Understanding the Basics: Banks vs Insurers

The classic banking model of liquidity risk is straightforward: banks transform short-term deposits into long-term loans. This maturity transformation creates inherent liquidity risk – even a perfectly solvent bank can face a crisis if too many depositors demand their money simultaneously. This fundamental risk drives the existence of central banks as lenders of last resort.

Insurance traditionally operated differently. With predictable claims patterns, regular premium income and unoptimised balance sheets, insurers weren’t thought to face significant liquidity risks. However, modern insurance practices and product designs have created more complex liquidity dynamics that challenge traditional frameworks. These liquidity-risk-increasing practices include some risk management choices (hedging and use of derivatives) and balance sheet sweating.

Sources of Liquidity Risk for Insurers

Insurance companies face several distinct sources of liquidity risk, some traditional and others emerging from modern practices:

Derivatives and Modern Asset Management

Modern investment strategies create significant liquidity demands:

• Use of illiquid assets through debt origination, greater use of corporate paper in general to provide higher yields for annuities and guaranteed/fixed bond products, private equity and other alternatives seeking additional yield
• Variation margin calls on derivatives require immediate cash as markets move
• Derivative roll risk creates periodic liquidity needs
• Rolling medium term corporate paper maturities into new instruments has elements of liquidity risk as part of the broader roll-risk universe
• Repo arrangements require careful liquidity management
• Hedging programs, while reducing other risks, increase liquidity demands

Policy Surrenders and Lapses

The liquidity impact of surrenders and lapses varies significantly by product type:

• Savings policies backed by liquid assets present limited liquidity risk
• Corporate policies often include liquidation notice periods
• Market value adjustments can share losses with policyholders
• Risk policies with negative liabilities create complex dynamics – while lapse might improve solvency ratios, the loss of positive cash flows can create future liquidity strains
• Loss of shareholder value is still likely the major risk for lapses and surrenders – and as a result it usually gets plenty of attention without the liquidity risk lens.

Internal Hedging and Optimisation

Insurance liquidity isn’t just about having assets to meet claims. Insurers often use positive cash flows from some policies (particularly risk policies with negative liabilities) to fund claims on other, especially older or maturing policies. This practice, while potentially efficient in normal times, creates hidden liquidity risks.

If these positive cash flows diminish (through lapses or reduced new business), the liquidity characteristics of the underlying assets become crucial. An insurer might appear to have strong liquidity based on expected premium inflows, but this can quickly change if those inflows reduce or stop.

Further, using negative liabilities (from profitable, early duration risk policies) to match positive ones (e.g., guaranteed savings products) creates hidden liquidity risk. This practice is another example of the “improvement” of an old, “lazy” matching approach that missed this opportunity for internal hedging, but perhaps reduces implicit buffers we may have come to rely on.

Claims Concentration

Sudden spikes in claims can create liquidity pressure:

• Natural catastrophes affecting property insurance
• Pandemic-related death claims
• Industrial accident clusters
• Legal or regulatory changes triggering multiple claims

Throughout these claim concentration risks, the performance of reinsurance and cash timing is also critical.

Premium Collection Disruption

Disruption to premium income can occur through:

• Economic downturns affecting customer ability to pay
• Operational disruptions to collection processes (South Africa experienced this a few years ago with the failure of a notable, concentrated exposure to a single premium collector)

Investment Portfolio Liquidity

Asset liquidity can become constrained through:

• Property/Real Estate holdings requiring time to sell
• Private equity/debt with limited secondary markets
• Complex structured products becoming illiquid in stress scenarios
• Market-wide liquidity stress affecting even traditionally liquid assets
• Money market fund holdings proving less liquid than assumed when stressed

Regulatory plans for improved liquidity risk management and reporting for insurers

Regulators are understandably keen to get a better handle on liquidity risk within the insurance sector – and are keen for insurers to take liquidity risk more seriously. Existing measures are widely considered imperfect (at best).

While we don’t want perfect to be the enemy of the good, there seems to be an opportunity to aim for better than current proposals.

The High-Quality Liquid Assets (HQLA) Paradox

A crucial distinction between banks and insurers lies in their access to central bank facilities. Banks can convert HQLA to cash via central bank discount windows, making these assets effectively cash equivalents. Insurers, lacking this access, face a different reality: even “highly liquid” assets can become illiquid during market stress. Insurers and other non-bank financial institutions may want access to the discount window, but my understanding is that this idea is a non-starter.

This creates an interesting regulatory paradox. Bank-style liquidity reporting, with its focus on monthly reporting, micro bucketing of asset maturities, but with implicit and assumptions about central bank access, may be suboptimal for insurers. Yet some regulatory frameworks still look to apply bank-centric thinking to insurer liquidity management.

Systemic Risk and Money Market Funds

A particular concern arises with money market funds, often assumed to be perfectly liquid. While an individual investor can usually liquidate their money market holdings easily, this isn’t true for the market as a whole. If the underlying instruments become illiquid, large-scale redemptions become impossible.

This creates a systemic risk: the appearance of liquidity in normal times masks the potential for market-wide liquidity crises. When multiple institutions rely on the same sources of apparent liquidity, the system becomes more fragile.

Testing Liquidity – Easier Said Than Done

Testing the ability to liquidate assets remains challenging. Current approaches to estimating liquidation costs are still maturing in many markets. Desktop exercises and historical analysis of liquidity crunches provide insights but have limitations.

Testing available liquidity by transacting in large volumes under normal conditions is expensive and, more importantly, tells us little about the ability to transact in disrupted markets. Tests of notional volumes may generate a false sense of security rather than inform real liquidation measures.

The true test of liquidity often only comes during stress events – precisely when you most need it to work.

When “Liquidity” Masks Solvency Issues

Some apparent liquidity crises are actually solvency issues in disguise. A prime example is minimum surrender guarantees in a rising rate environment. When interest rates rise significantly, policies with guaranteed surrender values can become deeply unprofitable. Each surrender crystallizes a real economic loss – no amount of liquidity support solves this underlying problem.

Policyholders can withdrawn their funds, benefit from the rising interest rate environment and re-invest in a new policy or other structure taking advantage of higher interest rates. It should be no surprise that this is the result of the dangerous combination of higher interest rates and guaranteed surrender values. (There are ways, complex, expensive ways, to manage this risk, but that first requires an appreciation of the risk. This requires at least adequate liability measurement, robust scenario testing that doesn’t assume prior low volatility periods will continue, and consideration of dynamic policyholder behaviour.)

Is this a liquidity risk? Firstly it is a solvency risk. Th value of “matching” assets has declined while the value of liabilities has not. A liquidity risk is only a liquidity risk if the provision of liquidity solves the problem.

When measuring liabilities and therefore solvency, it seems dangerous to rely on assumed policyholder irrationality (expecting them not to surrender when it’s clearly in their financial interest to do so) to support solvency calculations. Good risk management and appropriate liability measurement must recognize that policyholders will likely act in their financial interests, especially when the benefits of doing so become obvious.

Now there may also be a liquidity risk. If surrenders require liquidation of illiquid assets that may further depress asset prices, increasing yields and/or spreads. Resultant concerns around insurer solvency can also lead to a run on the insurer. It’s a mistake to think of all of this as a liquidity risk.

Implications for Risk Management

Liquidity risk is real, and may still be underestimated by many insurers. Insurers should be carefully evaluating their risk management systems for adequate coverage of liquidity risk.

These complexities demand sophisticated risk management approaches:

• Regular stress testing must consider both solvency and liquidity impacts
• These stress tests must be severe enough and must consider interactions
• Liability measurement needs to incorporate realistic policyholder behavior assumptions
• Investment strategies must balance efficiency with liquidity needs
• Liquidity buffers should consider both immediate and slow-burn scenarios
• Risk frameworks must recognize the limitations of market liquidity assumptions
• Consider when your sources of liquidity (money market fund contractual promises) may necessarily fail in systemic liquidity challenges

In the world of insurance regulation, few mechanisms are as misunderstood as the equity symmetric adjustment (ESA), also known as the equity dampener. This feature, present in both the Solvency II framework in Europe and the Solvency Assessment and Management (SAM) regime in South Africa, is often incorrectly associated with the concept of mean reversion in equity markets. This blog post aims to clarify the true purpose of the equity symmetric adjustment, explain how it works, and explore its implications for insurers and market dynamics.

The Real Purpose of the Equity Symmetric Adjustment

Contrary to popular belief, the ESA is not designed to predict or capitalise on market rebounds. Its primary purpose is to prevent pro-cyclicality in insurance regulation. But what exactly does this mean?

Understanding Pro-cyclicality

Pro-cyclicality refers to the tendency of financial variables to fluctuate around a trend in the same direction as the overall economic cycle. In the context of insurance regulation, pro-cyclical behavior can amplify market stress and potentially contribute to systemic risk.

For instance, during a market downturn:

1. Equity values decrease
2. This reduction in asset values could push insurers’ solvency ratios below regulatory requirements
3. To restore their solvency position, insurers might be forced to sell equities
4. This selling pressure could further depress equity prices, exacerbating the market downturn

This cycle can create a feedback loop, potentially deepening financial crises. The ESA aims to mitigate this risk by adjusting capital requirements based on market movements.

How the Equity Symmetric Adjustment Works

The equity symmetric adjustment modifies the standard equity capital charge based on the current level of an appropriate equity index relative to its average level.

In Solvency II and SAM, the adjustment is calculated as follows:

1. The reference level is the average level of an appropriate equity index, calculated over the last 36 months.
2. The current level of the same index is compared to this reference level.
3. The adjustment is equal to half the difference between these two levels, subject to a maximum adjustment of ±10%.

For example:

• If the current index level is 20% below the reference level, the adjustment would be -10% (capped at the maximum).
• If the current index level is 10% above the reference level, the adjustment would be +5%.

This adjustment is then applied to the base equity shock. For instance, if the base shock for type 1 equities is 39%, and the symmetric adjustment is -7%, the final shock applied would be 32% (39% – 7%).

It’s worth noting that in the recent Solvency II review, EIOPA proposed increasing the cap on this adjustment from ±10% to ±17% to enhance its effectiveness (EIOPA, 2020). There are no immediate plans to change this for South Africa’s regulations.

Dispelling the Mean Reversion Myth

The misconception that the equity symmetric adjustment is based on mean reversion likely stems from its symmetrical nature and its use of historical average index levels. However, it’s crucial to understand that the mechanism functions independently of any assumptions about future market movements.

Mean reversion in financial markets is the hypothesis that asset prices and other market indicators eventually return to their long-term average levels. While this concept remains a topic of debate among financial economists, it’s not the basis for the equity symmetric adjustment.

A comprehensive study by Spierdijk, Bikker, and van den Hoek (2012) found evidence of mean reversion across 18 OECD countries over the 20th century. However, they noted that the speed of mean reversion varies significantly over time and across markets, with half-lives ranging from 1.7 to 23.8 years. This variability underscores the complexity of market behavior and the risks of relying on mean reversion assumptions for short-term regulatory mechanisms.

Moreover, there are numerous examples of prolonged market declines that challenge simplistic mean reversion models. During the Great Depression, the U.S. stock market experienced multiple significant declines before reaching its bottom, and it took over 25 years for the market to regain its pre-crash peak (Mishkin & White, 2002). More recently, during the 2007-2009 financial crisis, global equity markets continued to fall for months after initial sharp declines (Bartram & Bodnar, 2009).

Market Performance After Significant Declines

While not directly related to the equity symmetric adjustment, it’s worth examining market performance following significant declines, as this often informs risk management decisions.

Batnick (2020) found that after 2 standard deviation drawdowns in the S&P 500, the average 1-year forward return was 23.8%. While this figure is impressive, it’s crucial to compare it to typical mean returns. The long-term average annual return of the S&P 500 is about 10% (Damodaran, 2021).

This data might suggest stronger performance post-decline, aligning with some mean reversion theories. However, it’s essential to remember that:

1. Past performance doesn’t guarantee future results
2. Some periods saw continued declines after initial drops
3. The timing and magnitude of any recovery can vary significantly

These factors underscore the importance of careful, context-specific analysis in risk management decisions.

Does the ESA increase or decrease risk?

The application of the ESA results in insurers holding less capital than would be required by a strict 1-in-200 calibration. While this reduction in capital may increase the risk of undercapitalisation and potential failure for individual insurers, the broader systemic benefits must also be considered.

By easing the capital burden during market downturns, the ESA help prevent insurers from being forced to sell assets at depressed prices, which could exacerbate market crashes and contribute to systemic risk. This stabilising effect reduces the likelihood of a market-wide financial collapse, arguably lowering the overall risk to the financial system. However, this trade-off comes with the inherent risk that insurers, holding less capital than prescribed, may face increased vulnerability in the face of prolonged downturns or unexpected shocks.

Balancing these risks is central to the argument for counter-cyclical measures in regulatory frameworks like Solvency II and SAM

Implications for Insurers: LACDT and DTA Recoverability

Understanding the true nature of the equity symmetric adjustment and the complexities of market dynamics is crucial when insurers calculate their Loss Absorbing Capacity of Deferred Taxes (LACDT).

When determining the recoverability of Deferred Tax Assets (DTA) from unrealised capital losses, insurers must carefully consider any assumptions about market recovery or mean reversion. While historical data may support some recovery expectations, it’s crucial to be conservative in these estimates.

The European Insurance and Occupational Pensions Authority (EIOPA) has emphasised the need for prudence in LACDT calculations, particularly concerning assumptions about future returns (EIOPA, 2019). Insurers should ensure that any assumed post-stress returns are well-justified and consider a range of potential scenarios.

Conclusion

The equity symmetric adjustment is a regulatory mechanism designed to mitigate pro-cyclical behavior in insurance markets, not a tool for capturing mean reversion. Its design reflects an understanding of market dynamics and the potential for regulatory requirements to inadvertently exacerbate market stress.

For insurers, it’s crucial to understand both the regulatory perspective of measures like the equity symmetric adjustment and the underlying market dynamics. When conducting internal risk assessments, such as economic capital calculations or Own Risk and Solvency Assessments (ORSAs), a nuanced understanding of market expectations and risks is essential.

Caution is warranted when assuming market recovery after catastrophic events. While historical data may show a tendency for markets to recover over time, the timing and path of such recoveries can be highly uncertain. Improving solvency positions based on optimistic recovery assumptions could expose insurers to significant risks if markets don’t behave as expected.

Effective risk management in the insurance industry requires balancing regulatory compliance with a deep understanding of financial markets, always erring on the side of prudence to ensure long-term stability and policyholder protection.

References

1. Bartram, S. M., & Bodnar, G. M. (2009). No place to hide: The global crisis in equity markets in 2008/2009. Journal of international Money and Finance, 28(8), 1246-1292.
2. Batnick, M. (2020). Here’s what happens after a massive stock market decline. The Irrelevant Investor. [Accessed 25 September 2024]
3. Damodaran, A. (2021). Historical returns on stocks, bonds and bills: 1928-2020. New York University Stern School of Business.
4. European Insurance and Occupational Pensions Authority (EIOPA). (2019). Report on insurers’ asset and liability management in relation to the illiquidity of their liabilities.
5. European Insurance and Occupational Pensions Authority (EIOPA). (2020). Opinion on the 2020 review of Solvency II.
6. Mishkin, F. S., & White, E. N. (2002). U.S. stock market crashes and their aftermath: implications for monetary policy (No. w8992). National Bureau of Economic Research.
7. Spierdijk, L., Bikker, J. A., & van den Hoek, P. (2012). Mean reversion in international stock markets: An empirical analysis of the 20th century. Journal of International Money and Finance, 31(2), 228-249.

• Growing regulatory acceptance as parametric solutions prove their value. (Issues of insurable interest have posed problems. Currently in testing in “sandbox” regulatory environments in a few countries including South Africa, where it has traditionally been viewed as non-compliant.)
• Addresses previously uninsurable risks for corporates and governments, filling protection gaps. Good application for captive insurers (I’ll cover this more in a later post)
• Complements reinsurance by covering areas traditional policies often exclude
• Primarily used for commercial lines, but personal applications are emerging
• Significant applications for transferring country-level risk for governments and certain NGOs
• Basis risk remains a consideration, but can be mitigated somewhat through careful structuring

Exciting developments include parametric ETFs, allowing investors to participate in this innovative market. We’re also seeing creative applications using new data sources, like phone signals to assess footfall.

I can get theoretically excited about smart-contracts for parametric insurance, but in practice this quickly feels like unnecessary complexity with limited current benefit.

Parametric insurance can compete with reinsurance, but it’s often best used in combination, or as a tool for reinsurers to spread risk

As always, professional advice is crucial when exploring these solutions.

I’ve had reasons to think about Nigeria recently, in general but also from an insurance market and acquisition environment. I’ve helped several investors looking at Nigerian insurers over the years. Familiarity with the market and the expectations of these investors has bred complexity rather than contempt. This isn’t an easy diagnosis of land of plenty or dystopian money pit.

Nigeria still presents a compelling opportunity with its expansive land, sizable population, youthful demographics, positive growth trajectory, and abundant natural resources. Beyond its renowned oil and agriculture sectors, Nigeria boasts a vibrant movie industry (Nollywood) and a robust financial services sector, albeit with banks making more headway than insurers. Wholesale and retail trade are the biggest contributors to economic growth. This dynamic mix showcases Nigeria’s diverse economic landscape and entrepreneurial spirit and an increased focus on the service sector over energy extraction and farming.

While Nigeria’s potential has long been evident, ongoing challenges test that optimism. 

Inflation (29.9% annual for January 2024) and currency depreciation (74% down against USD since January 2022) have impacted individuals and businesses, amplifying economic strains. The local impact of foreign currency denominated debt has ballooned due to Naira depreciation. Ghana’s recent default weighs on everyone’s mind.

Food security for many is now a significant risk. Infrastructure limitations persist, impeding the full realization of economic growth. High unemployment rates, coupled with security challenges and governance issues, have eroded public and investor trust. In the insurance sector, while some have some growth and success with new product lines, overall insurance penetration remains modest. Insurance adoption has not accelerated as rapidly as envisioned over the past decade or two

While Nigeria stands to gain from ongoing disruptions in the Middle East and related waterways, the nation’s oil and gas sector remains a double-edged sword—both a source of revenue and trouble. Given the historical challenges of theft and attacks on infrastructure, Nigeria may not be able to maintain let alone increase production to meet an increased demand.

The recent decision by Shell to exit Nigeria’s onshore oil sector highlights the substantial risks involved, not only to infrastructure but also to human life. As a significant portion of Nigeria’s economy is still reliant on the oil and gas sector, these developments raise concerns about potential prolonged challenges, affecting the economy and therefore adding headwinds to insurers growth aspirations.

Insurers can’t fix these challenges directly. They need to focus on perception and reputation, on paying claims and improving operational efficiencies. Some insurers are excited about mandatory health and pensions, to go along with mandatory cover for motorists, but these compliance push factors do little to promote trust in insurance unless servicing and claim payment are slick and reliable too.

Most of the growth that insurers have managed over recent years has related to growth in GDP rather than an increase in penetration. The sorts of sustained 20%+ real growth that attracts investors and revolutionises a market will not come from economic and population growth alone.

There are opportunities for growth. When someone cracks microinsurance distribution and costs, and reaps the rewards of brand awareness, that can unlock massive growth and profits over time. There are many uninsured vehicles that could be bought into the insurance net. Smaller group policies covering household help could meet a needs of employers and employees. Annuities are a growing product for some insurers, and may present a further way to accumulate assets and also demonstrate trust worthiness to the market. (On the flip side, a single failure of a provider of annuities will crush this market for decades.)

Insurers need to have a strategic plan to manage their business within the turbulent environment. Some of what’s needed:

• A focus on consolidation around key products, unsentimental views of product profitability and underwriting performance.
• Allocation of capital to products to demonstrate return on capital, or at least incorporating an appropriate cost of capital into performance measures.
• Clear separation of investment returns generated on shareholder assets when understanding operating performance. (Warren Buffet’s words can be on “the float” misconstrued to destroy shareholder value.)
• (While you’re at it, it’s way past time to carefully segregate portfolios and match or at least hypothecate assets to specific purposes.)
• Clear-eyed evaluation of participating products. Customer expectations, levels of fees and charges. Fair investment returns and bonuses. The aim is to grow trust over time and wealth for your policyholders. Performance for shareholders will come.
• In general, a greater proportion of premiums must be used for benefit payments to policyholders, distribution costs must be contained, and expenses must be decreased. This is necessary to drive customer value and build trust, while leaving space for returns to shareholders.
• A better understanding of the role and benefit of reinsurance in life insurance. Different structures and different retentions may provide better results than rolling over similar structures indefinitely.
• A Digital Distribution and Servicing Strategy than recognises the trust deficit insurers have to work with and constantly pushes that flywheel to build trust rather than just drive the next sale. Customers want ready access to policy information and up-to-date account balances and policy status. On the back end, a single view of customer is required, giving customers and servicing agents the ability to update details once – and then use those details for effective, useful communication to policyholders. The more self-service possible the more empowered customers will feel.
• Recognition that driving down unit expenses (per policy expenses) is necessary for profitability and customer value. And decreasing unit expenses requires economies of scale. And that economies of scale requires BOTH scale and low variable costs – which is a function of automation, Straight Through Processing, Standard Operating Procedures and streamlined products.

Nigeria presents an opportunity, but it’s not without risks. The time necessary to realise investment objectives may be longer than is palatable to many, and disinvesting in difficult times often leaves a bitter taste and a lightened pocket.

Focus areas will differ by entity, but based on my experience, the points above are a sensible starting point for most. Add the controversial elements of tax rule application consistency and greater market conduct regulation and Nigeria’s market could really begin to take off.

Insurers, unlikely retirement funds, have to hold risk-based capital against the risks inherent in their investments. This makes it more difficult to underestimate the risks and services as a deterrent to large allocations.

Infrastructure assets can play a part in linked funds for life insurers, where the investment risk is passed straight back to the policyholders and no market risk capital is held by the insurer.

Under this policy construction, the risks can be similar to a defined benefit retirement fund. These include the practical challenges of pricing and valuation, and conduct and fairness issues of managing investment and divestment prices, liquidity with large withdrawals and transparency of pricing.

These liquidity constraints also make this a poor investment for non-life insurers or smaller life insurers, especially where they primarily write risk business.

Where are alternative assets used in insurance?

The three areas where infrastructure assets have a meaningful place to play in insurance are:

1. As a part of a portfolio of assets for long-dated, predictable and illiquid annuity liabilities.

2. Part of a with-profits portfolio, whether this is accumulation phase or with profit annuities in payment.

3. Part of large, well-capitalised insurer’s shareholder portfolio, subject to risk appetite constraints.

How are infrastructure assets treated for insurers for regulatory purposes

In 2014, EIOPA started to consider whether the Solvency II regulations would discourage insurers to invest in infrastructure assets. It was carefully phrased as “removing disincentives† but the line between that and deliberate incentives for insurers to invest in infrastructure assets is invisible.

Right towards the end of the development of South Africa’s Solvency Assessment and Management (SAM) regulatory overhaul, Task Groups of the SAM project were asked whether any adjustments were recommended.

Technical Provisions adjustments for infrastructure assets

The answer from the Technical Provisions Task Group was “no†. Technical Provisions were intended to be market consistent and, with possible exceptions for illiquidity premium / matching adjustments (already a part of the regulations) returns on assets should not, in general, affect the measurement of liabilities.

The illiquidity premium is still very much relevant. Up to 50bps can be added to the risk-free yield curve for discounting life annuity cash flows, provided the backing assets are a good cash flow match and are managed separately from the rest of the portfolio. The illiquidity premium is calculated as 50% of the spread achieved on the matching assets.

In South Africa, most of the available corporate paper available to generate spreads has a term of five years or less. This greatly reduces the effective average spread that can be applied. Longer-term (20 or 40 year) infrastructure debt-based investments are very welcome in this scenario.

This allowance is not specific to infrastructure assets, but is important as part of the overall capital assessment of infrastructure assets.

It’s worth mentioning that the European Solvency II “matching adjustment† is far more generous. I regularly experience actuaries or consultants from the UK talking up great plans for assets in a SAM environment, assuming that the rules are the same in South Africa as they are across Europe.

(The volatility adjustment in theory also has a place in this discussion, but that’s a bigger topic and typically a smaller impact in any case.)

Solvency Capital Requirement (SCR) adjustment for infrastructure assets

The Capital Requirements Task Group followed the European lead and allowed reductions in the equity shock and spread shock that would be applied to qualifying, high quality, infrastructure investments.

• 33% shock for equity (which is 77% of the “SA equity† shock, or about 70% of “Other Equities† shock, which I’d argue would be the most typical classification in the absence of an infrastructure asset class)
• Symmetric adjustment = 77% of SA equity
• 70% of spread shock for debt
• 65% illiquidity premium shock

The 65% shock to the illiquidity premium is not specific to infrastructure. It’s also complete irrational and greatly reduces the benefit of the very limited illiquidity premium in the first place.

• The stated risk here is a narrowing of the illiquidity premium, but this could only be realized through an increase in the relevant asset prices, matched with an increase in liabilities with no net impact. Since the shock is defined as “A 65% fall in the value of the illiquidity premium used in the valuation of technical Provisions† there is no offset for the asset of this calculation.
• The actual risk, if there were one, would be an increase in illiquidity premiums in the market, resulting in a decrease in asset values, only partially offset by a decrease in liability values due to the 50bps cap.) 

Impact of SCR relief

The impact of lower SCR on after cost-of-capital investment returns needs to be calculated for the specific portfolio and how it interacts with other risks within the business. One might expect a 1% to 2% increase in penalized returns.

Qualifying criteria

To qualify as an “infrastructure asset† and benefit from the lower capital charges, a fairly lengthy set of criteria must be met. For insurers already intended to invest in only high quality (and therefore lower return) infrastructure assets, these criteria may overlap with existing due diligence and investment analysis processes.

Non risk-based criteria

Risk-based criteria

The Infrastructure project entity can meet its financial obligations under sustained stresses that are relevant to the risk of the project.

• Must be externally rated (in theory it doesn’t have to be, but in practice it really should be and questions would be asked by the Prudential Authority if it weren’t.)
• The off-taker must be either the South African government, or there must be a large number of, ideally independent, diversified customers.

• The Infrastructure assets and Infrastructure project entity are governed by a contractual framework that provides debt providers and equity investors with a high degree of protection
• For bond investments, significant additional covenants are required
• The cash flows that the Infrastructure project entity generates for debt providers and equity investors are predictable. This must be demonstrated through one of the following:
  • Availability based revenues
  • Rate of return regulation covering revenues
  • Take or pay contract
  • Output or usage and price imply low risk

Should insurers invest in infrastructure?

It’s unhelpful to say “it depends†, but of course it does. However, with appropriate due diligence and consideration of the financial and capital implications, life insurers with large with profits or annuity books can benefit shareholders and policyholders, as well as potentially the country as a whole, by investing judiciously in infrastructure assets.

The risk is that they are outbid by retirement funds with less risk sensitivity to the investments.

I don’t remember looking at this, but the blog entry was actually about insider trading and the information content of directors’ dealings. Here is a quote showing some wisdom and a near miss:

Am I going to invest in Steinhoff? Well, no, not yet, not until I have actually done some proper research into the fundamentals of the company. And also not until I have understood the reasons for the decline in price over the last year properly. If the market thinks they are worth less, I had better know why the market thinks so before I disagree too strongly.

Having said that, I pay careful attention to knowledgeable insiders when they put their money where there collective mouths are and vote with their personal wealth and risk appetites that a company is a good bet.

I never sufficiently understood the fundamentals of the business and how it related to their accounts and valuation. Score one for then not investing.

However, I was also saying that I saw value in following directors’ dealing and possible positives from directors investing in their own stock. In the case of Steinhoff, it’s hard to separate out:

• true belief in their business;
• attempts to demonstrate confidence in the shares (whether or not the confidence was actually held); from
• artificial attempts to prop up the share price

I have less time for fundamental analysis these days so low cost trackers is more my flavour. Given my mixed success in the past, perhaps that’s just as well.

In particular, he mentioned a specific assumption set he had seen including:

• ERP of 6.8%
• company specific risk premium of 4%

He also commented on how haphazard the use of risk premiums can be and referenced a few sources I’ve used myself.

The ERP of 6.8% does seem high. However, it really isn’t possible to comment on the specifics of the company specific risk premium without knowing the company.

Although I haven’t updated my research on this in a few years, in my own work I still generally stick with a range of 3% to 5% for an ERP, before considering company specific factors, liquidity, and so on. Historically / empirically estimated ERPs shouldn’t change frequently since the time series used is long. Another few years on a 20 year estimation period shouldn’t have much impact.

Why some practitioners persist in using too-high ERP estimates

This delves into the area of philosophy, but here are my top reasons (a post from 2011 also covers this):

• Naive analysis of the historical returns in the US over very successful periods for the US economy and stock market easily give high ERP estimates
• comparison of equity returns against short dated T bills rather than longer term T bonds.  (This is less terrible if you apply the premium to short dated rates, but still problematic for several reasons.  It is totally wrong if you apply the rate to bond yields.)
• Confusing of ERP with the total risk premium for a specific share (and more on that later in this post)
• Declining ERPs over time has boosted historical realised ERPs compared to forward looking estimates.

Quick updated estimate of market implied ERP

The use a market implied ERP is still useful as a forward looking measure, especially where a valuation relative to current listed market instruments is important (and it usually is). However, it’s not like this isn’t a subjective process either.

Using this old spreadsheet ERP estimation tool, I used the following quick assumptions:

• Dividend yield of 2.8% (from the All Share)
• Real risk-free yield (R210 yield, which matures in about ten years time) of 2.5%
• Break Even Inflation of 6.1% (based on nominal ten year bond yields of 8.6% and the 2.5% real risk free yield)
• Assumed real GDP growth of 1.8% per annum (based on a combination of sources including our reserve bank, world bank and others) showing 1% growth in the immediate future possibly getting up to 2% over time.  (None of this is pretty, and none of this will really materially increase GDP per capita).

This gives a market implied ERP of just 2.2%. Although this feels quite low, it shouldn’t be surprising given that we all recognise the economic fundamentals feel weak but our stock market is priced at record nominal levels.

Other estimates of market implied ERP

The reader sent me to this website, which shows market implied ERPs. It’s a useful resource. Here is the current view up to 30 September.

They end up with a higher ERP of 2.6%, which actually gives me comfort in my quick estimate of 2.2%, especially when I see that in August their estimate was only 2.4%, which is even closer.

The problem with the JSE as the market for South African companies

Tencent. In a word, that is.And other multinationals and entities with significant exposures outside of South Africa. I believe one of the reasons these ERPs are looking so low is that growth prospects outside of South Africa are better than inside South Africa, so the stock market prices look “too high” compared to South African country prospects, resulting in a too-low ERP.

The problem with “risk free” in emerging markets

Risk free is a term that makes less and less sense the more one thinks about it.  Is Greece government debt risk-free? Is South African government debt risk-free? What about the credit and liquidity characteristics?

Differences between these even within a country, say between the chosen nominal and real bonds used to estimate certain parameters can influence the estimates.

Although the credit spreads should in theory be removed in the estimation of ERP, it is hard to shake the concern that there might be second order implications that are not quite so simple.

So what about other countries then?

From that same site (I’m not going to do a whole range of other countries myself):

• UK 5.8%
• US 3.6%
• Australia  4.4%
• Canada 4.8%
• Switzerland 5.8%
• Germany 6.4%
• France 6.1%
• China 3.8%
• Brazil 2.0%
• India 2.3%

I don’t know enough about Brazil or India to know where there are specific issues for those markets, whether the methodology here falls down, or whether this is part of an emerging market trend.

But overall, these ERPs fall mostly within a comfortable range of 3% to 5% , with some stretching a little outside that on either side.

Company specific parameters

Standard CAPM models assume company specific factors are irrelevant because that risk can be diversified away and therefore should earn no reward. This is broadly true for a diversified investor investing in listed, liquid stocks. Empirically it is absolutely not true for privately held shares, illiquid shares, investments where control may be gained or given up and a host of other possible scenarios.

Estimating a reliable Beta to apply in the CAPM model is about as difficult as anything else covered here, so even then the ERP is not the end of the story.

When valuing a private company, one needs to look at how private companies are valued.

That’s not as vapid as it may sound. Valuation should be concerned with market consistency. This is why we speak about “market implied ERP” in the first place. So, if most other private company valuations (and transactions) factor in company specific factors such as:

• liquidity
• control
• small stock effects
• key person risks
• concentrated customer risks
• leverage (especially if not factored into the Beta).

then a valuation that aims to be consistent with other valuations should factor these in too.

That list isn’t complete and many of the items overlap.  Each one also needs to be carefully weighed against:

• is this not already factored into the ERP?
• is this not already factored into the Beta if one is used
• is this not already factored into the estimation of cash flows

That last one is key.  In fact, it is often the reverse that is true.  Known risks are not reflected in a true probability weighted best estimate manner in the future cash flows. Thus, without some risk adjustment in the discount rate, the value will be overstated.

Scenarios and cash flows as alternative ways to allow for risk

If multiple scenarios are used in the valuation, with attached probabilities, it may be that these risks are adequately considered in the cash flows and do not need an additional adjustment in the discount rate.  Key person risk or customer concentration risk can be reflected in a scenario with a 10% or 20% probability of seriously negative consequences of losing that rainmaker or specialist knowledge, or of losing a single customer along with 50% of revenues.

For larger businesses, with more diversified revenue streams, larger numbers of customers and fewer key person risks (or better ways of mitigating them), these risks tend can be reflected naturally in the cash flows since past experience will likely include some instances of the risk. (This links to another post on ENID.)

Consistent with the market

A useful resource here is PwC’s valuation methodology survey.

Final thought – is a company specific risk premium of 4% too high?

While it is hard to say without knowing the specifics of the company, it doesn’t strike me as obviously too high for a moderate sized, unlisted company.

A recent article in The Actuary magazine addressed whether “de-risking in members’ best interests?“  I say “recent” even though it’s from August because I am a little behind on my The Actuary reading.

In the article, the authors demonstrate that by modelling the impact of covenant risk, optimal investment portfolios for Defined Benefit (DB) pensions actually have more risky assets than if this covenant risk is ignored.

The covenant they refer to is the obligation of the sponsor to make good deficits within the pension fund. Covenant risk then is the risk that the sponsor is unable (typically through its own insolvency) to make good on this promise.

On the surface it should seem counterintuitive that by modelling an additional risk to pensioners, the answer is to invest in riskier assets, thus increasing risk.

The explanation proffered by the authors is that the higher expected returns from riskier assets allow the fund to potentially build up surplus, thus reducing the risks of covenant failure.

I can follow that logic, particularly in the case where the dependence between DB fund insolvency and sponsor default is week. It doesn’t mean it’s a useful result.

The optimisation considered only one side of the equation – what is in members’ best interests. It ignores the other side of the problem – the financial impact (expectation and variability around that) for the sponsor’s financial position.

To take it to the extreme, if every sponsor just liquidated all its assets and transferred them to the DB fund, that would be a pretty good outcome for fund members. Not so much for the sponsor.

Without having seen the detailed model results, what I expect is happening is that the increased allocation towards risky assets is increasing expected returns (as it should) but also increasing the risk to the sponsor of having to put in additional funds. Any time this is done and the sponsor doesn’t default, there is no downside for fund members. The only risk to fund members is the combination of being underfunded and sponsor default.

The risk to the sponsor of increased frequency of injections required has been well established for decades. Depending on what you are willing to assume about risk premiums for risky assets and the assumed risk appetite, the higher expected return might outweigh the increased risk. This is not a point to be glossed over let alone left entirely untouched.

It also raises an awkward question about what they’ve modelled in terms of the covenant in the “no covenant risk” scenario. Surely if one models the covenant and not risk to the covenant, all benefits should always be paid to the members, regardless of asset mix?

I’m not at all convinced that these results are reliable. But either way, the fact that they are optimising and showing results for only one side of a two sided problem makes the approach utterly flawed.

Messages? This time isn’t different, sovereign debt crises happen all the time, ignore currency risk at your peril and there are many reasons governments can default on their debt.