Default Recovery Rates And Implied Default Probability -Books Download

Default Recovery Rates and Implied Default Probability

2020 | 4 views | 24 Pages | 331.17 KB

Default Recovery Rates and Implied Default Probability Estimations: Evidence from the Argentinean Crisis, December 2001. Ramiro Sosa Navarro (University of Evry, EPEE)y January 2005 Abstract This paper estimates both the default recovery rates and the risk-neutral default probabilities of the Argentinean Sovereign Bonds during the crisis of December 2001. The model presented by J. Merrick Jr ...



1 Introduction
Over the last thirty years the theory of pricing credit risk has been put
forward in order to measure corporate debt Even if similar approaches
should be applied for the calculation of sovereign risk it becomes essen
tial to point out the di erences between risky corporate debt and risky
sovereign debt as well as their consequences in valuing assets For in
stance emerging country sovereign bonds are issued in countries such as
the United States of America and the United Kingdom under completely
di erent legal jurisdiction and capacity of enforcement if compared with
corporate bonds Emerging countries are more stable than corporations
they are fewer in number they have longer term economic planning
they do not default as frequently as corporations do and they do not
typically disappear Consequently there is considerably less empirical
evidence of default on sovereign debt than on corporate debt 1
As regards the theoretical background most of the models focus on
default risk adopting static assumptions treating default recovery rates
either as a constant parameter or as a stochastic variable independent of
the probability of default The connection between default recovery rates
and implied default rates has traditionally been disregarded by credit
risk models Accordingly the problem faced by analysts in 2001 was
how to settle default recovery rates and the implied default probability
of their portfolios only on the grounds of their bond prices Now if
the bond price is a function of two unknown determinants how could
analysts calculate both of them simultaneously and consistently
Thus the approaches applied by portfolio managers in Argentina
in 2001 were grounded on the analysis of domestic and foreign data
generated by earlier international crises such as those of Mexico 1995
Russia 1998 and Brazil 1999 One of the approaches consisted in the
analysis of the time series in relation to indicators such as peaks trends
and the volatility of domestic and foreign sovereign bond price levels
An alternative approach based on a sensitivity analysis considers the
bond market price or spreads in order to calculate the implied default
probability for di erent possible recovery rates This method entails
forming conjectures about the value of recovery and the size of spread
by resorting to evidence provided by earlier crises 2
For a survey of the literature concerning this topic see Altman Edward Andrea
Resti and Andrea Sironi 2004 Default Recovery Rate in Credit Risk Modeling A
Review of the Literature and Empirical Evidence Economic Notes by Banca dei
Monte dei Paschi di Siena Volume 33
For an example of this approach see Federico Sturzenegger 2000 Defaults
The disadvantage of this approach is that its outcomes result from
di erent bond temporal term structures and hence from di erent bond
durations when compared to those of the analysed bonds Consequently
the information provided is misleading Moreover the approach does not
include information concerning recently issued bonds nor the particular
macroeconomic conditions of the country subject to analysis There
fore these methods neglect highly relevant information which is later
incorporated ad hoc into the analysis
Knowledge of both bond price determinants the default recovery
rate and the implied default probability enables the analyst to antici
pate the value of their position in case of default and assume a long or
short position according to the benchmark among other strategic deci
sions As a result the motivation of this research was based on the lack
of methodology applied by Argentinean portfolio managers in valuing
their stressed portfolios of Sovereign Bonds in the period previous to the
economic collapse
In order to avoid these disadvantages we have applied a model origi
nally presented by Merrick 2001 to estimate the default recovery rates
and the implied default probabilities in Argentinean Sovereign Bond
1 1 Brief Summary of Events Preceding the Crisis
Before presenting the model it is worth looking at the most important
events which caused the Argentinean crisis in December 2001 In August
1998 Russia defaulted on their public debt depriving Argentina of access
to the international capital market Five months later Brazil devalued
their currency causing Argentina s competitiveness in foreign markets
to deteriorate The economy sank into recession with twin de cits
a trade balance gap and a scal budget gap which foreigners were
less and less willing to nance Argentinean economy needed to regain
competitiveness and since the exchange rate could not be permitted to
fall prices and wages had to drop In December 1999 after the general
election Mr De la R a was elected to o ce but the new political
structure was too weak to face the strong political change necessary to
overcome the crisis
As a consequence peso quotation edged downwards tax revenues
faltered and Argentina s debts in US dollars became harder to repay In
Episodes in 90 s Factbook Tool kit and Preliminary Lessons prepared for the
World Bank page 14
spite of this Argentina refused to fold and kept raising the stakes At the
beginning of 2001 Argentina requested a USD 15 billion loan from the
IMF which was known as blindaje or armour In order to buy some
time in June 2001 the country completed the notorious megaswap in
which near dated securities were exchanged for longer dated securities
higher yielding bonds In August 2001 Argentina received a second 8
billion bail out Finally political turmoil and lack of further assistance
from multilateral institutions drove Argentina into default in December
2001 see Graph1
Graph 1 Argentinean Sovereign Debt Spread
Relevant Pre Default Events
JPM EMBI ARGENTINE STRIPPED SPREAD
Second IMF
Basic Points bp
Megaswap for Near dated
to Long dated Bonds
Brazil devalues First IMF Bail out
the Real the Blindaje
01 01 2002
01 01 1999
01 02 1999
01 03 1999
01 04 1999
01 05 1999
01 06 1999
01 07 1999
01 08 1999
01 09 1999
01 10 1999
01 11 1999
01 12 1999
01 01 2000
01 02 2000
01 03 2000
01 04 2000
01 05 2000
01 06 2000
01 07 2000
01 08 2000
01 09 2000
01 10 2000
01 11 2000
01 12 2000
01 01 2001
01 02 2001
01 03 2001
01 04 2001
01 05 2001
01 06 2001
01 07 2001
01 08 2001
01 09 2001
01 10 2001
01 11 2001
01 12 2001
01 02 2002
01 03 2002
This paper is divided in three sections Section II describes the Model
and the Data Section III analyses the estimations and results and Sec
tion IV presents the conclusions Finally the Appendix produces a de
tailed presentation of the estimated results and other complementary
macroeconomic data
2 The Model
This section presents the pricing framework for N period sovereign bonds
which is made up of four elements
The rst element is the bond structure which is made up of the
coupons and the principal showing the amount of the coupon paid in
period t as Ct and the amount of the principal paid on due date in
period N as FN
The second component is the default recovery rate which is repre
sented with letter R In this analysis R is the amount paid to the
bondholder immediately after default has been announced If the scal
authority defaults on the public debt the following scheme takes place
the coupons are not longer paid but the investors will receive a xed
fractional recovery of the face value immediately after defaulting 3
The third element is the adjusted risk neutral payment probability
distribution We will de ne Pt as the joint probability of no default be
tween the moment when the bond is issued and the moment t Moreover
denote the adjusted probability of default during the speci c date t 1
to date t period as pt Thus the risk free adjusted default probability is
indicated by means of pt and is de ned as 4
p t Pt Pt 1
The fourth and last element is the risk free present value discounted
factor for a time t cash ow denoted ft The discount rate used is the
risk free rate since the asset risk is captured by the probabilities of each
possible cash ow as it is shown below in equation 1
Having described the four elements we are in a better position to
state equation 1 which enables us to value a bond through the ex
pected present value of cash ows As it has already been suggested by
Jonkhart 1979 Fons 1987 and Hurley and Jonson 1996 we state
that the present value of a bond is the sum of its expected cash ows
coupons principal and the recovery rate multiplied or adjusted by
their probability As in Leland and Toft 1996 and Merrick 2001
the probability distribution used here is interpreted as the implied risk
neutral distribution Henceforward we are implicitly referring to risk
neutral probabilities
V0 fPt ft Ct g fPN fN FN g fpt ft Rg 1
The bond s current value is viewed as the probability weighted sum
of the coupon ows the principal and the recovery rate It should be
The recovery rate can also be de ned as the expected present value of cash
ows which have been or are to be reprogrammed For a detailed presentation see
Recovery Rates The Search of Meaning High Yield Merrill Lynch March 2000
Alternatively the probability
Pof receiving a promised date t coupon payment Pt
can be expressed as Pt 1 s 1 ps
noted that expressing the pricing equation in these terms implies that
the asset risk becomes captured by the implied default probability and
its complement the implied probability of payment As a consequence
all possible cash ows coupons principal and the recovery rate remain
discounted at the risk free rate Otherwise the asset risk is generally
enclosed in the discount rate factor
Let us now outline the model a little further Before stating the
joint probability of no default Pt we de ne the risk neutral default
probability rate noted as t Previous researches such as Fons 1987
and Bhanot 1998 consider a constant t Our proposal as much as
Merrick 2001 understands t as an increasing linear function with
respect to time t as it is shown in equation 2
The purpose of this function is to capture the default probability
temporal term structure throughout time in a parsimonious way This
formalisation registers the fact that in a critical period the probability
of default is greater as the deadline of the coupons and the amortisation
become closer in time 5
Thus the joint probability of no default Pt can be de ned as
In which parameters and are restricted so that Pt is always
less than or equal to one and greater or equal to zero Consequently
equation 4 explicitly states the three unknown elements R and
incorporated in the model
V0 1 t t ft Ct 4
1 N N fN FN
1 t t ft R
Otherwise during crisis long term default probabilities might be lower than the
short term conditional on the sovereign s ability to avoid the case to fall into default
This e ect is not captured by this assumption
Having established the equations it is possible to present the model
that allows for a consistent estimation of the three unknown parameters
which will in turn enable us to know the default recovery rate and the
default probability temporal term structure
2 1 Estimation Strategy
In order to estimate the unknown parameters we de ne the bond s
model value Vi 0 by substituting in equation 4 the three unknown
parameters by its estimations R Then consider at date t 0 a
cross section of I outstanding bonds indexed by the subscript i Now
we are able to de ne the sum square of residuals SSR at date 0 as
SSR0 Vi 0 Vi 0 5
where Vi 0 denote the market value of the ith bond at the date 0
recalling that Vi 0 is the estimated ith bond price
The each day estimation parameters can be achieved by getting the
value for R and that minimise equation 5 subject to the average
cross sectional bond pricing residual equalised to zero expressed as
Vi 0 Vi 0 0 6
As a consequence the model as a whole is formalised through the
statement of ve equations it means i 1 5 Then for each day in
the sample it was constructed the cash ow event tree for each of the ith
bonds according to equation 4 Next initial guesses were used for the
unknowns to estimate the parameters Finally a Solver was employed to
minimise square residuals equation 5 on condition that the average
sum of errors is equalised to zero equation 6 Subsequently this exer
cise is repeated for each day of the analysed period The Solver applies
the Generalised Gradients Method to estimate the unknown elements 6
For the model to be consistent it is assumed that the bonds have
a cross default clause which is a realistic assumption in the case of
Argentina This assumption implies that there is a representative default
recovery rate for the economy as a whole
In this paper we have used the Solver included in Microsoft O ce Package
Notice that the estimations were computed using an algorithm of
non linear optimisation subject to non linear constraints So it does
not guarantee that the results are the global solution However experi
mentations with alternative initial guesses conduct to the same results
The Appendix includes an example that shows the estimated results
based on the market price structure of October 1st 2001 The data and
results concerning the fourth quarter 2001 are shown in a Table
It have been selected the ve most representative bonds of the econ
omy i e the bonds which have been most actively traded in the short
medium and long term From these ve bonds we obtain the default re
covery rate and the default probability temporal term structure which
are the most representative determinants of the economy for a given
market price structure at each moment in time
2 2 The Data
For the period subject to analysis October 2001 December 2001 we
have considered 5 Global Bonds denominated Eurobonds at a xed
rate with semestrial coupons and amortisation at nish These charac
teristics are speci ed below
Table 1 Sample of US Dollar denominated Eurobonds
Name Issue Date Maturity Date Coupons
Arg 03 20 Dec 1993 20 Dec 2003 8 375
Arg 06 09 Oct 1996 09 Oct 2006 11 000
Arg 10 15 Mar 2000 15 Mar 2010 11 375
Arg 17 30 Jan 1997 30 Oct 2017 11 375
Arg 27 19 Sep 1997 19 Sep 2027 9 758
These bonds are not guaranteed They have a cross default clause
and they were issued under the jurisdiction of English Courts in London
This analysis was carried out considering the daily prices supplied by
the Secretary of Finances of the National Ministry of Economy from the
Argentine Republic
Figure 1a shows the average daily prices for the bonds which have
been described as representative of the economy for the period we are
analysing Figure 1b in turn speci es the same series considering each
of the bonds individually
Figure 1a Average Bond Prices
Average Price
Bond Price for each USD 100 Face Value
October November December
Figure 1b Individual Bond Prices
RA 03 RA 06 RA 10 RA 17 RA 27
Bond Price for each USD 100 Face Value
October November December
3 Estimation Results
This section deals with the model estimations concerning the aforemen
tioned Eurobonds for the case of the Argentinean domestic crisis It will
be focu on the fourth quarter 2001
It is worth noticing that the Base Default Probability is denoted in
the model by means of parameter Alfa and it de nes the current de
fault probability The estimations regarding parameter Beta which
is employed to calculate the default probability temporal term struc
ture shows an increasing linear trend with respect to time as it was
de ned However we will not analyse the estimations of the Betas and
the changes in the steepness of the temporal term structure In what
follows both the default recovery rates and base default probabilities
estimations are presented in Figure 2a 7
Figure 2a Estimated Default Recovery Rates and Base Default
Probabilities
Alpha Recovery Rates
US Dollar and Percentage
It is depicted that between October 1st and December 28th 2001 the
average bond market value re ected a downward trend falling from USD
59 5 to USD 27 6 for each USD 100 face value Similarly default recovery
rates descended from USD 28 5 to USD 20 1 reaching its maximum level
USD 40 9 on October 19th and its minimum USD 14 6 on November
13th Conversely the base default probability registered an increase
from 14 8 to 40 4 reaching its maximum level 45 5 December 21st
and its minimum 13 3 on October 19th
Notice that on October 19th the estimations show it the maximum re
covery rate USD 40 9 and its minimum base default probability 13 3
On the other hand on December 21st the base default probability reg
istered maximum level 45 5 while the default recovery rate is one of
the lowest in the sample USD 20 8 Thus both embedded determinants
become relevant in explaining bond price volatility while they seem to
follow a negative correlation but long periods have to be considered for
instance one and half month equivalent to 30 observations Figure 2b
shows the estimation results depicting linear trend lines
Regarding the shape of the default probability temporal term structure another
approach is presented in more detailed way by Andritzky J R 2004
Figure 2b Estimated Recovery Rates and Default Probabilities with
linear trendlines
Alpha Recovery Rates
US Dollar and Percentage
Regarding the standard deviation of the estimations they are due to
the fact that for some estimation the square residuals are low one digit
whereas for others estimations the square residuals range from 15 to 30
See in the Appendix the table of input data and estimation results
The cases in which residuals are close to zero and so the estimations
are very accurately the Solver has found a combination of estimated
parameters and so estimated bond prices which exactly reproduce
the yield duration market curve See in the Appendix the example for
October 1st
The information provided by the model enables the individualisation
of the parameters ruling over market prices But in order to improve
the quality of the information supplied it has been plotted the series
considering a two period and four period moving average to obtain a
more stable series which can average out the statistic errors See Figure
Figure 3 Default Recovery Rate and Base Default Probability with
Figure 3a Two moving average Figure 3b Four moving average
Alpha Recovery Rates Alpha Recovery Rates
Price for each USD 100 Face Value
Price for each USD 100 Face Value
Dates Dates
As it can be seen in Figures 2 the period October 1st October 10th
shows that both curves are stable and that the default recovery rate
registers a downward trend whereas the Default Probability reveals an
upward trend both being coherent with a drop in bond prices It must
be observed that both determinants show a moderate gradient which cor
responds to the trend intensity registered by market prices see Figures
1 Subsequently the opposite phenomenon is registered from October
11th to October 19th Thus the Model presented is capable of assessing
slight oscillations in market prices However for some short periods two
weeks which equal 10 observations the estimations register a positive
correlation between recovery rates and base default probabilities
A negative relationship is accomplished if we take a longer period
so that statistic errors can be compensated for Considering the period
extending from October 19th to December 21st along which bond prices
registered a downward trend it is possible to observe that default re
covery rates start at USD 40 9 for each USD 100 face value and reach
USD 20 8 whereas base default probability starts at 13 3 and reaches
45 5 See below Figures 4
Figure 4 Default Recovery Rate and Base Default Probability with
From October 19th to December 21st
Figure 4a Logarithmic Trendline
Alpha Recovery Rates
US Dollar and Percentage
Figure 4b Linear Trendline
Alpha Recovery Rates
US Dollar and Percentage
To sum up the increase in prices was accompanied by an increase in
default recovery rates and a fall of implied default probabilities Con
versely the reduction in prices was accompanied by a drop in default
recovery rates and an increase in implied default probabilities The re
sults obtained show that for long periods e g a two month period the
model produces results which are consistent in time
3 1 Interpretation of Results
Market information produced between December 10th and December
28th before and after default was o cially announced is presented in
the following Table
Table 2 Estimated Parameters
Before and after Default December 24th
Average Recovery
Date RA 03 RA 06 RA 10 RA 17 RA 27
Price Rate
10 Dec 36 8 32 8 29 0 29 0 29 0 31 32 20 73
11 Dec 36 0 34 0 29 0 30 0 29 0 31 60 22 04
12 Dec 35 9 34 4 30 1 30 0 31 0 32 28 24 16
14 Dec 37 0 33 1 30 0 27 1 32 0 31 84 22 15
17 Dec 37 0 33 6 29 4 30 0 31 5 32 20 23 30
18 Dec 35 5 34 0 30 5 27 5 32 0 31 90 24 21
19 Dec 36 1 33 4 29 5 25 8 30 0 30 96 20 77
20 Dec 28 5 34 5 29 5 26 3 32 0 30 16 16 08
21 Dec 28 9 28 5 26 0 23 9 25 3 26 52 20 79
26 Dec 28 0 28 0 23 3 23 9 26 0 25 84 20 01
27 Dec 29 8 25 5 24 0 26 0 23 0 25 66 17 50
28 Dec 31 0 28 0 26 0 28 0 25 0 27 60 20 15
These data show that the market adjusted the bond prices falling
from USD 30 02 for each USD 100 face value to USD 26 5 on December
21st after the resignation of the Minister of Economy and the President
instead of producing the adjustment on December 26th after default
was o cially announced Thus we understand that Argentina really
defaulted on December 21st 8
As regards the default recovery rates evidenced between December
10th and December 28th these estimations make for a good approxima
tion to the market value as they present quite small square residuals
except for those registered on December 20th It should be obseved that
estimations recorded on December 20th registered square residuals of
three digits Consequently in order to obtain a better approximation to
this value we will take the average value of default recovery rates in the
pre default period i e between December 10th and December 19th
This average value amounts to USD 22 48 9
Brief chronicle of the events leading to the crisis On December 20th the Minister
of Economy Dr Domingo F Cavallo and the President Dr Fernando De La R a
submit their resignation On December 21st the president of the Senate Dr Ram n
Puerta takes over provisionally for a 48 hour period On December 23rd Dr Adolfo
Rodr guez Saa is appointed as President On December 24th he announces the
country s insolvency before the National Congress
Given that the market price on December 20th registers USD 30 2 less than the
prices registered between December 10th and December 19th USD 31 0 USD 32 3
the Default Recovery Rate implicit in that price should be marginally smaller but in
no case close to USD 16 08
To sum up the results before and after market adjustment were as
Period 10 20 12 2001
Data Interval Average
Average Price USD 30 2 USD 32 3 USD 31 5
Recovery Rate 1 USD 20 7 USD 24 2 USD 22 5
Period 21 28 12 2001
Data Interval Average
Average Price 2 USD 25 8 USD 27 6 USD 26 4
Recovery Rate USD 20 8 USD 17 5 USD 19 6
Market average prices registered as of December 21st the date the
market considers Argentina defaulted are considered as the default
recovery rates validated by the market As a result this paper compares
market prices registered on December 21st and the default recovery rate
evidenced on December 20th In other words if the economic system
unexpectedly defaults in a period t the market price in the period t 1
should be equal to the recovery rate implicit in the last market price
Thus we have that
Data Interval Average
The difference
USD 5 1 USD 3 4 USD 3 9
It follows that bonds were overvalued at USD 3 9 on average in a
range of USD 5 1 and USD 3 4 that is by 12 9 We interpret that
it would have been correct to adopt a short position and buy when the
market evidenced the model estimations that is when the assets were
quoted at average values of USD 22 5 in a range of USD 20 7 and USD
24 2 10 as it happened as of May 2002
However for a proper interpretation of the data it is crucial to sit
uate the model in the market conditions registered at the time With
this respect two elements should be highlighted Firstly the Stage 1
of the debt swap started on October 30th the public debt held by do
mestic investors was forcibly swapped in this stage replacing the bonds
which accrued an annual 10 4 interest on average with bonds quoted
at a 6 annual interest rate This explains why Argentinean Bonds were
These values are correspondent with the Recovery Rates previous to the market
adjustment


Related Books

Default Recovery Rates in Credit Risk Modeling: A Review ...

Default Recovery Rates in Credit Risk Modeling: A Review ...

Default Recovery Rates in Credit Risk Modeling: A Review of the Literature and Empirical Evidence Edward Altman*, Andrea Resti** and Andrea Sironi*** December 2003 Abstract Evidence from many countries in recent years suggests that collateral values and recovery rates on corporate defaults can be volatile and, moreover, that they tend to go down just when the number of defaults goes up in ...

Continue Reading...
The determinants of bank loan recovery rates - SSRN

The determinants of bank loan recovery rates - SSRN

The determinants of bank loan recovery rates 1. Introduction Recovery rates on defaulted loans play a critical role in credit risk modeling. Under the 2004 Basel Accord (as well as the proposed drafts of Basel III), some global banks are now permitted, and in certain required, to use an “internal rating-based approach” to estimate “loss- given-default.” The recovery rate is measured as ...

Continue Reading...
European Corporate Default and Recovery Rates, 1985-2006

European Corporate Default and Recovery Rates, 1985-2006

European Corporate Default and Recovery Rates, 1985-2006 This report is Moody’s sixth annual study of European corporate bond and loan issuers and their default experience. Broad conclusions include the following: • In 2006, five Moody’s-rated European issuers defaulted: th ree on bonds, one on loans, and one on both bonds and loans ...

Continue Reading...
Default Recovery Rates and LGD in Credit Risk Modeling and ...

Default Recovery Rates and LGD in Credit Risk Modeling and ...

Default Recovery Rates and LGD in Credit Risk Modeling and Practice . Edward I. Altman** Abstract . Evidence from many countries in recent years suggests that collateral values and recovery rates on corporate defaults can be volatile and, moreover, that they tend to go down just when the number of defaults goes up in economic downturns. This link

Continue Reading...
Default and Recovery Rates of European Corporate Bond ...

Default and Recovery Rates of European Corporate Bond ...

Default and Recovery Rates of European Corporate Bond Issuers: 1985-2005 This is Moody's fifth annual study of European corporate bond issuers and their default experience. Broad conclu-sions include: • The downward trend in European corporate defaults continued in 2005 as only two Moody’s-rated issuers defaulted on bonds in 2005.

Continue Reading...
Corporate Default and Recovery Rates, 1920-2009

Corporate Default and Recovery Rates, 1920-2009

3 FEBRUARY 2010 SPECIAL COMMENT: CORPORATE DEFAULT AND RECOVERY RATES, 1920-2009 Introduction Moody’s credit ratings facilitate the efficient functioning of capital markets by providing independent opinions on the creditworthiness of debt obligations issued by corporate issuers around the world.

Continue Reading...
Corporate Default and Recovery Rates, 1920-2006

Corporate Default and Recovery Rates, 1920-2006

Corporate Default and Recovery Rates, 1920-2006 Summary In this report - Moody's 20th annual default study - we update statistics on the default, loss, and rating transition expe-rience of corporate bond and loan issuers for 2006 as well as the historical period since 1920. In summary:

Continue Reading...
What Determines Creditor Recovery Rates?

What Determines Creditor Recovery Rates?

tice, actual recovery rates vary significantly. Moreover, recovery rates are systematically related to default rates. For example, recovery rates on corporate bonds are inversely related to the aggregate corporate default Nada Mora is an economist at the Federal Reserve Bank of Kansas City. This article is

Continue Reading...
SPECIAL COMMENT Corporate Default and Recovery Rates, 1920 ...

SPECIAL COMMENT Corporate Default and Recovery Rates, 1920 ...

GLOBAL CORPORATE FINANCE 3 FEBRUARY 28, 2011 SPECIAL COMMENT: CORPORATE DEFAULT AND RECOVERY RATES, 1920-2010 Similar to what we observed in 2008 and 2009, distressed exchange s still played an active role in 2010,

Continue Reading...
DATA REPORT and Recovery Rates, 1920 - 2017 Annual Default ...

DATA REPORT and Recovery Rates, 1920 - 2017 Annual Default ...

MOODY'S INVESTORS SERVICE CROSS-SECTOR Gas recorded 25 defaults, or 27% of the total count. Retail had the second most defaults at 13, or 14% of the total, as it suffered from

Continue Reading...
Risk Assessment for Banking Systems

Risk Assessment for Banking Systems

inter-bank loans have lower capital requirements than commercial loans, implicitly as-suming that credit risk is lower in the inter-bank market. In our paper we suggest a new methodology to estimate default and recovery rates. Second, regulators are concerned about systemic risk in the banking sector and the possibility of a chain reaction of bank

Continue Reading...