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Putting all things together : how is the Loss Given Default – LGD – of my credit portfolio affected by the domino effect ?

In the Eurovision LGD Contest post Algosave shows that BP plc forward-looking Loss Given Default (LGDs) are the highest among 10 western major integrated oil and gas producers.

In the Credit Portfolio domino effect post, Algosave Fintech also delivered powerful insights into issues of credit dependencies, for instance by showing which of those 10 corporates was the least “toxic” in the portfolio.

Putting all things together, and putting Algosave FinTech to the test : when another corporate of the portfolio defaults, (1) what happens to BP relatively high LGDs, and (2) is BP – then – in a high, average or low LGD regime ?

For instance, let’s focus on what happens to BP LGDs if Chevron was to default. Why Chevron ? because it is the “toxiest” corporate for BP plc. Indeed, If Chevron was to default, Algosave Fintech shows that BP 5year PD would jumps from its current 3.5% to 11.8% = more than 3.7 times. Whereas, BP 5year PD only jumps 2.2 times on average with the other 8 corporates of the portfolio.

First, let’s remind ourselves how BP 5year unsecured LGD distribution looks like (this distribution can be found in the EUROVISION LGD Contest article).

Now the first good piece of news and the answer to the first challenge : what happens to BP LGD distribution when Chevron defaults ?

  • Algosave FinTech shows that BP median 5-year unsecured conditional LGD (if Chevron was to default) is 8% lower that BP “standalone” LGDs.

Hence, also the answer to the second question : when Chevron defaults, is BP is a low, average or high LGD regime ?

  • Algosave FinTech shows that upon Chevron default, if BP was to also to default, it would – on average – be in a relatively lower LGD regime.
  • the BP LGD “standalone” and “conditional” 5-year unsecured LGD distribution shows exactly that.
  • In red (and on the left hand y-axis) BP “standalone” LGDs, more centered around its mean. In blue (and with the right hand y-axis) BP “conditional” LGDs (if Chevron defaults), with a larger left-hand tail, illustrating the lower LGD regime if BP was to go bankrupt as well.

Algosave can address these complex challenges thanks to the high granularity of its big data universe. Also, current credit model are either calibrated on Equity Capitalization (Option-type models) or on Bond/CDS (reduced form models). Algosave model takes it all : it is both calibrated on Equity as well as on bond spread data (when available). Departing from “thru the cycle” ratings, this gives Algosave FinTech its refreshing “live” and Point in Time flavor.

All the best. Algosave Team – 01/06/2018

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Portfolio management critical question : how severe is the “domino effect” in my credit portfolio ?

Remember Algosave last LinkedIn Article : “Eurovision LGD Contest” ?

We then examined Point-in-Time (PiT) and forward looking LGDs of a sample portfolio of 7 Western oil majors : BP Plc., Exxon Mobil, Royal Dutch Shell, Total SA, Husky Energy, Chevron and ENI Spa.

Algosave unique modelling technology, – surprisingly – revealed that Husky Energy – the lowest credit rating – offered the lowest LGDs. On the other hand, BP plc. was also – and surprisingly – “disappointing”, with the highest and the most scenario-sensitive senior unsecured LGD.

This Algosave exclusive BP dashboard summarizes those findings (right click “Open image in new tab” for a better view) :

By the way, the same Algosave dashboard is available on demand for each of those corporates. Do not hesitate to ask.

Let’s go one step further into portfolio management and look into some core issues of credit dependencies : Domino Effect <=> from single borrower management (single PD) to portfolio management (conditional PD)

Let’s define a “Domino Effect” as being the sensitivity of a given borrower PD to the default of another borrower.

The same data granularity and “real-life” rich universe of possibilities which help ALGOSAVE deliver unique insights into Loss Given Default, are also instrumental in Algosave FinTech delivering powerful insights into issues of credit dependencies for portfolio and risk managers alike.

First, and to make things crispier, let’s enlarge the portfolio to also include Statoil, Repsol and Suncor Energy and ask how severe is the domino effect – a.k.a Conditional Default Probability – in this sample portfolio of 10 oil majors ?

Portfolio = BP Plc., Exxon Mobil, Royal Dutch Shell, Total SA, Husky Energy, Chevron, ENI Spa, Statoil, Repsol and Suncor Energy.

Here is Algosave full insight into issues of credit dependencies produced from its database, with Algosave unique conditional default probabilities and toxicity score (right click “Open image in new tab” for a better view).

Let’s read thru those Algosave insights into portfolio domino effect with a few examples :

For instance, let’s start simple. If Total was to default, how would Statoil 5-year cumulative PD be affected ?

  • Algosave answer : it will jumps from its current 1.3% to 2.3% if FP (Total SA) defaulted.

For instance, knowing that Total SA current 5-year cumulative PD is 2.2%, will this PD jump – on average – to 4%, 6% or 8% if one the other 9 corporates defaults over the next 5 years ?

  • ALGOSAVE answer : on average, Total SA 5-year PD will jump from 2.2% to 5.6% if one of the other 9 oil majors defaults on its debt. And, this average hides a broad range of possibilities, whereby some corporate defaults are more friendly while some are far more “toxic” to Total SA.

If so, which of the other 9 corporates is the “friendliest” and which is the most “toxic” for Total ? and does it have to do with credit rating ?

  • ALGOSAVE answer : the friendliest is ENI. Indeed, if ENI was to default, Total SA 5-year PD will hardly move from its current 2.2% to 2.3%.
  • ALGOSAVE answer : the most toxic is Chevron. Indeed, If Chevron was to default, Total SA 5-year PD will jump from its current 2.2% to 9% (more than 4 times !)
  • And yes, this is somehow related to Credit Rating, but in a “reversed” way. Indeed, since Chevron enjoys one of the highest credit rating of the portfolio, it is logical that if it were to default, it would very likely do so on the grounds of systemic/industry risk. Hence a higher degree of toxicity. Inversely, if ENI – one of the lowest rating of the sample – was to default, it would probably have to do more with idiosyncratic risk. Hence a lower level of toxicity.

Which of those 10 oil majors are the 2 least and the 2 most exposed to domino effect (significant jump in their respective PD upon default of others) over the next 5 Years ?

  • ALGOSAVE answer : the two least exposed to domino effect are ENI and Statoil. On average, and upon default of one of the other 9 corporates, ENI 5-year cumulative PD will move up 1.5 times, while Statoil PD will move up 1.6 times.
  • ALGOSAVE answer : the two most exposed to domino effect are Exxon Mobil and Chevron. On average, and upon default of one of the other 9 corporates, Exxon 5-year cumulative PD will move up 2.4 times, and Chevron will shoot up 2.9 times !

Finally, and assuming I want to increase my exposure to this sample portfolio with a 5-year unsecured Revolver Credit Facility – which is the best and which is the worst candidate from a portfolio management perspective ?

  • In order to answer this question, Algosave assigns to each borrower a toxicity score which corresponds to the borrower level of toxicity for each of its peer.Score = 1, being the least toxic (i.e. causing the least increase in PD) andScore = 9 being the most toxic (i.e. causing the greatest increase in PD)
  • ALGOSAVE answer : the best candidate for an increased exposure is ENI with an average toxicity score of 1.9. The second best is Statoil with an average toxicity score of 3.
  • ALGOSAVE answer : the worst candidate is Chevron with an average toxicity score of 8.3 ! The second worse being Exxon with a average toxicity score of 7.

Now, your turn to put Algosave FinTech to the test with your own challenging questions.

  • Example of challenge for Algosave : when this domino effect happens, what is the probability of being in a low, average or high LGD regime ?
  • Another challenge for Algosave : from a domino effect point-of-view, which is the most and which is the least “toxic” industry for this oil basket ?

All the Best

Algosave Team, 30-May-2018

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EUROVISION LGD Contest : When Loss Given Default – LGDs – becomes an unfair competitive advantage

Fancy another Eurovision vote for this week ?

Imagine your bank is at the forefront of energy financing. Its clients are the majors of this world : BP, Exxon, Royal Dutch Shell, Total and also Husky Energy, Chevron and ENI Spa.

Which of those 7 giants deserves your 12-points vote for its rock-bottom-low 15% LossGiven Default (in every economic scenario) on all the following 3 credit exposures :

  • a 10 year secured Revolver Credit Facility,
  • a 10-year senior-unsecured bond,
  • a one-year subordinated CVA charge.

Here are a few – misleading 🙂 – pieces of information to help you give your 12-points :

Ok…you are probably – and naturally – thinking EXXON, with its highest credit rating and lowest Net Debt to EV ratio. Well, in bad macro-economic scenario, EXXON 10-year senior unsecured bond will cost its capital providers a heavy LGD : as you can see on the here-under candlestick chart, 50% of EXXON 10-year unsecured LGD distribution stands between 15% and 31%.

Let’s stick to credit rating – for what it is worth – and examine Chevron. Chevron unsecured LGD in a bad economic scenario, is unsurprisingly worse than EXXON : 50% of Chevron 10-year unsecured LGD distribution stands between 15% and 40% with a median at 29%.

Ditto for Royal Dutch Shell which shows very similar 10-year unsecured LGD distribution albeit with a slightly lower median at 20%

Surprisingly – or not – things are getting worse both for TOTAL as well as for BP. Indeed, 50% of TOTAL 10-year unsecured LGD distribution stands between 29% and 43% with a median at 36% in bad macro-economic scenario.

Now, watch-out for BP 5-year unsecured LGD distribution which is worse even in the OECD concensus macro economic scenario ! Indeed, 50% of BP 5-year unsecured LGD distribution stands between 43% and 50% with a median at 46% even in such a benign macro scenario.

As a side note, and admittedly, all those LGDs are still far lower than standard CDS-market 60% LGD. Already offering an unfair competitive advantage for anyone pricing a credit exposure.

As another side note, the next box-chart will probably raise a few eyebrows if BP was to ask for a subordinated unsecured credit line. Indeed in almost all cases, BP subordinated LGD stands at a solid 100%. Now, that is even higher that the standard CDS-market 75% LGD for subordinated credit exposure. Except in very good macro-economic scenarios (the green bars).

Interestingly – and despite its lower credit rating – ENI’s LGDs are relatively low : they resemble those of EXXON in all macro economic scenarios.

But, of course, ENI forward looking PD curve are a lot different and would push any RAROC or ECL computation higher than those of EXXON

Now the answer for your vote ? The only corporate among those 7 majors which will always show rock-bottom-low 15% LGD in the here-above 3 credit exposure is : Husky Energy, the lowest credit rating of all.

So, 12 points for Husky Energy, and let’s all sing “O, Canada our home and native land…”

By the way, with such low LGDs, it is hardly surprising to see that Husky Energy IFRS9 12-month ECL is 22% lower than that of Total. Even with 3 notches difference in credit rating.

Having this information is real unfair competitive advantage, isn’t it ?

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Normal asset value return distribution : the normal tree that hides the risky forest

First the not-so-good piece of news.

By construction, most existing credit models assume normality of asset-return distribution in order to be able to compute their Distance to Default and other related credit attributes.

But do we really leave in a normal world ? No comment…

What if an innovative and sophisticated structural credit model could provide all borrower credit attributes based on real life asset return distribution ?

That is exactly what we have achieved at Algosave.

For instance, let’s examine Algosave latest forward-looking asset return distribution for a high grade global human resource and employment corporate borrower. Any hint of normality ? NOPE.

Now : the good piece of news.

This is the exact universe which Algosave FinTech uses in order to deliver all borrowers’ forward-looking credit attributes : multi-year, Point-in-Time, and scenario sensitive PDs and LGDs. Those are therefore based on a far richer and real-life set of possible scenarios than most current credit models.

Ditto for the here-above corresponding Asset Value distribution. Watch the fat – fat – tail !

The many – many – tails we see on those distributions are hidden away from you in most existing credit models. And, unfortunately, that is also – and precisely – where the “action” may happen. But, for its clients, Algosave FinTech goes the extra-mile and shows the unseen risk : the risky forest behind the normal tree.

As a courtesy, should you want to see all Algosave deliverables – which of course, include those ones – on 1 or 2 specific borrowers, do not hesitate to leave me a note – here in the comments. We will send them to you with pleasure.

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From IAS39 historical statistic data to Point in Time Expected Credit Losses : when the world shifted from Google Maps to Waze.

My dear friend Gregory is an experienced and talented Geo-location IT engineer as well as an amazing story-teller. I would like to share with you what he told me about IFRS 9  and CECL regulations because he gave those accounting tsunamis a palatable touch-and-feel description.

First, a bit of history. Some of us recall using Google Maps to find a place ?  It was very efficient. Especially in Tokyo with its complicated house numbers. It almost replaced the policeman stationed a the corner of Omotesando Dori.

But when it came to real-time traffic, no comment.

Indeed, until Google purchased Waze, its traffic algorithm was based on statistics whereby historical records of what happened on Finchley road last year on Early May Bank Holiday will appear on your screen and dictate your journey to Covent Garden. That is, if you leave in NW…something. And since we – human – have a bizarre tendency of doing things differently – which proves that we are not as predictable as monkeys – you and I inevitably hit traffic. All together. And spent our beautiful morning on the roads with the kids getting wild in the backseats.

Until Waze brought free and live traffic data to our smartphones. And the rest is history.

Well; that is exactly what the accounting regulators have in mind with IFRS 9  and ALLL CECL regulations. When provisioning for potential credit losses in their loan and bond portfolio, Gregory told me that the regulators want Financial Institutions to give out a Waze and no longer a old Google Map to its many stakeholders.

And Yes. Those Wazes – not pronouncable – will change direction according to what is happening on the roads – in the economy – real-time – Point In Time. It will make our trips – investments – sometime unexpected – more volatile – especially when its takes us thru remote roads – uncharted territories.

But boy, have you ever noticed how accurate Waze is in timing arrivals. We exactly know how long it will take to go Covent Garden next week the second we board our car. And if something happens on the road we will all be updated – real time. And with driver-less cars and other new disruptive technologies coming on board – literally – Waze will become more efficient and indispensable.

Statistics and historical data are useful but Point In Time and accurate financial information will become more and more relevant and indispensable in rapidly changing, AI / IoT hungry economies. So, kudos to the accounting regulators who – this time – are far ahead of everyone else in many “Waze”.