,7
     01/01/ 2008

     01/03/ 2008

     01/05/ 2008

     01/07/ 2008

     01/09/ 2008

     01/11/ 2008

     01/01/ 2009

     01/03/ 2009

     01/05/ 2009

     01/07/ 2009
                                                                               Gre e ce




     01/09/ 2009

     01/11/ 2009

     01/01/ 2010

     01/03/ 2010

     01/05/ 2010

     01/07/ 2010

     01/09/ 2010
                                                      Govt


                               Financial
                                           Systemic




60
               0
                          0,1
                                             0,2
                                                                       0,3
                                                                                    0,4




                   0,05
                                0,15
                                                              0,25
                                                                             0,35
                                                                                          0,45
     01/01/ 2008

     01/03/ 2008

     01/05/ 2008

     01/07/ 2008

     01/09/ 2008

     01/11/ 2008

     01/01/ 2009

     01/03/ 2009

     01/05/ 2009

     01/07/ 2009
                                                                                                 Ire land




     01/09/ 2009

     01/11/ 2009

     01/01/ 2010

     01/03/ 2010

     01/05/ 2010

     01/07/ 2010

     01/09/ 2010
                                                                Govt


                                       Financial
                                                   Systemic




61
              0
                                                     0,1
                                                                                              0,2




                  0,02
                         0,04
                                0,06
                                       0,08
                                                                  0,12
                                                                         0,14
                                                                                0,16
                                                                                       0,18
     01/01/2008

     01/03/2008

     01/05/2008

     01/07/2008

     01/09/2008

     01/11/2008

     01/01/2009

     01/03/2009

     01/05/2009

     01/07/2009
                                                                                                    U.K.




     01/09/2009

     01/11/2009

     01/01/2010

     01/03/2010

     01/05/2010

     01/07/2010

     01/09/2010
                                                                Govt


                                         Financial
                                                     Systemic




62
              0
                                                      0,1
                                                                                                    0,2




                  0,02
                         0,04
                                0,06
                                       0,08
                                                                        0,12
                                                                               0,14
                                                                                      0,16
                                                                                             0,18
     01/01/2008

     01/03/2008

     01/05/2008

     01/07/2008

     01/09/2008

     01/11/2008

     01/01/2009

     01/03/2009

     01/05/2009

     01/07/2009

     01/09/2009
                                                                                                          Netherlands




     01/11/2009

     01/01/2010

     01/03/2010

     01/05/2010

     01/07/2010

     01/09/2010
                                                                 Govt


                                          Financial
                                                      Systemic




63
              0
                                                              0,1




                  0,02
                         0,04
                                0,06
                                                       0,08
                                                                    0,12
                                                                           0,14
     01/01/2008

     01/03/2008

     01/05/2008

     01/07/2008

     01/09/2008

     01/11/2008

     01/01/2009

     01/03/2009

     01/05/2009

     01/07/2009
                                                                                  France




     01/09/2009

     01/11/2009

     01/01/2010

     01/03/2010

     01/05/2010

     01/07/2010

     01/09/2010
                                                       Govt


                                Financial
                                            Systemic




64
              0
                                                                     0,1




                  0,02
                         0,04
                                0,06
                                                          0,08
                                                                           0,12
                                                                                  0,14
     01/01/2008


     01/03/2008


     01/05/2008


     01/07/2008


     01/09/2008


     01/11/2008


     01/01/2009


     01/03/2009


     01/05/2009


     01/07/2009
                                                                                         Germany




     01/09/2009


     01/11/2009


     01/01/2010


     01/03/2010


     01/05/2010


     01/07/2010


     01/09/2010
                                                              Govt


                                       Financial
                                                   Systemic




65
               0
                          0,1
                                               0,2
                                                                    0,3
                                                                                 0,4




                   0,05
                                0,15
                                                             0,25
                                                                          0,35
     01/01/ 2008

     01/03/ 2008

     01/05/ 2008

     01/07/ 2008

     01/09/ 2008

     01/11/ 2008

     01/01/ 2009

     01/03/ 2009

     01/05/ 2009

     01/07/ 2009
                                                                                       Aus tria




     01/09/ 2009

     01/11/ 2009

     01/01/ 2010

     01/03/ 2010

     01/05/ 2010

     01/07/ 2010

     01/09/ 2010
                                                          Govt


                                   Financial
                                               Systemic




66
                            -to-market of theimplicit guarantee to asystemic shock (bn euro)
                Table 5. Mark
             Intensity        DP          LGD       Government Commitments          Liability -
                                                       Liability                 Commitments
 Portugal      6,04%       26,06%        312,12         73,68           20            53,68
  Ireland      7,15%       30,05%         980,4         266,85         430           -163,15
    Italy      2,65%       12,42%       2248,62         252,98          20            232,98
  Greece      12,12%       45,45%        295,14         121,51          28            93,51
   Spain       4,73%       21,06%       2068,08         394,57         329            65,57
 Germany       0,94%        4,57%       4461,66         184,89         480           -295,11
  France       1,36%        6,56%       4594,02         273,00        288,95          -15,95
     UK        2,07%        9,85%        5677,2         506,61        444,66          61,95
Netherland     1,70%        8,15%        1330,2         98,23          200           -101,77
  Austria      2,79%       13,02%        618,12         72,90           90            -17,10
                                              Total    2245,23       2330,61          -85,38




                                                                                                  67
             600




             500                                     Germany
                                                                                                 y = 0,8025x + 52,877
                                                                  Ireland                            R2 = 0,3876
                                                                                                                  U.K.
             400
Commitment




                                                                  France
             300                                                                         Spain




                                  Netherlands
             200




             100
                        Austria

                                            Greece             Italy
                       Portugal
              0
                   0                  100              200                  300           400                    500     600
                                                                  Government Liability



                                                                                                                               68
The Government crisis, finally
                Table 6. Bail-out Government liabilityand
                                Debt/GDP
              Debt/GDP      Liability/GDP        Total
   Portugal    76,80%          44,96%           121,76%
    Ireland    64,00%          163,17%          227,17%
      Italy   115,80%          16,63%           132,43%
    Greece    115,10%          51,16%           166,26%
     Spain     53,20%          37,54%           90,74%
   Germany     73,20%           7,68%           80,88%
    France     77,60%          14,31%           91,91%
       UK      68,10%          32,34%           100,44%
  Netherlands 60,90%           17,23%           78,13%
    Austria    66,50%          26,33%           92,83%




                                                            69
         Ingredients of the crisis
• Credit crisis: example of Greece and the so called
  PIIGS (GIPSI). Unsustainable debt with respect to
  credible future primary surpluses.
• Liquidity crisis: funding liquidity experienced for
  the GIPSI at the beginning of the year, primary
  market closely monitored by regulators
• Accounting crisis: no fair value (thanks God), but
  lot of accounting creativity. Lite accounting? May
  be…


                                                        70
          Public debt transparency
• Derivatives have been used to “window dress” public debt
  accounting data: the case is Greece (and rumours about
  Italy). The technique is fairly easy. Instead of plain loans,
  investment banks offer swap transactions with large
  upfront in favour of the Government (and large
  commissions hidden in the deal). You receive money for
  your current deficit in exchange for higher deficits that
  next generations will pay.
• Lite accounting? We are not sure. But in some situation
  one could suspect a transfer of debt from the central
  Government to the municipal Governments in much the
  same way as debt was transferred from Enron to the
  subsidiaries. This is something that is worth studying.

                                                                  71
                   Eurobonds?
• The Eurobond proposal: to substitute public debt with
  bonds guaranteed by a fund supported by all
  Governments, and make these bonds senior with respect
  to the others.
• Problems:
   – Who would like to become junior? Any volunteer?
   – Tranching implies the “banana effect” (low spread for
      senior debt will be paid by higher spread on junior)
   – If junior debt becomes defaultable or subject to
      restructuring is the same as saying that the guarantee
      of Government is limited to a subset of debt, so there
      may be spread effect beyond the “banana”
   – What will happen to CDS with the restructuring clause?
                                                               72
           Ammortizing schedules
• Someone has proposed that Governments should be
  prevented from issuing bullet bonds and be compelled to
  define an amortizing plan for each issue. Italy is a case
  study with respect to this, since local councils are exactly
  required to define an amortizing plan for debt. But this has
  caused some problems.
• Problems:
   – Since the market is not used to amortizing issues,
     Government would be required to go on with bullet
     issues and to turn to financial engineering to change
     these issues to amortizing
   – Governments would be pushed to use derivatives and
     sinking funds.
                                                                 73

								
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