Hyperinflation, Currency Crises & Insurance Industry Collapse

Hyperinflation destroyed Zimbabwe’s insurance sector, and decades later, it still hasn’t recovered. Currency crises in Lebanon, Argentina, and Venezuela have crippled financial institutions, showing how fragile financial systems can be when trust in money itself disappears.

A recent discussion started as a tongue-in-cheek debate: Is inflation a more efficient way to raise revenue than taxation? But it evolved into a broader debate on monetary risk, Bitcoin, inflation, and long-term economic trends—and why so many common arguments deserve scrutiny.

How Inflation Impacts Insurance

Premiums & Inflation Risk
High inflation makes level premiums unworkable, erodes the real value of cover. Optional benefit increases create adverse selection problems in life insurance. Even constant percentage increases fail under volatile inflation, and real wage stagnation worsens affordability pressures.

Monetary Instability & Insurer Solvency
Currency collapses create huge challenges for insurers trying to meet liability obligations in real terms. When inflation spikes, reserves built on past assumptions become grossly inadequate, leading to solvency concerns and even industry-wide failure.

Crypto & Smart Contracts in Insurance
Blockchain has potential for parametric insurance, automated claims processing, and fraud reduction. But much of the excitement outpaces practical application—or solves problems that were already solved while not addressing key remaining challenges.
(And let’s be real—just because a smart contract auto-executes doesn’t mean lawyers won’t find ways to argue intent and “meeting of minds.†)

My (Cautious) View on Blockchain

I spoke at the 2016 ASSA Convention on Seductions of the Blockchain, and my position remains:

• Cautiously optimistic
• Interested in opportunities
• Frustrated by the lack of rigorous debate from both fanatics and skeptics

The fanboys see blockchain as a cure-all, while the status-quo-invested skeptics dismiss it entirely. Reality, as always, is more nuanced.

Key Arguments & Concerns

1 Inflation as an ‘Efficient’ Tax?

Some argue that taxes are administratively complex, difficult to collect, and inflation acts as an “invisible tax† that transfers wealth to the state with less friction.

The Problem? Inflation isn’t a neutral mechanism:

• Distorts price signals and makes long-term contracts unreliable.
• Increases uncertainty and raises borrowing costs.
• Disproportionately harms those without inflation-protected assets—often the poorest.
• Erodes trust in government’s ability to manage financial stability.

Hyperinflation isn’t just caused by overspending—it requires excessive money printing to cover deficits. Many governments (e.g., Japan, the US, and EU countries) have run huge deficits for years without hyperinflation because they borrow responsibly instead of monetising debt.

Friedman’s famous quote:
“Inflation is always and everywhere a monetary phenomenon in the sense that it cannot occur without a more rapid increase in the quantity of money than in output.”

2 Bitcoin as a Predictable Alternative to Fiat?

Bitcoin proponents argue that a fixed supply prevents inflation and provides monetary certainty. But there’s a flip side:

The Problem? A rigid money supply is deflationary, which discourages spending and investment:

• BTC expansion (~0.9% today, falling below 0.5%) is well below global population and economic growth.
• Fixed-supply currencies have historically failed because economies need monetary flexibility to adjust to shocks.
• A deflationary currency discourages productive investment. If BTC’s price is expected to rise, why spend it? Why take out a loan?

This is why almost all mainstream economists—from Keynesians to monetarists—support some level of controlled monetary expansion.

Academic reference: Friedman advocated rules-based money supply growth, not a hard cap. Even Hayek, a proponent of free-market money, acknowledged the need for adaptable monetary systems.

A bigger issue: Some crypto coins have fixed supply, but the total universe of crypto coins is unlimited. New projects, forks, and tokens emerge constantly, meaning there is no true scarcity at a system-wide level.

3 Credit Risk & Smart Contracts – Who Pays When the Funds Aren’t There?

Smart contracts don’t solve credit risk. Traditional insurers must hold capital reserves and meet solvency requirements to ensure claims can be paid. Smart contract-based insurance lacks an equivalent safety net—yet.

Key Risks:

• No Guarantee of Payouts: If a smart contract is underfunded, it can’t issue emergency capital or negotiate claims—it just fails.
• Over-Collateralization Isn’t a Perfect Fix: Many DeFi protocols require excessive collateral to mitigate risk, but this limits scalability and locks up capital inefficiently. Actuarial approaches to capital adequacy could provide a smarter balance.
• Cascading Failures in Market Shocks: A major market downturn can cause mass liquidations, leading to systemic failures—just like traditional financial crises, but with fewer stabilizers.

Future Opportunity:

• As DeFi regulation increases, some form of capital adequacy requirements (like Solvency II for insurers) may emerge.
• Actuaries and insurance risk experts could play a role in designing smarter DeFi risk models.

Final Thought: Smart Contracts Are an Exciting Tool—but They Need More Work

Smart contracts introduce new efficiencies, but they also introduce new risks:
They remove intermediaries—but also eliminate safety nets.
They change fraud risk—but introduce oracle manipulation risk.
They enable fast, automated transactions—but don’t guarantee funds will always be there when needed.

For insurance, finance, and risk management, blind reliance on smart contracts is dangerous. But recent advancements show promise:

• Regulators are starting to provide legal clarity.
• Hybrid smart contracts (automated + human oversight) are emerging.
• Decentralized oracles & improved collateral models are evolving.

The real opportunity? Combining smart contract automation with actuarial risk management principles to build more resilient decentralized insurance solutions.

Would love to discuss with those working in insurance, risk management, DeFi, and blockchain regulation.

#Inflation #Blockchain #BTC #ETH #DeFi #DistributedLedger #MonetaryPolicy #FinancialRisk #Insurance #RiskManagement #Actuary #Economics #LegalRisk #ParametricInsurance

The data is compelling: global population will likely peak sometime this century before beginning a sustained decline – a phenomenon unprecedented in modern history.

The Emerging Demographic Reality

The charts from the Global Aging Institute tell a compelling story. Most demographic models now predict global population peaking between 2064 and 2086, with maximum populations ranging from 9.7 to 10.3 billion people. What’s particularly notable is that newer projections tend to forecast earlier and lower peaks than older ones – suggesting that fertility decline is accelerating beyond previous expectations.

China represents perhaps the most dramatic example of this demographic shift. Once feared for its population explosion (internally and externally, but perhaps for different reasons), China’s fertility rate has plummeted to approximately 1.2 children per woman – far below the replacement rate of 2.1. China’s population peaked in 2020. The precipitous decline in fertility has already resulted in a decline in the population. Some models now suggesting its population could halve (or worse) by 2100 from its peak.

The Middle Income Trap and Demographic Headwinds

This chart illustrates what demographers call the “middle income trap,” where countries achieve middle-income status but struggle to join the ranks of high-income nations. Despite substantial growth in East Asia (6.4% annually) and South Asia (3.9% annually) between 1990 and 2022, their GDP per capita remains far below U.S. levels. Meanwhile, regions with lower growth rates like Middle East & North Africa, Latin America, and especially Sub-Saharan Africa (0.8%) show little convergence with developed economies. Sub-Saharan Africa has become relatively poorer relative to the US in the last 30 years.

A key insight here is that population dynamics may exacerbate this trap. Many middle-income countries are ageing rapidly before achieving high-income status – a phenomenon economists call “getting old before getting rich.” Sub-Saharan Africa as a region is almost unique in that it is still growing. But this rate is declining and the global pattern is clear.

This creates a challenging environment where countries must support ageing populations without the institutional and financial infrastructure that developed economies built during their demographic dividends.

Immigration: The Decisive Variable

This chart highlights immigration’s critical role in determining population trajectories in low-fertility environments. For the United States, the difference between the “zero immigration” and “high immigration” scenarios by 2100 is stark – 226 million versus 435 million people. This 209 million person difference exceeds the entire current U.S. population.

This reality transforms immigration from a purely social or political issue into a fundamental economic consideration. Countries with below-replacement fertility essentially face a choice: accept immigration or manage decline. Japan has largely chosen the latter path, while countries like Canada and Australia have embraced the former. The economic implications of these choices will shape national fortunes for decades.

Beyond GDP Growth: Rethinking Economic Impact

A recent conversation with a colleague raised an important question: Does population decline necessarily mean economic weakness? This requires nuanced analysis beyond simple GDP growth metrics.

The Debt Challenge

Population decline creates particular challenges for debt sustainability. With slower or negative population growth, overall GDP growth becomes more dependent on productivity improvements. This makes debt/GDP ratios harder to reduce through growth alone, potentially forcing difficult fiscal adjustments. While automation and technological advancement could boost productivity to offset population decline, experience suggests achieving sufficient productivity growth consistently is challenging.

Labor Market Transformations

An ageing, shrinking population dramatically alters labour market dynamics. While labour shortages may drive wage increases in certain sectors, they can also accelerate automation and reshape entire industries. Japan’s response to its demographic challenges provides valuable lessons, with its emphasis on robotics and technology reflecting adaptation rather than surrender to demographic destiny.

Consumption Patterns and Capital Markets

The traditional life-cycle theory of consumption suggests peak spending occurs in one’s 30s and 40s before declining in later years. However, emerging evidence indicates that aging societies develop different consumption patterns rather than simply reduced consumption. Healthcare, leisure, and personalized services become more prominent, while housing and transportation may decrease in importance.

For capital markets, the traditional view suggests an “asset meltdown” scenario as retirees liquidate investments. Yet the evidence for this remains mixed, with capital flows, policy interventions, and changing retirement patterns creating more complex outcomes than simple models predict.

Insurance Industry Implications

As an actuary, I see several potential implications for insurance markets in this demographically transformed landscape:

Long-Term Care Evolution

Increasing longevity combined with smaller families creates greater need for formal long-term care insurance, though significant challenges remain:

• Greater prevalence of LTC outside the US: As family sizes shrink, dependent ratios increase, and individuals live longer in retirement, Long Term Care (LTC) becomes an increasingly key product. LTC has had a difficult few decades in the US. It’s still fairly uncommon in emerging markets and even some developed markets. Early adopters in these markets can establish the brand, credibility, and experience to become a major player in this market as it grows.
• Hybrid Products: Traditional standalone LTC policies may give way to life/LTC or annuity/LTC hybrids that address the “use it or lose it” concern that has limited market acceptance.
• Home Care Focus: Products emphasizing aging-in-place technology and home care services rather than institutional care will likely gain prominence as consumer preferences shift.
• Public-Private Partnerships: The scale of the long-term care challenge may necessitate government involvement, with insurers potentially managing supplemental coverage above a public baseline.

Retirement Income Transformation

The traditional accumulation-to-decumulation retirement paradigm faces fundamental challenges:

• Flexible Drawdown Solutions: Products will need to accommodate phased retirement, part-time work, and variable income needs over potentially 30+ year retirement periods.
• Longevity Insurance: Advanced-age annuities that begin payments at 80 or 85 may become more prevalent as longevity risk pooling becomes essential for sustainable retirement planning.
• Integration with Healthcare: Retirement products that explicitly address healthcare cost uncertainty will become increasingly important, potentially with features that adjust income based on health status changes.

Investment and Risk Management Innovation

Traditional asset allocation approaches require rethinking:

• Extended Risk Horizons: Longer retirement periods necessitate maintaining higher equity allocations later in life, challenging conventional glidepath models.
• Real Asset Focus: Inflation protection becomes more critical with extended retirement periods, potentially increasing demand for real estate, infrastructure, and inflation-linked securities.
• Intergenerational Products: Multi-generational wealth transfer solutions that optimize across family units rather than individuals may emerge as family structures adapt to longevity.

Industry Structure Changes

Demographic shifts will reshape the insurance landscape structurally:

• Consolidation Pressure: Declining population in certain markets will reduce the absolute size of insurance pools, driving consolidation as fixed costs must be spread across smaller customer bases.
• Digital Transformation: Cost pressures will accelerate automation in underwriting, claims, and customer service, potentially turning insurance from a high-touch to a primarily digital industry.
• Scale vs. Specialisation: Large multinational insurers with the scale to invest in technology may have advantages, while specialized insurers focusing on specific demographic niches could also thrive.

Geographical Divergence

Insurance markets will increasingly bifurcate:

• Mature Markets: Rapidly aging countries will prioritize decumulation solutions, long-term care, and longevity protection.
• Growth Markets: Countries still experiencing demographic dividends will focus on protection, accumulation, and developing institutional capabilities.
• Cross-Border Opportunities: Insurers able to transfer knowledge between these divergent markets may develop competitive advantages through global learnings.

Addressing Behavioral Challenges

Demographic changes intensify existing behavioral biases:

• Longevity Underestimation: Products will need to address systematic underestimation of lifespan by consumers, potentially through novel framing of longevity risk.
• Cognitive Decline Protection: Financial products incorporating protection against diminished financial capacity in advanced age will become increasingly important.
• Family System Integration: Insurance solutions recognizing the role of family systems in later-life care and financial management will gain prominence.

Alternative Perspectives: Not All Decline

While I’ve outlined several challenges, it’s important to consider alternative viewpoints that paint a more optimistic picture of demographic change:

Productivity Growth as Compensation

As referenced earlier, technological advancement and artificial intelligence could potentially drive unprecedented productivity growth that more than offsets population decline. Countries like South Korea have maintained strong economic performance despite rapidly falling birth rates. The labor scarcity created by shrinking populations might accelerate automation and AI adoption, potentially unleashing productivity increases that our models currently underestimate.

Per Capita Prosperity vs. Total GDP

While total GDP might grow more slowly in shrinking populations, GDP per capita could still rise. Fewer people sharing national resources might lead to higher individual living standards, particularly if automation effectively addresses labour shortages. However, this doesn’t fully address debt sustainability issues – a challenge that might create incentives for moderate inflation to decrease the real value of accumulated public debt. Supply constraints from declining labour forces could contribute to such inflationary pressures.

It’s also worth questioning whether GDP per capita is even the right measure for societal wellbeing in aging societies. This metric excludes non-monetary aspects of wellbeing and non-remunerated work. An over-focus on GDP might misrepresent a future that includes many content retirees engaged in meaningful but economically unmeasured activities – from community service to artistic pursuits.

Environmental Benefits

A significant but often overlooked benefit of population stabilization or decline is reduced environmental pressure. Lower population could ease resource competition, reduce pollution, and potentially support more sustainable economic models. While climate change rightfully dominates environmental discussions, population stabilization represents one of the most effective (if slow-acting) approaches to reducing humanity’s ecological footprint.

The Japan Question

Japan’s experience with population decline provides a complex but instructive case study. Despite demographic headwinds, Japan has maintained relatively high living standards, low unemployment, and social stability. Their example suggests adaptation is possible, though not without trade-offs. Japan’s emphasis on automation, careful immigration, and social cohesion offers one path for other ageing societies.

Global vs. Local Asset Markets

While individual countries might mitigate asset price declines through global capital flows, the prospect of worldwide population aging raises questions about whether these balancing mechanisms will remain effective when most major economies face similar demographic trajectories simultaneously. This remains one of the great unanswered questions in demographic economics.

Adaptation Over Decline

Despite these challenges and opportunities, history teaches us that economies adapt. The 1950s comparison my colleague raised is instructive – economic structures reorganize around demographic realities. The key difference today is the direction of change: we’re entering an era where labor becomes more scarce rather than more plentiful.

This transition creates winners and losers. Countries and companies that successfully adapt to aging populations through technology, immigration, and institutional innovation will thrive. Those clinging to growth models predicated on expanding populations may struggle.

For actuaries and financial professionals, these demographic shifts demand fresh thinking about longevity risk, retirement adequacy, and intergenerational equity. Our traditional models built during an era of population growth require fundamental reconsideration.

The future may not be one of economic decline, but rather one of economic transformation – driven by demographic forces that are now firmly established and unlikely to reverse in the coming decades.

The US has better population growth forecast than many countries, but is still facing long term headwinds. South Korea still is the most glaring example, but China will continue to see truly shocking population ageing and decline.

https://edition.cnn.com/2024/04/24/health/us-birth-rate-decline-2023-cdc/index.html

Declining populations are rapidly becoming the norm. Global population growth is slowing dramatically and will be negative by the second half of this century.

This may be a bigger driver of change in our lifetimes than climate change.

Actuaries should be dusting off their long term thinking, demographic modelling skills, understanding of economics and markets and yield curves, and penchant for scenario testing. This is a problem for us

Business Rescue precludes creditors from applying for liquidation of the business. This is the removal of an existing right of lenders: “a temporary moratorium on the rights of claimants against the company or in respect of property in its possession”

From what I gather it’s not clear that this formally counts as default – might depend on specific loan or bond terms and how credit rating agencies respond to this.

How one “feels” about this is less relevant than the legal interpretation for cross-default provisions. It certainly feels like default to me.

For SAA, it’s also a step which means the government is no longer prepared to keep putting in money. That’s certainly a message about how likely any implicit (rather than explicit) governmental guarantees are for other entities.

Short aside on government debt and balance sheets

It’s not really so much that this is bad news, but rather this is the long-overdue recognition of how bad the news is around SOEs and their total contribution to the true Debt/GDP and their zero or negative contribution to the less-publicised Asset/GDP ratio. As I’ve mentioned before, another useful ratio would be (Debt-Assets)/GDP, which if measured carefully can be a more useful measure of the true financial position of a country and a better guide for decisions on whether to privatise an existing SOE.

A full balance sheet approach and one that considers return on capital (as well as also-important social-development, second-order, longer-term and positive externality items) should form a greater part of policy decisions.

The Cyclically Adjusted Price Earnings (CAPE) or Shiller PE ratio is 28.5. Although this is down from January 2018 where it stood at 33.3, the only other time’s it has been this high was in January 2000 (followed by years of poor returns) and Black Tuesday (of Great Depression fame.)

The standard PE ratio of 21.1 is high too, but not perhaps to the same degree as the CAPE ratio. This indicates that current earnings and relatively higher (compared to their historical average) than the average of earnings over the last 10 years (compared to the historical average of that measure over time.) This is, after all, the point of the CAPE ratio – it recognises that earnings are cyclical and that a simple PE ratio isn’t that predictive. In other words, recent earnings are high by compared to the last ten years.

Still, none of this is really good news for future performance on the S&P. However, is the situation really as dire as it seems? What impact did the 2008/2009 earnings period have? Do the extremely low interest rates in the US have a part to play here?

Any time part of a measure includes several prior years, it is worth considering what happened in that ten period and if there are any specific base year effects. The only one I’ll mention here is that the cyclically adjusted earnings part still includes the catastrophically low earnings period around 2008/2009. In another 6 to 12 months most of this effect will have disappeared and the CAPE ratio will decline all on its own

My prospective Equity Risk Premium (ERP) estimation tool takes US 10 year nominal yields (1.6%) and real yields (0.2%), the current S&P500 dividend yield (2.0%) and expected real GDP growth of 2% to realise an ERP of 3.8%.

The GDP forecast is the crucially subjective estimate. With population growth of just 0.7% per annum, estimates for medium term GDP growth of between 2% and 3% imply fairly significant per capita growth so I’m more comfortable with 2% than 3%. The 0.8% decline in population growth over the last 50 years (from 1.5% in the 1960s) means some gut feel estimates of achievable GDP growth still reflect this higher growth period.

As a sensitivity on this uncertain input, 3% GDP growth implies an ERP of 4.8%.

The reasonable long term range for an ERP is either 2% to 4% or 3% to 5% depending on who you ask. (There are also those who mis-estimate this and end up with 8% to 10%, but I’ve blogged extensively before on the ERP estimation flaws that are required to get those estimates.)

Whichever range you work with, actual market implied ERPs of 3.8% to 4.8% do not look unusually low, and therefore do not suggest the US market is quite as expensive as some other measures suggest. What is missing here is a time series of market implied ERPs.

In summary, the US market is likely expensive, and there are enough risk factors around to concern an investor. However, I don’t think it’s quite as dire as some have indicated.

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.

What impact will the loss of Bitcoins over time have on the economy?

Why Bitcoin as a national currency will damage the economy

One of my concerns with Bitcoin as a national or universal currency is actually something proponents claim as a benefit. There is a finite number of Bitcoins that will ever be available. At the moment, the increase in the supply of Bitcoins is a positive number. Even with that, due to hoarding of Bitcoins and a little bubble mania, the price of virtually everything measured in Bitcoins is experience hyper deflation. (If that sounds odd, spend a minute thinking about it. We are used to thinking of the price of Bitcoins in Sterling or USD or ZAR, rather than the price of eggs or houses in Bitcoins.)

Deflation

Deflation of prices measured in Bitcoins is virtually assured in the long term, provided the global economy continues to grow. More stuff being bought, more services provided, more people being paid, more assets existing being chased by a finite number of bitcoins will lead to deflation. Which in turn leads to hoarding, unavoidably high real interest rates and contractionary pressure on the economy. This is a slice of Japan over the last 30 years.

But… negative interest rates?

We have limited negative nominal interest rates in certain markets at the moment. To be fair, this was unexpected when it happened some years ago, but is now relatively normal. Not all of the reasons for negative interest rates in Euro denominated sovereign bonds will apply to Bitcoin though, so I don’t believe that will be an out from high (positive!) real interest rates.

Alternative view, Bitcoins are infinitely substitutable

The aside I traditionally make at this point is that Bitcoin can have infinitely many substitutes. So the risk may be one of deflation, but could also be of eventual progression to nil value due to substitutes.

What to do about lost Bitcoins?

Now onto the point of this post. Bitcoins get “lost” all the time. You lose your wallet encryption key and the Bitcoins are gone forever. With fiat currency, the total money supply can be managed so that if somehow a fire burnt up a meaningful part of the money supply, more money could be created. It would still be a loss for those who lost the money, but at least the economy wouldn’t have to stumble along with a decreased money supply indefinitely.

If Bitcoins are lost at a positive probability per year, this will decrease the supply, adding to the deflationary problems described above. Yes, this is mostly an academic point because the targeted real rate of economy growth is likely way higher than the rate of loss of Bitcoins, so the contribution to the overall problem is small.

My suggestion? Do the world economy some good and give me your encryption keys for safe keeping. Just in case.

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.

This has nothing to do with the Famous Five. While it is most common in the general insurance space, it is relevant across the spectrum of risk management and assumption setting.

Events Not In Data or “ENID” is the forgotten cousin of “what to do with outliers in your data”.

Outliers and where to find them

Outliers are observed values substantially different from others in a sample. Some more formal definitions include:

“An outlier is an observation that lies an abnormal distance from other values in a random sample from a population”

“an outlier is an observation point that is distant from other observations”

How to deal with outliers?

Simple question, complex answer. It depends a great deal on the context.

Ultimately you need to make the judgement call “are these outliers under- or over-represented in the data”.

If you believe you have a 1 in 100 year event in your 3 year data set, then leaving it in unadjusted will skew your average (as well as the rest of the distribution) away from the true underlying population distribution.

However, it is also key to ask yourself if there are Events Not In Data (ENID) that would be part of the underlying population distribution. Most 3 year samples won’t include a 1 in 100 year event and therefore might understate the average and bias the distribution.

I believe it can be good practice to consider the data with and without the data point. And without could mean capping or omitting entirely. The cut-off point for outliers can be expressed as a multiple of assumed standard deviations based on how much data you have in your sample. It’s obviously also circular in that your estimate of standard deviation depends on whether or not you include the outlier in the estimate of standard deviation!

I said at the start, it depends on the context and the underlying variable.

Putting Events Not In Data into the data

Outliers may be hard to pin down an define exactly, but they usually appear because they make the results look worse than hoped for and there is plenty of pressure to “fix it”. When experience has been good, because those outlying events did not occur, there is naturally less pressure to go looking for trouble.

Except that’s exactly what one should do.

Just because events are slightly away from the mean or occur somewhat less often doesn’t mean they shouldn’t have been considered prior to the pricing / reserving / business forecasting / assumption setting / risk management process. So in theory, the analyst should already have a view (however accurate) about what to expect away from the expectation. That can be a useful starting point to assess whether an allowance for ENID should be made.

Over time, the views on the shape of the distribution and the reasonability of ENID at various levels will be refined. That is after all, the basics of the Actuarial Control Cycle.

ENID and skewness

The world we live in seems to be naturally asymmetrical.  So many of these ENID are on the down side. Including them contributes to greater variance, greater kurtosis and also negative skewness.

Examples of variables exposed to ENID (aka where to find them)

The list is endless and it might be more practical to think of examples where it could not apply. However, just to make sure you are thinking broadly enough, here are a tiny number of examples.

• general insurance claims (the standard example)
• stock market returns
• corporate defaults
• lapse assumptions (upwards spikes in lapses due to economic conditions or regulatory changes are not every year occurrences)
• expense assumptions (where “one off” costs relating to restructuring or retrenchment are very likely to occur over a 15 year projection period, but hopefully didn’t occur in the last 3 year expense investigation period.)
• rainfall and drought (Cape Town planners, I’m looking at you)

Remember this list is incomplete, so there will be ENID in this list of ENID affected variables.

Events not in data – guidance from the Bank of England

Many firms use reserving methods that project forwards
from historical data. On its own, this is unlikely to satisfy the
Directive requirement for a probability-weighted average of
future cash-flows, since not all possible future cash-flows — or
the events that cause them — may be represented in the data.

Although these events are sometimes referred to as
‘binary events’ or ‘extreme events’, such terms suggest that
events not found in the data are necessarily extreme or rare.
This is not the case, so the PRA prefers to use the term ‘events
not in data’, or ENID.

Firms should take ENID into account when calculating
technical provisions. Applying a simple percentage uplift
without justification is not an adequate method.

Where outliers are removed from the data as part of the
reserving process, this removes events from the data. Firms
should make an allowance for this in the technical provisions
calculation unless they have shown that it would not be
possible for these, or similar, events to occur again in future.

This graph brought it home a little to me:

Egypt, despite an Arab Spring has not experienced the same precipitous decline. Nigeria’s recent troubles are now also clear.

Our USD GDP is below the point it was in 2010, offsetting a brief period after 2010 when it was still increasing. So maybe it’s more about the last 5 years than the last 7.

Of course, that is the wrong chart. GDP is affected by population growth and what we experience as individual citizens within a country is closer to GDP per capita.

For that, we need to go back to 2004/2005 for the last time our USD GDP per capita was as low as this. What makes it feel so much worse is that our USD GDP per capita more than doubled from 2002 to 2007. So many of us became familiar with the feeling of growing wealth, of good times and progress.

I know that that period of growth still left many in South Africa behind. While significant gains were made in service delivery and social grants and most of the population probably did feel better off, the gains were not as deep as they needed to be.  I suspect this was a mixture of policy and the beginnings of fraud and corruption on the scale we see today.

What will turn this around? Gold is no longer a major earner, coal is becoming persona non grata, uranium is looking its appeal as solar, wind and other renewables come to the fore. Climate change is likely to hit our dry country worse than some other areas and thefore dent agricultural production.

I’m not convinced we have 7 years of Feast ahead of us.