Tag Archives: credit risk

Nero fiddles

This week it’s Syria and Russia, last week it was China. Serious in their own right as these issues are, Donald Trump’s erratic approach to off the cuff policy development is exhausting markets. In the last 60 trading days, the S&P500 has had 9 days over 1% and 12 days below -1%. For above 0.5% and more than -0.5%, the number of days is 21 and 15 respectively! According to an off the record White House insider, “a decision or statement is made by the president, and then the principals come in and tell him we can’t do it” and “when that fails, we reverse engineer a policy process to match whatever the president said”.  We live in some messed up world!

As per this post, the mounting QE withdrawals by Central Banks is having its impact on increased volatility. Credit Suisse’s CEO, Tidjane Thiam, this week said, “the tensions are showing and it’s very hard to imagine where you can get out of a scenario of prolonged extraordinary measures without some kind of, I always use the word ‘trauma’”.

Fortune had an insightful article on the US debt issue last month where they concluded that something has to give. According to an Institute of International Finance report, global debt reached a record $237 trillion in 2017, more than 317% of global GDP with the developed world higher around 380%. According to the Monthly Treasury Statement just released, the US fiscal deficit is on track for the fiscal years (Q4 to Q3 of calendar year) 2018 and 2019 to be $833 billion and $984 billion compared to $666 billion in 2017.

This week also marks the publication of the Congressional Budget Office’s fiscal projections for the US after considering the impact of the Trump tax cuts. The graphs below from the report illustrate the impact they estimate, with the fiscal deficits higher by $1.5 trillion over 10 years. It’s important to note that these estimates assume a relatively benign economic environment over the next 10 years. No recession, for example, over the next 10 years, as assumed by the CBO, would mean a period of nearly 20 years without one! That’s not likely!

The first graph below shows some of the macro-economic assumptions in the CBO report, the second showing the aging profile in the US which determines participation rates in the economy and limits its potential, with the following graphs showing the fiscal estimates.

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The respected author Satyajit Das highlighted in this article how swelling levels of debt will amplify the effect of any rate rises, with higher rates having the following impacts:

  • Increase credit risk. LIBOR has already risen, as per this post, and large sways of corporate debt is driven by LIBOR. This post shows some of debt levels in S&P500 firms, as per the IMF Global Financial Stability report from last April and the graph below tells its own tale.
  • Generate large mark-to-market losses on existing debt holdings. A 1% increase is estimated to impact US government debt by $2 trillion globally.
  • Drive investors away from risky assets such as equity, decimating the now quaint so-called TINA trade (“there is no alternative”).
  • Divert cash to servicing debt, further dampening economic activity and business investment.
  • Restrict the ability of governments to deploy fiscal stimulus.

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Back in the land of Nero, or Trump in our story, his new talking head in chief, Larry Kudlow, recently said the White House would propose a “rescission bill” to strip out $120 billion from nondefense discretionary spending. Getting that one past either the Senate or the House ahead of the November midterm elections is fanciful and just not probable after the elections. So that’s what the Nero of our time is planning in response to our hypothetical Rome burning exasperated by his reckless fiscal policies (and hopefully there wouldn’t be any unjustified actual burning as a result of his ill thought out foreign policies over the coming days and weeks).

Hi there LIBOR

According to this article in the FT by Bhanu Baweja of UBS, the rise in the spread between the dollar 3-month LIBOR, now over 2.25% compared to 1.7% at the start of the year, and the overnight indexed swap (OIS) rate, as per the graph below, is a “red herring” and that “supply is at play here, not rising credit risk”. This view reflects the current market consensus, up until recently at least.

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Baweja argues that the spread widening is due to the increased T-bill-OIS spread because of increased yields due to widening fiscal deficits in the US and to the increased commercial paper (CP) to T-bill spread due to US company repatriations as a result of the Trump tax cuts. Although Baweja lists off the current bull arguments to be cheerful, he does acknowledge that an increasing LIBOR will impact US floating borrowers of $2.2 trillion of debt, half of whom are BB- and below, particularly if 3-month US LIBOR breaks past 3%. Baweja points to rises in term premiums as the real red flags to be looking out for.

Analysts such as Matt Smith of Citi and Jonathan Garner of Morgan Stanley are not as nonchalant as the market consensus as articulated by Baweja. The potential for unintended consequences and/or imbalances in this tightening phase, out of the greatest monetary experiment every undertaken, is on many people’s minds, including mine. I cannot but help think of a pressure cooker with every US rate rise ratcheting the heat higher.

Citi worry that LIBOR may be a 3-month leading indicator for dollar strengthening which may send shock-waves across global risk markets, particularly if FX movements are disorderly. Garner believes that “we’re already looking at a significant tightening of monetary policy in the US and in addition China is tightening monetary policy at the same time and this joint tightening is a key reason why we are so cautious on markets”. Given Chairman Powell’s debut yesterday and the more hawkish tone in relation to 2019 and 2020 tightening, I’ll leave this subject on that note.

The intricacies of credit market movements are not my area of expertise, so I’ll take council on this topic from people who know better.

Eh, help Eddie….what do you think?

Beautiful Models

It has been a while since I posted on dear old Solvency II (here). As highlighted in the previous post on potential losses, the insurance sector is perceived as having robust capital levels that mitigates against the current pricing and investment return headwinds. It is therefore interesting to look at some of detail emerging from the new Solvency II framework in Europe, including the mandatory disclosures in the new Solvency and Financial Condition Report (SFCR).

The June 2017 Financial Stability report from EIOPA, the European insurance regulatory, contains some interesting aggregate data from across the European insurance sector. The graph below shows solvency capital requirement (SCR) ratios, primarily driven by the standard formula, averaging consistently around 200% for non-life, life and composite insurers. The ratio is the regulatory capital requirement, as calculated by a mandated standard formula or a firm’s own internal model, divided by assets excess liabilities (as per Solvency II valuation rules). As the risk profile of each business model would suggest, the variability around the average SCR ratio is largest for the non-life insurers, followed by life insurers, with the least volatile being the composite insurers.

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For some reason, which I can’t completely comprehend, the EIOPA Financial Stability report highlights differences in the SCR breakdown (as per the standard formula, expressed as a % of net basic SCR) across countries, as per the graph below, assumingly due to the different profiles of each country’s insurance sector.

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A review across several SFCRs from the larger European insurers and reinsurers who use internal models to calculate their SCRs highlights the differences in their risk profiles. A health warning on any such comparison should be stressed given the different risk categories and modelling methodologies used by each firm (the varying treatment of asset credit risk or business/operational risk are good examples of the differing approaches). The graph below shows each main risk category as a percentage of the undiversified total SCR.

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By way of putting the internal model components in context, the graph below shows the SCR breakdown as a percentage of total assets (which obviously reflects insurance liabilities and the associated capital held against same). This comparison is also fraught with difficulty as an (re)insurers’ total assets is not necessarily a reliable measure of extreme insurance exposure in the same way as risk weighted assets is for banks (used as the denominator in bank capital ratios). For example, some life insurers can have low insurance related liabilities and associated assets (e.g. for mortality related business) compared to other insurance products (e.g. most non-life exposures).

Notwithstanding that caveat, the graph below shows a marked difference between firms depending upon whether they are a reinsurer or insurer, or whether they are a life, non-life or composite insurer (other items such as retail versus commercial business, local or cross-border, specialty versus homogeneous are also factors).

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Initial reactions by commentators on the insurance sector to the disclosures by European insurers through SFCRs have been mixed. Some have expressed disappointment at the level and consistency of detail being disclosed. Regulators will have their hands full in ensuring that sufficiently robust standards relating to such disclosures are met.

Regulators will also have to ensure a fair and consistent approach across all European jurisdictions is adopted in calculating SCRs, particularly for those calculated using internal models, whilst avoiding the pitfall of forcing everybody to use the same assumptions and methodology. Recent reports suggest that EIOPA is looking for a greater role in approving all internal models across Europe. Systemic model risk under the proposed Basel II banking regulatory rules published in 2004 is arguably one of the contributors to the financial crisis.

Only time will tell if Solvency II has avoided the mistakes of Basel II in the handling of such beautiful models.

Divine Diversification

There have been some interesting developments in the US insurance sector on the issue of systemically important financial institutions (SIFIs). Metlife announced plans to separate some of their US life retail units to avoid the designation whilst shareholder pressure is mounting on AIG to do the same. These events are symptoms of global regulations designed to address the “too big to fail” issue through higher capital requirements. It is interesting however that these regulations are having an impact in the insurance sector rather than the more impactful issue within the banking sector (this may have to do with the situation where the larger banks will retain their SIFI status unless the splits are significant).

The developments also fly in the face of the risk management argument articulated by the insurance industry that diversification is the answer to the ills of failure. This is the case AIG are arguing to counter calls for a breakup. Indeed, the industry uses the diversification of risk in their defences against the sector being deemed of systemic import, as the exhibit below from a report on systemic risk in insurance from an industry group, the Geneva Association, in 2010 illustrates. Although the point is often laboured by the insurance sector (there still remains important correlations between each of the risk types), the graph does make a valid point.

click to enlargeEconomic Capital Breakdown for European Banks and Insurers

The 1st of January this year marked the introduction of the new Solvency II regulatory regime for insurers in Europe, some 15 years after work begun on the new regime. The new risk based solvency regime allows insurers to use their own internal models to calculate their required capital and to direct their risk management framework. A flurry of internal model approvals by EU regulators were announced in the run-up to the new year, although the amount of approvals was far short of that anticipated in the years running up to January 2016. There will no doubt be some messy teething issues as the new regime is introduced. In a recent post, I highlighted the hoped for increased disclosures from European insurers on their risk profiles which will result from Solvency II. It is interesting that Fitch came out his week and stated that “Solvency II metrics are not comparable between insurers due to their different calculation approaches and will therefore not be a direct driver of ratings” citing issues such as the application of transitional measures and different regulator approaches to internal model approvals.

I have written many times on the dangers of overtly generous diversification benefits (here, here, here, and here are just a few!) and this post continues that theme. A number of the large European insurers have already published details of their internal model calculations in annual reports, investor and analyst presentations. The graphic below shows the results from 3 large insurers and 3 large reinsurers which again illustrate the point on diversification between risk types.

click to enlargeInternal Model Breakdown for European Insurers and Reinsurers

The reinsurers show, as one would expect, the largest diversification benefit between risk types (remember there is also significant diversification benefits assumed within risk types, more on that later) ranging from 35% to 40%. The insurers, depending upon business mix, only show between 20% and 30% diversification across risk types. The impact of tax offsets is also interesting with one reinsurer claiming a further 17% benefit! A caveat on these figures is needed, as Fitch points out; as different firms use differing terminology and methodology (credit risk is a good example of significant differences). I compared the diversification benefits assumed by these firms against what the figure would be using the standard formula correlation matrix and the correlations assuming total independence between the risk types (e.g. square root of the sum of squares), as below.

click to enlargeDiversification Levels within European Insurers and Reinsurers

What can be seen clearly is that many of these firms, using their own internal models, are assuming diversification benefits roughly equal to that between those in the standard formula and those if the risk types were totally independent. I also included the diversification levels if 10% and 25% correlations were added to the correlation matrix in the standard formula. A valid question for these firms by investors is whether they are being overgenerous on their assumed diversification. The closer to total independence they are, the more sceptical I would be!

Assumed diversification within each risk type can also be material. Although I can understand arguments on underwriting risk types given different portfolio mixes, it is hard to understand the levels assumed within market risk, as the graph below on the disclosed figures from two firms show. Its hard for individual firms to argue they have material differing expectations of the interaction between interest rates, spreads, property, FX or equities!

click to enlargeDiversification Levels within Market Risk

Diversification within the life underwriting risk module can also be significant (e.g. 40% to 50%) particularly where firms write significant mortality and longevity type exposures. Within the non-life underwriting risk module, diversification between the premium, reserving and catastrophe risks also add-up. The correlations in the standard formula on diversification between business classes vary between 25% and 50%.

By way of a thought experiment, I constructed a non-life portfolio made up of five business classes (X1 to X5) with varying risk profiles (each class set with a return on equity expectation of between 10% and 12% at a capital level of 1 in 500 or 99.8% confidence level for each), as the graph below shows. Although many aggregate profiles may reflect ROEs of 10% to 12%, in my view, business classes in the current market are likely to have a more skewed profile around that range.

click to enlargeSample Insurance Portfolio Profile

I then aggregated the business classes at varying correlations (simple point correlations in the random variable generator before the imposition of the differing distributions) and added a net expense load of 5% across the portfolio (bringing the expected combined ratio from 90% to 95% for the portfolio). The different resulting portfolio ROEs for the different correlation levels shows the impact of each assumption, as below.

click to enlargePortfolio Risk Profile various correlations

The experiment shows that a reasonably diverse portfolio that can be expected to produce a risk adjusted ROE of between 14% and 12% (again at a 1 in 500 level)with correlations assumed at between 25% and 50% amongst the underlying business classes. If however, the correlations are between 75% and 100% then the same portfolio is only producing risk adjusted ROEs of between 10% and 4%.

As correlations tend to increase dramatically in stress situations, it highlights the dangers of overtly generous diversification assumptions and for me it illustrates the need to be wary of firms that claim divine diversification.

Inhibiting Derivatives

The array and complexity of new financial regulation in response to the financial crisis can have unforeseen impacts. A reduction in the liquidity of the bond markets today compared to before the crisis is commonly explained as a result of increased regulation of the banking sector.

A report by International Organization of Securities Commissions (IOSCO) in 2013 highlighted the impact of the regulatory push, following a G20 direction in 2009, for the OTC derivatives markets to be cleared through central counterparties (CCPs), thereby creating a potential for systemic counterparty risk (as per this post). The idea was to provide a centralised clearing point per asset class with the goal of increasing transparency and providing regulators with consistent data across borders to monitor.

The reality today is somewhat different that the theory. Many competing repositories have sprung up with the commercial intend of leveraging the valuable data. David Wright, the Secretary General of IOSCO, recently stated “we’ve got 25 of these beasts today and they don’t talk to each other, so a basic fundamental trawl of transparency is actually missing”. Regulators are stressing the need for further reform so that data can be aggregated to improve monitoring and, in February, issued requirements on CCPs to disclose information on topics such as the size of their credit risk, liquidity risk, collateral, margins, business risk, custody, and investment risks

Benoît Cœuré, a member of the Executive Board of the ECB, said in a speech this month that “the gross notional outstanding amount of centrally cleared positions was estimated at $169 trillion for OTC interest rate derivatives, and at $14 trillion for credit derivatives. The sheer magnitude of these figures (around ten times the GDP of the United States or European Union) gives us an idea of the severity of the potential consequences from a stress event at a major global CCP”.

Cœuré outlined a number of options for strengthening the financial resilience of CCPs including increased regulatory capital, initial margin haircutting, setting up cross-CCP resolution funds or a central resolution fund. Any such measures would have to be consistently applied across jurisdictions to ensure fairness and designed so as not to provide a disincentive to using CCPs.

In March, the Bank of International Settlements (BIS) and IOSCO announced a delay until September 2016 for the introduction of margin requirements for non-centrally cleared derivatives (above certain thresholds and subject to exemptions). The proposed margin requirements are split between initial and variable, with the initial margin phased in from September 2016 to September 2020 and the variation margin phased in from September 2016 to March 2017.

The amount of initial margin reflects the size of the potential future exposure calculated “to reflect an extreme but plausible estimate of an increase in the value of the instrument that is consistent with a one-tailed 99 per cent confidence interval over a 10-day horizon, based on historical data that incorporates a period of significant financial stress”. The required amount of initial margin is calculated by reference to either a quantitative portfolio margin model or a standardised margin schedule (as per the schedule below). The requirements also prohibit the re-hypothecation of initial margin required to be collected and posted under the rules.

click to enlargeInitial Margin for Derivatives

The amount of variation margin reflects the size of this current exposure dependent on the mark-to-market value of the derivatives at any point in time. As such, the volatility of this requirement may be significant in stressed cases, particularly for illiquid derivatives.

The proposals, as set out by the BIS and IOSCO, are ambitious and it will be interesting to see how they are enforced across jurisdictions and the impact they will have on market behaviour, both within and outside CCPs. I suspect there will be a few twists in this tale yet, particularly in relation to unintended consequences of trying to tame the derivative monster.