Naive modeling of credit defaults using a Markov Random Field¶
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This notebook is the adaptation to pyAgrum of the model proposed by Gautier Marti which is itself inspired by the paper Graphical Models for Correlated Defaults (adaptation by Marvin Lasserre and Pierre-Henri Wuillemin).
import numpy as np
import matplotlib.pyplot as plt
import pyAgrum as gum
import pyAgrum.lib.notebook as gnb
import pyAgrum.lib.mrf2graph as m2g
Constructing the model¶
Three sectors co-dependent in their stress state are modelled: (Finance, Energy, Retail).
For each of these sectors, we consider a universe of three issuers.
| Within Finance | Within Energy | Within Retail |
|---|---|---|
| Deutsche Bank | EDF | New Look |
| Metro Bank | Petrobras | Matalan |
| Barclays | EnQuest | Marks & Spencer |
The probability of default of these issuers is partly idiosyncratic and partly depending on the stress within their respective sectors. Some distressed names such as New Look can have a high marginal probability of default, so high that the state of their sector (normal or stressed) does not matter much. Some other high-yield risky names such as Petrobras may strongly depend on how the whole energy sector is doing. On the other hand, a company such as EDF should be quite robust, and would require a very acute and persistent sector stress to move its default probability significantly higher.
We can encode this basic knowledge in terms of potentials:
Conventions:
Sector variables can be either in the state no stress or in the state under stress while issuer variables can be either in the state no default or in the state default.
The choice of these potentials is not an easy task; It can be driven by historical fundamental or statistical relationships or forward-looking views.
Given a network structure, and a set of potentials, one can mechanically do inference using the pyAgrum library.
We will show how to obtain a distribution of the number of defaults (and the expected number of defaults), a distribution of the losses (and the expected loss). From the joint probability table, we could also extract the default correlation
tl;dr We use here the library pyAgrum to illustrate, on a toy example, how one can use a Markov Random Field (MRF) for modelling the distribution of defaults and losses in a credit portfolio.
Building accurate PGM/MRF models require expert knowledge for considering a relevant graph structure, and attributing useful potentials. For this toy model, we use the following structure:
The first step consists in creating the model.
For that, we use the pyAgrum class pyAgrum.MarkovRandomField.
# building nodes (with types)
mn = gum.MarkovRandomField('Credit default modeling')
# Adding sector variables
sectors = ['Finance', 'Energy', 'Retail']
finance, energy, retail = [mn.add(gum.LabelizedVariable(name, '', ['no stress', 'under stress']))
for name in sectors]
# Adding issuer variables
edf, petro, eq = [mn.add(gum.LabelizedVariable(name, '', ['no default', 'default']))
for name in ['EDF','Petrobras','EnQuest']]
mb, db, ba = [mn.add(gum.LabelizedVariable(name, '', ['no default', 'default']))
for name in ['Metro Bank', 'Deutsche Bank', 'Barclays']]
nl, matalan, ms = [mn.add(gum.LabelizedVariable(name, '', ['no default', 'default']))
for name in ['New Look', 'Matalan', 'Marks & Spencer']]
# Adding and filling factors between sectors
mn.addFactor([finance, energy])[:] = [[90, 70],
[60, 10]]
mn.addFactor([finance, retail])[:] = [[80, 10],
[30, 80]]
mn.addFactor([energy, retail])[:] = [[60, 5],
[70, 95]]
# Adding factors between sector and issuer
mn.addFactor([db, finance])[:] = [[90, 30],
[10, 60]]
mn.addFactor([mb, finance])[:] = [[80, 40],
[ 5, 60]]
mn.addFactor([ba, finance])[:] = [[90, 20],
[20, 50]]
mn.addFactor([edf, energy])[:] = [[90, 5],
[80, 40]]
mn.addFactor([petro, energy])[:] = [[60, 50],
[ 5, 60]]
mn.addFactor([eq, energy])[:] = [[80, 20],
[10, 50]]
mn.addFactor([nl, retail])[:] = [[ 5, 60],
[ 2, 90]]
mn.addFactor([matalan, retail])[:] = [[40, 30],
[20, 70]]
mn.addFactor([ms, retail])[:] = [[80, 10],
[30, 50]]
nodetypes={n:0.1 if n in sectors else
0.2 for n in mn.names()}
gnb.sideBySide(gnb.getMRF(mn,view='graph',nodeColor=nodetypes),
gnb.getMRF(mn,view='factorgraph',nodeColor=nodetypes),
captions=['The model as a Markov random field','The model as a factor graph'])
gnb.sideBySide(mn.factor({'Energy', 'Finance'}),
mn.factor({'Finance', 'Retail'}),
mn.factor({'Energy', 'Retail'}),
mn.factor({'Deutsche Bank', 'Finance'}),
mn.factor({'Metro Bank', 'Finance'}),
mn.factor({'Barclays', 'Finance'}),
mn.factor({'EDF', 'Energy'}),
mn.factor({'Petrobras', 'Energy'}),
mn.factor({'EnQuest', 'Energy'}),
mn.factor({'New Look', 'Retail'}),
mn.factor({'Matalan', 'Retail'}),
mn.factor({'Marks & Spencer', 'Retail'}),
ncols=3)
Making inferences¶
Example 1 & 2¶
The first two examples consists in finding the chance of EDF to default with no evidence and knowing that the energy sector is not under stress. For that, we can use the getPosterior method:
gnb.sideBySide(gum.getPosterior(mn, target='EDF', evs={}),
gum.getPosterior(mn, target='EDF', evs={'Energy':'no stress'}),
captions=['$P(EDF)$','$P(EDF|Energy=$under stress$)$'])
Hence, if we cannot observe the state of the energy sector, then EDF has a higher chance of defaulting (9%) as it is possible that the energy sector is under stress.
Example 3 & 4¶
Given that Marks & Spencer (a much safer name) has defaulted, New Look has now an increased chance of defaulting: 97%.
Even if the retail sector is doing ok, New Look is a distressed name with a high probability of default (92%).
gnb.sideBySide(gum.getPosterior(mn, target='New Look', evs={'Marks & Spencer': 'default'}),
gum.getPosterior(mn, target='New Look', evs={'Retail':'no stress'}),
captions=['P(New Look|Marks & Spencer=default)',
'P(New Look | Retail = no stress)'])
pyAgrum offers the option to vizualise the marginal probability of each variable using showInference.
# gum.config['factorgraph','edge_length_inference']=1.
gnb.showInference(mn, size='7!',nodeColor=nodetypes)
Impact of defaults on a credit portfolio and the distribution of losses¶
Amongst the 9 available issuers, let’s consider the following portfolio containing bonds from these 6 issuers:
portfolio_names = ['EDF', 'Petrobras', 'EnQuest', 'Matalan', 'Barclays']
portfolio_exposures = [2e6, 400e3, 300e3, 600e3, 3e6]
LGD = 0.9
losses = {portfolio_names[i]:(portfolio_exposures[i]*LGD) for i in range(len(portfolio_names))}
print('** Total notional: USD {} MM'.format(sum(portfolio_exposures)/1e6))
** Total notional: USD 6.3 MM
We have a USD 6.3MM total exposure to these names (notional).
We assume that in case of default, we recover only 10% (LGD = 90%).
Let’s start using the model:
ie = gum.ShaferShenoyMRFInference(mn)
ie.makeInference()
p=ie.jointPosterior(set(sectors))
print(f'** There is {100*p[{"Retail":0,"Energy":0,"Finance":0}]:.2f}% chance that no sector in under stress.')
print()
p
** There is 42.38% chance that no sector in under stress.
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|---|---|---|---|
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| 0.4238 | 0.0105 | |
| 0.0083 | 0.0000 | ||
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| 0.2999 | 0.1215 | |
| 0.1251 | 0.0109 | ||
If we observe that the finance sector is doing ok, and that Marks & Spencer is still doing business as usual, then this is the current joint probabilities associated to the potential defaults in the portfolio:
ie = gum.ShaferShenoyMRFInference(mn)
ie.addJointTarget(set(portfolio_names))
ie.setEvidence({'Finance': 'no stress', 'Marks & Spencer': 'no default'})
ie.makeInference()
portfolio_posterior = ie.jointPosterior(set(portfolio_names))
Note that the line ie.addJointTarget(set(portfolio_names)) is important here because adding hard evidences removes the corresponding nodes in the graph and then no factor containing the variables in portfolio_names can be found. The method addJointTarget ensure that such a factor exists.
portfolio_posterior
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| 0.1495 | 0.0332 | |
| 0.1552 | 0.0345 | ||||
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| 0.0383 | 0.0085 | |||
| 0.0412 | 0.0092 | ||||
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| 0.1268 | 0.0282 | ||
| 0.1350 | 0.0300 | ||||
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| 0.0431 | 0.0096 | |||
| 0.0627 | 0.0139 | ||||
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| 0.0084 | 0.0019 | |
| 0.0088 | 0.0020 | ||||
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| 0.0026 | 0.0006 | |||
| 0.0034 | 0.0008 | ||||
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| 0.0081 | 0.0018 | ||
| 0.0102 | 0.0023 | ||||
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| 0.0077 | 0.0017 | |||
| 0.0170 | 0.0038 | ||||
We now want to compute the distribution of the number of defaults.
For that, we create a new Markov random field containing an additional node Number of defaults connected to each issuers:
mn2 = gum.MarkovRandomField(mn)
mn2.add(gum.RangeVariable('Number of defaults', '', 0, len(portfolio_names)))
factor = mn2.addFactor(['Number of defaults',*portfolio_names]).fillWithFunction('+'.join(portfolio_names))
gum.config['factorgraph','edge_length']=1.1
nodetypes={n:0.1 if n in sectors else
0.3 if "Number of defaults"==n else
0.2 for n in mn2.names()}
gnb.flow.add(gnb.getMRF(mn2, view="graph",size='7!',nodeColor=nodetypes))
gnb.flow.add(gnb.getMRF(mn2, view="factorgraph",size='7!',nodeColor=nodetypes))
gnb.flow.display()
Once this new network is created, we can obtain the distribution of Number of defaults using getPosterior :
ndefault_posterior = gum.getPosterior(mn2, target='Number of defaults',
evs={'Finance': 'no stress', 'Marks & Spencer': 'no default'})
ndefault_posterior
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| 0.1495 | 0.3620 | 0.3119 | 0.1371 | 0.0357 | 0.0038 |
fig, ax = plt.subplots()
l = ndefault_posterior.tolist()
ax.bar(range(len(l)), l)
ax.set_xlabel('Number of defaults')
ax.set_title('Distribution of the number of defaults in the portfolio')
plt.show()