I once read that meteorologists are better calibrated forecasters than stock pickers. By calibrated, I do not mean they are better or worse at forecasting, but that their level of confidence matches their accuracy. When they believe they are 80 % likely to be right, they are right about 80 % of the time. When they are uncertain, their forecasts are indeed less accurate. Stock pickers, and I suspect some economists, tend to be less well calibrated. They are not necessarily poor forecasters, but they are often overconfident, and that is a different problem altogether.

The point came from Superforecasting by Philip Tetlock and Dan Gardner, which explored how some individuals become remarkably well-calibrated forecasters through constant feedback and disciplined self-assessment. One reason meteorologists perform better is feedback. Stock pickers and economists can always point to exceptional events that explain why forecasts went wrong. Who knew there would be a drought, or that Russia would invade Ukraine, or that COVID-19 would shut down economies? There is always a convenient exogenous shock. Weather forecasters do not have that luxury. They live by feedback. They issue forecasts every day and are judged immediately by outcomes they cannot explain away. Their models evolve constantly because their results are tested constantly.

That contrast came to mind when I first encountered Computable General Equilibrium (CGE) models about a decade ago. They were intriguing. A little too clever, perhaps, and impressive in their scale. Thousands of equations, countless parameters, and from all of that emerges the predicted effect of an intervention on GDP, employment, or wages—sometimes quoted to two decimal places. At the time, it seemed remarkable, but I could not see the transparency I wanted. There was little evidence of how these models were calibrated, how reliable they were, or how confident one should be in their results.

I came across them again recently. I am not saying CGE models are unhelpful or that their results are wrong. They can be useful for exploring direction and mechanism – how substitution between sectors or between labour and capital might unfold. They are elegant frameworks for thinking through equilibrium interactions. What concerns me is the way they are sometimes used. There is often little effort to quantify uncertainty, measure accuracy, or report diagnostics that would tell us how well the model is performing. That makes me uneasy.

I would like to see genuinely robust studies that take a CGE model, generate predictions, and check them against what actually happens. Yes, there will always be surprises, but if the models barely outperform a simple trend line, the enormous complexity and opacity seem hard to justify.

Even before that, I would want to see what the model predicts with no intervention at all. CGE models are built to find a general equilibrium, but there is no guarantee the economy we feed in is itself in equilibrium. The first step should surely be to see what the model does on its own. If its natural equilibrium is very different from the starting point, that tells us something. Either the real economy is far from equilibrium, or the model’s structure is not reflecting it well.

I am not suggesting this problem occurs frequently, but the diagnostic results are rarely reported, and that makes me wonder whether it sometimes does. If the model is not allowed to reach its internal equilibrium before the intervention is applied, the change in GDP or other results could be partly driven by the model’s own adjustment toward equilibrium. It would be useful to know.

I would also like to see how stable the results are when the model is run with successive years’ data. If we input the economy as it stood each year, do the parameters and balances remain consistent? If not, that raises questions about the model’s stability. For example, can it predict the next year’s social accounting matrix (the SAM) with any meaningful accuracy? If it cannot, then I am not sure how much confidence we can place in the counterfactual results.

As I understand it, CGE models are calibrated rather than statistically estimated. They rely on factual data inputs, some parameters solved to achieve equilibrium, and elasticities imported from previous studies. Those elasticities differ across the literature, and the uncertainty around them is often considerable. How confident are we in those numbers? What happens if we vary them within plausible ranges? If small changes produce very different equilibria even before any intervention, then those elasticities are critical. If the results remain fairly stable, that is reassuring.

We could also test how much the estimated impact of a policy or intervention depends on these elasticity choices. If the results swing widely, confidence in the model’s precision should be low. And if the model’s internal uncertainty interval for GDP, wages, or productivity is wider than the intervention effect, that should be stated clearly.

In actuarial work, we routinely perform analysis of surplus to understand why results differ from expectations. We ask whether deviations stem from data, assumptions, or the model itself. That discipline drives improvement and professional scepticism.

Actuaries are not perfect. There are areas of our own work where experience analysis or model validation is weak. Some solvency projections (ORSA AvE notably, but there are others) , for example, are never properly reconciled to outcomes. That is disappointing, but at least the mindset of testing and validation is embedded in the profession. The actuarial control cycle is still taught.

We are also learning from “new” data science, which takes validation seriously. Models are trained and tested on different data, evaluated out of sample and out of time, and using techniques such as Monte Carlo Cross Validation (a new discovery for me) or k-fold cross-validation. These approaches help ensure that models perform reliably, not just on the data that built them but on data they have never seen.

I would like to see economists apply similar rigour to CGE modelling. Calibration is not validation. Without testing, backtesting, and clear communication of uncertainty, the apparent precision of these models can be misleading.

CGE models can be valuable tools, but their uncertainties and dependencies are often poorly understood, poorly expressed, and underappreciated.

Perhaps I am missing something. Perhaps these diagnostic checks are done quietly somewhere. I would be glad to be corrected by economists who understand these models better than I do. I am genuinely open to learning more.

Because if these models can withstand that kind of scrutiny, they deserve confidence. If they cannot, they still have value, but we should be honest about what they are: structured thought experiments with useful stories to understand. Not forecasts – and those second decimals places should never be shown.

This raises an important question: Should developing markets, including South Africa, adopt SFCR-style public disclosures? How do weigh the costs and benefits, and is this calculus different than in Europe?

The Case for Public SFCR Reporting

Enhancing Industry-Wide Risk Management

• Public disclosures let insurers benchmark themselves against their peers, highlighting best practices and exposing weaknesses.
• Insurers gain valuable insights into what “good” looks like, thus driving overall improvements in industry risk management standards.
• To my own interests, having more detailed information to understand the insurance sector and perform benchmarking would be invaluable. Hopefully my work has some value for individual insurers and maybe even the industry as a whole, but I recognise this point may have less weight for others.

Transparency and Trust

• Detailed reports provide analysts and policyholders with greater clarity into insurers’ operations, solvency, and risk strategies.
• It becomes significantly more challenging for insurers to differently represent (a range from gentle positioning to heavy spin to outright misrepresentation) their financial or risk positions to different stakeholders such as management, control functions, boards, analysts, and regulators when comprehensive information is publicly available.

Better Stakeholder Discipline

• Enhanced transparency makes it more difficult for insurers to conceal emerging solvency or risk issues, thus prompting earlier and more effective regulatory or market intervention.
• Analysts and rating agencies benefit from having direct access to consistent, detailed data, promoting market discipline and investor confidence.

The Downsides and Challenges

Cost and Complexity

• Producing detailed SFCR-style reports is resource-intensive, requiring substantial actuarial expertise, time, and money—resources that are often scarce in developing markets. This is not generally true in South Africa, but is absolutely true across the rest of the continent. Anyway, just because there are resources in South Africa doesn’t automatically mean this is the best use of their time, or that additional demands on these resources won’t impact the supply-demand equating level of salaries and therefore costs for insurers.
• Many insurers in developing markets face significant skills shortages, making it challenging to produce consistently high-quality reports. The level of current internal reporting could benefit from additional resources and time as it is.

Competitive Sensitivities

• Public disclosures risk exposing sensitive strategic insights to competitors, potentially placing companies at a disadvantage in competitive markets. This is often mentioned by insurers – it came out with the original IFRS4 disclosure requirements and again with the IFRS17 disclosure requirements.
• The thing is – I don’t know how many people trawl through competitor financial disclosures to uncover secret strategic source. I’m not dismissing the point, but I am questioning how much of an issue this is. With staff turnover and rotation through industry, there are plenty of mechanisms for more crucial practices to disperse across insurers.

Quality and Utility Concerns

• My experience across large numbers of South African insurers suggests that many insurers already go through the motions, incurring costs without value, in producing ORSA (Own Risk and Solvency Assessment) reports that are not used internally for anything other than compliance.
• Without careful oversight, SFCR-style reports risk becoming tick-box exercises—costly documents that serve regulatory compliance rather than genuine risk management.

Finding the Right Balance

Considering these points, adopting SFCR-style public reporting in South Africa and other developing markets should be approached cautiously:

• Incremental Implementation: Gradually introduce public disclosures, starting with key sections but with a clear roadmap so that insurers know now what they are building towards. There is merit in starting and producing something rather than having endless projects to produce some grand opus in 5 years’ time.
• Proportionality Principle: Ensure reporting requirements align with the insurer’s size and complexity – but this can’t mean that small insurers do nothing. The relevance of risks to each insurers must be considered.
• Standardisation with Flexibility: Provide clear reporting templates to minimise redundancy, enabling insurers to leverage internal reports such as ORSAs, thereby enhancing ongoing risk management practices. There is value in allowing insurers to customise their approach, especially for an ORSA, so that it is most useful for their internal purposes. However, the SFCR is an external document. There is arguably greater merit in standardisation for the reader (ease of navigation, ease of comparability) and for the producer (less time spent changing structure and content and wondering what is expected). Sometimes paint by numbers can great bang for buck.

Final Thoughts

Public SFCR reporting undeniably offers valuable transparency, improves risk management practices, and strengthens market discipline. However, the real challenge is striking a balance—achieving meaningful disclosures without imposing excessive burdens. If implemented thoughtfully, tailored to market realities, and aligned with insurers’ practical capacities, SFCR-style reports could become an essential part of strengthening insurance markets in South Africa and beyond.

In a world where even detailed internal reports like the ORSA are often unread compliance artefacts, is it naïve to think public SFCRs will be any better? Maybe. But transparency has a strange way of forcing people to care. It may be that the SFCR, being publicly available to analysts, regulators, academic researchers, students, and consultants (!) will find more traction and more use than most ORSAs.

The act of writing for an external audience can clean up fuzzy thinking and force clearer articulation of risk positions—something that internal-only reports often fail to achieve. It’s one thing to desire diverse views on a Board, but group-think and anchoring are all too common. I’ve lost track of the number of times the discipline of writing things down has made me realise the ideas in my head weren’t quite as brilliant or even consistent as I’d thought.

Perhaps SFCRs can do that at scale.

This article outlines a collection of common and problematic pitfalls I’ve seen in ORSA stress and scenario testing, capital modelling, and governance. Some are technical, some cultural, and all are worth addressing if we want the ORSA to do what it should: support better decision-making under uncertainty.

These insights reflect my experience across a wide range (and varying quality) of ORSAs, including independent reviews, informal and formal regulatory feedback (including from the Prudential Authority), informal discussions with regulators, and public statements from supervisors across multiple jurisdictions.

1. Toothless Scenarios and Soft Stresses

• Many ORSA scenarios are too mild to test anything meaningful.
• Often there’s no indication of severity. Is this a 1-in-5 or 1-in-50 event? Without context, interpretation is impossible.
• Firms are sometimes surprised they survive a 1-in-200 scenario, forgetting that survival at that level is by design.

2. Recycled, Stale, or Misaligned Scenarios (and Ignored Emerging Risks)

• Same tired stresses reused each year without meaningful refresh.
• Narrative scenarios assigned numerical calibrations that don’t match the story.
• Horizon scanning is often absent or perfunctory; emerging risks must be systematically identified and tested.
• Scenario testing should anticipate what could plausibly happen next, not merely repeat past events.

3. Implausible or Alienating Scenario Design

• Unrealistic or inconsistent scenarios alienate management and the board.
• Severe scenarios are valuable, but they must be framed with historical precedent or research to be credible.
• Overconfidence in models is dangerous; even the best models can fail catastrophically, as history shows.

4. Over-Engineering vs Usefulness

• Attempting to build the “most accurate” pandemic scenario misunderstands the point: scenarios are for learning and planning, not for prediction.
• Prioritise strategic insight over technical perfection.

5. Investment Returns Detached from Reality

• While not common, flat investment income across stress scenarios is a serious modelling failure.
• Investment returns must reflect changes in asset levels and market conditions under stress.

6. LACDT: Tax Calcs Behaving Badly

• Deferred tax recoverability often lacks robust testing.
• Future stressed profits must first create a DTA before any LACDT benefit can be recognised. Tiering here can hit you – more than you considered for the base SCR calc and your QRT.
• Income vs capital gains treatment and tax fund nuances are often overlooked.
• Just because LACDT can’t be negative (per the FSIs), doesn’t mean you can’t have existing DTAs fail recoverability testing in a stress and have loss amplification from deferred taxes!

7. Tiering and Fungibility Constraints Not Considered

• Capital tiering restrictions often ignored under stress.
• Assumed fungibility between entities or tiers can be unrealistic, especially under stress scenarios.
• See the point about DTA and tiering above too.

8. Over-Reliance on Standard Formula Extrapolation

• Normal distribution assumptions are often inappropriate; t-distributions, Lognormal, Pareto tails, or piecewise fittings are better suited.
• Ideally your own experience should be able to inform 1 in 10 stresses and act as a sanity check on scaled 1-in-200 stresses.
• Thin historical experience leads to poor calibration of rare-event risks, especially for equity markets.
• And really, there are several standard formula stresses that are probably not appropriate as a starting point. Some non-life cat stresses may be too conservative – and mass lapse has its critics, but life cat risk, expense risk, and retrenchment risk stresses are likely too low.

9. Unrealistic Business Volume and Expense Assumptions

• Base cases often adopt stretch targets as certain outcomes.
• Expenses are incorrectly assumed to scale perfectly down with policy volumes, ignoring the reality of fixed costs.

10. Incurred vs Paid Confusion

• Claims incurred and claims paid are routinely confused. The impact on profit vs balance sheet and cash can be counter-intuitve.
• Timing differences, especially under IFRS 17 (LCI/CIP dynamics), matter for liquidity and solvency modelling.

11. Short Projections for Long Risks

• Three-year horizons are insufficient for long-burn risks including the obvious candidate – climate change.
• Five years should be the baseline internally, with qualitative insights over longer horizons. Yes, the reliability decreases as the term increases, but it can still be informative. You may chose to disclose only 3 years more broadly, but the longer view is important to at least understand trends.
• Long-term (10–30 year) qualitative assessments should supplement the ORSA, accounting for amplifying systemic interactions.

12. Disconnect Between ORSA and Management Forecasts

• Management runs the business based on one view; the ORSA is prepared using another.
• Without alignment, the ORSA cannot pass the use test or add value to strategic decision-making.

13. Ignoring Dynamic Risk Interactions

• Risks are often modelled in isolation.
• In reality, correlations and feedback loops matter: lapse impacts guarantees, claims experience shifts reinsurance pricing, and market volatility affects lapse and claims simultaneously.

14. Either No Management Actions, or Superhero Versions

• Some ORSAs model no management actions (overly conservative but unrealistic).
• Others assume immediate, flawless actions without delay or cost (equally unrealistic).

15. Unexplained Profit and NAV Changes

• ORSA profit projections must reconcile to balance sheet movements.
• Adjustments between IFRS and SAM/Solvency II frameworks should be clearly documented.

16. ORSA Process Too Slow to Be Relevant

• A nine-month ORSA development cycle leads to stale outputs.
• ORSA timing must be aligned with the business planning cycle and responsive to external shocks.

17. Weak QA and Model Review

• Detailed, independent model review is often absent.
• Common failures include claims timing mismatches, unrealistic ROEs, omitted asset growth dynamics, and unstated assumption interactions.

18. Boilerplate Overload, Insight Underload

• ORSAs are often bloated with standard wording, burying the important insights.
• Focus must remain on what is changing and what genuinely informs management decisions.

19. No Trigger or Process for Out-of-Cycle ORSA

• Firms sometimes only trigger an out-of-cycle (OOC) ORSAs for an SCR breach — far too late. If the risk or solvency situation (internal or external) has changed, it’s time for an OOC.
• Proportional, trigger-based OOC ORSAs must be defined and actioned when material changes occur.
• An OOC doesn’t need to cover the entire process or the full 80 page report. Just the key parts that have changed.

20. Reverse Stress Testing as an Afterthought

• Reverse stress testing needs to explore genuinely different failure modes, not just ramp up severity.
• Defining what constitutes “failure” (capital breach, strategic collapse, or profitability death spiral) needs careful thought.

21. Weak or Missing Rationale for Scenario Selection

• Documenting why scenarios are chosen reveals how the firm prioritises risk.
• Disconnects between identified risks and tested scenarios highlight critical weaknesses.

22. Board Engagement and Use Test Failures

• Board sign-off without meaningful engagement misses the point.
• Effective risk functions bring ORSA components to the Board repeatedly during the year to drive strategic debate.

In Closing

None of these issues is inevitable. Most stem from habits — some from lack of scrutiny, others from good intentions that weren’t tested hard enough. But if the ORSA is to support real-world resilience, it has to reflect how capital and risk actually behave. That means grounding assumptions, engaging the business, and constantly asking: “Would I act on this?†

If the answer is no, the ORSA needs work. If the answer is yes, you’re on the right track.

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

Regulatory Ambiguity: When Is a Dividend Foreseeable?

The official guidance under Solvency II and SAM states that foreseeable dividends must be deducted from Basic Own Funds (BOF). But when does a dividend become foreseeable?

The European Insurance and Occupational Pensions Authority (EIOPA) defines it as follows:

“A dividend is foreseeable when the payment becomes likely considering the dividend payment history of the company, the business development throughout the year, the reference date of the assessment and, where appropriate, other relevant circumstances.†

Similarly, the South African Prudential Authority (PA) states:

“A dividend is foreseeable at the latest when it is declared or approved by the board of directors, regardless of any requirement for formal approval at an annual general meeting.†

On the surface, this sounds reasonable. But what does “likely† mean in this context? More than a 50% probability? Should a dividend that is merely probable be deducted against a 1-in-200 stress scenario? The dividend itself is not independent of financial stress—if an insurer were actually facing a severe loss event, that dividend likely wouldn’t be paid.

Defining the latest time to recognise a dividend as foreseeable doesn’t help in deciding when a typical or expected time might be. The PA released “technical observations” on this a little while back. Even while taking pains to highlight that technical observations don’t count as regulation, they were still unclear around what is expected.

The crux is that the regulatory guidance provides no clear answer on whether insurers should assume dividends payable from the preceding financial period, or always consider the next 12 months of “likely” or expected dividends. Equally, they also aren’t clear that insurers should not take a multi-year view. Some regulations on subordinated debt require a five-year term to prove permanence. Should insurers also be considering a 3- to 5-year horizon for foreseeable dividends? That doesn’t seem to be expected, but the reasoning and application aren’t consistent across different parts of the regulations.

The Problem of Capital Permanence, Availability, and Loss Absorption

Under Solvency II and SAM, regulatory capital must meet three key criteria:

1. Permanence – Capital should be available for the foreseeable future.
2. Availability – It must be accessible to absorb losses when needed.
3. Loss Absorption – It should genuinely absorb financial shocks.

The rationale in deducting foreseeable dividends is that once a dividend has been communicated to the market or approved by internal management structures, even before shareholder approval, it is nearly impossible not to pay it. That capital is no longer available.

However, requiring insurers to deduct a full year’s dividend in advance assumes earnings have already been generated. If those earnings fail to emerge (as they wouldn’t in a 1-in-200 scenario), then the dividend would likely not be paid. The dividends can absorb these future losses. There’s a parallel here for liquidity risk – Should cash be held now to ensure liquidity for dividends months into the future, even though expected premium receipts will exceed even adverse claims—meaning the dividend could be comfortably funded from future positive cash flow?

Are insurers being asked to treat dividends like senior debt obligations rather than discretionary equity distributions? If so, does that undermine the core purpose of equity funding?

Divergent Industry Practice and Alternative Approaches

Given this uncertainty, industry practice varies widely:

• Many insurers argue that only dividends expected in terms of prior financial periods should be deducted, and then only once the decision has been made to pay the dividend.
• Some insurers take a conservative approach, deducting dividends 12 months ahead, taking a double hit from recently declared dividends and dividends for another year. This depresses reported SCR cover ratios, but should not change absolute required capital levels. Targeted SCR cover levels will often be determined using earnings at risk or economic capital models, or adverse scenarios from an ORSA – all of which will factor in the economic reality that distant future dividends are loss absorbing.
• Other insurers accrue foreseeable dividends based on assumed payout ratio and earnings retained to date. This approach has much to recommend it, including being consistent with many banks’ treatment.

The FCA’s approach under Capital Requirements Regulation (CRR, which applies to banks, not insurers) summarises this last option:

“Before the management body has formally taken a decision or proposed a decision on the distribution of dividends, the amount of foreseeable dividends to be deducted shall equal the amount of interim or year-end profits multiplied by the dividend payout ratio.†

This effectively accrues foreseeable dividends over time rather than imposing a sudden drop in solvency ratios when dividends are declared. While not part of Solvency II or SAM, it is an interesting approach that could bring greater stability to insurance solvency ratios.

Determining SCR Cover Targets: A Practical Approach

Given the uncertainty in regulatory guidance, insurers should ensure that foreseeable dividends are integrated into a broader capital strategy rather than treated as a compliance checkbox. The key is to align foreseeable dividends with SCR cover targets, earnings at risk, and capital models that reflect economic reality.

Rather than simply applying rigid deductions, insurers should consider:

• Economic Capital and Earnings at Risk: Many insurers set target SCR cover ratios based on earnings at risk, ensuring capital sufficiency over a medium-term horizon. Since distant future dividends are inherently loss-absorbing, capital models should reflect that rather than treating them like fixed obligations.
• Scenario-Based Capital Planning: Insurers often use adverse scenario testing to set SCR cover targets. These scenarios should reflect dividend flexibility—how payouts might adjust in stress events rather than assuming mechanical deductions.
• Aligning Regulatory and Economic Views: The disconnect between regulatory capital and economic capital is well known. A structured approach to foreseeable dividends should integrate both perspectives, avoiding artificial volatility in reported solvency while maintaining a robust risk framework.

Insurers that take a strategic approach to SCR cover target setting—factoring in foreseeable dividends dynamically rather than through arbitrary deductions—are better positioned to maintain both solvency resilience and investor confidence. In a regulatory environment that lacks precise guidance, a clear, defensible methodology can differentiate well-managed insurers from the rest.

In a thought-provoking dialogue, Nassim Taleb critiques VaR and highlights the dangers of portfolio insurance and dynamic hedging strategies. Here are key arguments from his 1997 forceful response to Philippe Jorion’s support for VaR.

Misplaced Precision and Concrete Metrics

Taleb warns that the unique precision of VaR creates a false sense of certainty. He describes this as a form of “misplaced concreteness,” where risk managers mistakenly believe they have a comprehensive understanding of potential losses based solely on point estimates. This can lead to dangerous oversimplifications in risk assessment, potentially masking the underlying complexities of market behavior.

The Risks of Portfolio Insurance

Dynamic hedging, often employed in portfolio insurance, is particularly perilous. Taleb argues that these strategies can exacerbate market downturns, relying on flawed statistical models that underestimate tail risks. When events occur that fall outside expected parameters, the repercussions can be catastrophic, as seen in past financial crises where such strategies failed to provide the intended safety net.

Standard Error vs. Point Estimates

A critical issue Taleb raises is the phenomenon where the standard error of a risk estimate can exceed the estimate itself. This stark mismatch reveals the inherent dangers of relying on these calculations. The history of financial crises shows that bizarrely improbable events—deemed unlikely by VaR—frequently materialize, often with devastating consequences that could have been better anticipated with a more qualitative understanding of risk.

Forecasting Volatility

Taleb emphasizes that accurately forecasting volatility is exceptionally challenging. The reliance on historical data and models leads to a blind spot regarding unpredictable market dynamics. This difficulty only compounds the risks associated with tools like VaR and portfolio insurance, which may provide a false sense of security in the face of uncertainty.

The Illusion of Credibility

Moreover, the widespread adoption of VaR among financial institutions is not a measure of scientific credibility. Instead, it often reflects a collective oversight of significant risks, leading to disastrous outcomes. Financial institutions may become overly reliant on VaR, neglecting other qualitative assessments of risk that could better inform their strategies.

While we are all familiar with George Box’s quote, “All models are wrong, but some are useful,” Taleb’s perspective might be paraphrased more pessimistically: “All models are wrong, and most are downright dangerous.” This insight serves as a crucial reminder that while models can aid in decision-making, they are not infallible and should not be the sole basis for risk management.

Conclusion

For 2025, let’s all recognise the limitations of our tools and the potential pitfalls of over-reliance on quantitative metrics. By fostering a deeper understanding of risk through both quantitative and qualitative lenses, we can better prepare for the unpredictable nature of financial markets.

🔗 Explore the full discussion for deeper insights: Nassim Taleb Replies to Philippe Jorion, 1997

Risk Appetite Measures and Limits

Risk appetite articulates the level of risk an organisation is willing to accept in pursuit of its goals. This encompasses various metrics and limits that inform decision-making, balancing the pursuit of opportunities with sound risk management. Clear and transparent risk measures empower organisations to evaluate their risk exposure and make informed decisions.

Common measures might include SCR cover, Earnings at Risk, Maximum Single Loss, Maximum and Minimum claims ratios, among others.

Good vs. Bad Reasons to Change Risk Appetite Measures

Organisations frequently confront pressures to adjust their risk measures. Understanding the motivations behind these changes is crucial for effective governance.

Bad Reasons to Change Risk Measures

1. Risk Normalisation: Organisations can become desensitised to risk, gradually accepting higher levels as “normal.” This often surfaces when:
   • Risk indicators linger in amber or red for extended periods without corrective action.
   • Erosion of margins is attributed to market conditions rather than acknowledged underlying issues.
   • Management pressures lead to subjective adjustments of risk ratings to green, creating a façade of control.
   This normalisation breeds complacency, masking potential crises that may arise when unaddressed risks materialise.
2. Strategic Helplessness: When organisations cite perceived limitations—such as outdated systems or legacy portfolios—as reasons for inaction, they fall into a trap of strategic helplessness. Research by Power, Ashby, and Palermo indicates that this can lead to:
   • Ignoring legacy challenges until they escalate to critical levels.
   • Cultivating a culture that discourages acknowledging risks, perpetuating a cycle of poor decision-making.
3. Cultural Complacency: When risk management becomes an afterthought, adjustments to risk measures may reflect organisational inertia rather than genuine risk appetite. This can result in:
   • Diminished engagement from risk teams who feel sidelined in decision-making.
   • A growing disconnect between stated risk appetites and actual practices.

Good Reasons to Change Risk Measures

In contrast, there are valid motivations for revisiting risk appetite measures:

1. Regulatory Changes: New regulations can necessitate adjustments in risk management practices. The introduction of IFRS 17, for example, represents a significant shift in how insurers recognise earnings and assess risk, prompting a thorough reassessment of existing measures.
2. Evolving Market Conditions: Shifts in the external environment, such as economic fluctuations or emerging risks, may require organisations to recalibrate their risk appetite to remain competitive and responsive.
3. New Data and Insights: Advances in data analytics and innovative thinking can enhance calibration processes, enabling organisations to refine their risk measures more accurately. Incorporating new methodologies allows for a more nuanced understanding of risk exposure and leads to more informed decision-making.
4. Strategic Objectives: As organisations evolve and pursue new goals, reassessing risk appetite becomes essential to ensure alignment with broader business strategies.

Example: IFRS 17

The implementation of IFRS 17 demands changes in limits relating to profit, presenting an opportunity for a broader overhaul of risk management frameworks.

Changes to Earnings Recognition and Volatility

IFRS 17 alters earnings recognition by replacing compulsory margins, zeroisation, and discretionary margins—with potentially dramatic impacts on investment guarantee reserves and related insurance contracts—with the Contractual Service Margin (CSM). Key implications include:

• The CSM applies only to profitable contracts and offsets non-economic assumption changes, potentially increasing overall volatility.
• Insurers with minimal prior margins may experience a decrease in volatility as a result of these changes.
• Different choices regarding risk adjustment levels and classifications of directly attributable expenses will impact the size of the CSM, affecting the assessment of onerous contracts and the degree to which severe stresses can deplete the CSM.

IFRS 17 introduces significant complexities related to risks arising from the CSM:

• Matching the CSM is particularly challenging, especially with how it accrues interest based on forward rates locked in over prior decades.
• Insurers now face more intricate decisions regarding whether to hedge Embedded Value (EV), solvency, or IFRS earnings, necessitating a reevaluation of existing risk management strategies.

These changes may require risk limits to adjust with a new subjective acceptance of risk or could place greater pressure to manage risk elsewhere to offset this new volatility.

By recognising these shifts, organisations can make informed decisions about adjusting their risk appetite measures and limits in a manner that reinforces governance and accountability.

Conclusion

Effective risk management in insurance requires a sophisticated understanding of risk appetite measures and the motivations behind changes to these frameworks. By distinguishing between detrimental reasons for adjustment—such as the pitfalls of risk normalisation and strategic helplessness—versus constructive motivations like regulatory changes, shifts in the market, and additional data for calibration, risk functions can seize the opportunity to enhance their risk management systems.

“You’re worse off relying on misleading information than on not having any information at all. If you give a pilot an altimeter that is sometimes defective he will crash the plane. Give him nothing and he will look out the window. Technology is only safe if it is flawless.”

This post is neither an all out defense or vilification of VaR, but Taleb has made his position pretty clear! I will have a separate post trying to put some of Taleb’s strongest points forward. I’m not yet convinced it is more dangerous than useful.

Background to VaR

Value at Risk (VaR) attracts significant criticism in risk management circles. Many of these criticisms are valid – but are they targeting VaR itself, or just its most basic, flawed (and, unfortunately, common) implementation?

A key criticism is that “VaR intrinsically and dangerously underestimates tails”. Let’s unpack that – What makes VaR dangerous: assuming normal distributions, using limited historical periods, applying parametric methods, and assuming independence. This combination systematically understates tail risks and creates false confidence in risk estimates.

But VaR doesn’t require these simplifying assumptions. Consider:

1. Using empirical distributions (typically via bootstrapping) that capture actual observed tail behavior, or at least fitting distributions that better match higher moments.
2. Including longer historical periods that incorporate significant stress events. (10 day VaR estimated over a year seems bizarre)
3. Applying Extreme Value Theory techniques for modeling beyond observed data
4. Carefully understanding independence and dependence, include where created through use of rolling periods. Estimating confidence intervals is useful.

Other common criticisms include that VaR isn’t sub-additive and that it doesn’t consider the shape of risks or losses beyond the selected percentile.

Alternative measures like Tail VaR (TVaR, also Conditional Tail Expectation or Expected Shortfall) are increasingly popular, including being incorporated into regulatory requirements. It does require greater specification of the tail, but I see this as a feature not a bug.

The key questions for practitioners:

• What explicit and implicit assumptions are you making? Are you aware of the implicit ones (careful, they bite)?
• How do your distributional assumptions compare to empirical data?
• What history are you capturing, and what stress periods might you be missing?
• How are you modeling tail behavior beyond your observed data?
• What dependencies might break down in stress scenarios?
• Are you evaluating the stability and potential error in your estimates?
• What does your complementary stress and scenario testing process tell you about your risk measures?

Some Deeper Problems

Having discussed basic VaR implementation issues, let’s explore more fundamental challenges – ones that even “better” implementations struggle with.

Gaming and Metric Manipulation

“When a measure becomes a target, it ceases to be a good measure” ~ Goodhart’s Law

Risk measures becoming targets fundamentally changes behavior. This isn’t always about deliberate manipulation – it’s about rational responses to incentives that can make the financial system less stable:

• Pegged currencies show deceptively low historical volatility while fundamental pressures build. Traders take on this risk because the VaR measure used under-estimates the risk
• Risk not captured by the measure becomes systematically under-priced. There is a correlation between those opportunities where the risk measure understates the risk and the incentives to explore those opportunities!
• Short written OTM options positions are classic examples – small regular profits mask rare catastrophic losses – especially if this wasn’t factored in volatility in the historical period used to calibrate VaR. (option risk management can and often does go beyond simple VaR though)
• Complex products are designed to exploit specific weaknesses in risk measures
• “Risk-free arbitrage” often means risk has been moved somewhere the metrics don’t capture

Dynamic Estimation Challenges

Markets exhibit complex behaviors that make reliable estimation difficult:

• Volatility clustering, and regime changes in both means and volatilities make naive distribution fitting and VaR estimation less accurate
• GARCH and similar models can help but require careful specification
• Longer data periods capture more regimes but with potential loss of relevance

Systemic Risk through Standardisation

When regulators standardise risk measurement:

• Institutions adopt similar risk management approaches
• Similar triggers/limits create correlated responses
• Market participants react similarly to breaches
• Diversification benefits (and liquidity!) disappear exactly when needed most
• The system becomes more fragile precisely because everyone is using the same risk measures

Expected vs Unexpected Loss

When measuring risk, the distinction between expected and unexpected losses is fundamental. Expected losses should be handled through pricing and provisions – you don’t hold capital against losses you expect.

Capital exists to protect against unexpected adverse outcomes. By “unexpected” I mean the difference between the loss level considered and the mean or expected value.

So should Value at Risk measure:

1. Total potential losses from current value, or
2. Deviations from expected outcomes (unexpected losses)?

When deriving or applying VaR, we must explicitly consider the treatment of expected values. This affects both the calculation and interpretation of your risk measure.

Consider two examples:

Example 1: Equity Risk: With 100 invested, your 99.5th percentile worst outcome over a year might be 60. But if you expect to earn 10, is your VaR 40 or 50? Both are valid measures, but mean very different things for capital adequacy.

Example 2: Insurance Claims: If you expect 100m of claims but face 99.5th percentile potential claims of 150m, is your VaR 50m (above expected) or 150m (total)? Given insurance pricing should allow for expected claims, capital needs to focus on the unexpected component.

One reason this is often overlooked is that when considering short time period VaR (e.g. daily VaR or even 10 day VaR) the mean or expected return is often tiny, practically small enough to ignore. This changes as the time period becomes longer. The mean generally glows linearly with time. Standard deviation and many risk measures, assuming time periods are not perfectly dependent, will grow less than linearly with t, stereotypically sqrt(time) if the time periods are independent (not an assumption to make loosely though!)

Tail VaR (TVaR) handles this more elegantly. By taking the average of losses beyond your threshold, TVaR naturally incorporates the relationship between expected and unexpected components. The tail mean relative to the distribution mean becomes an inherent part of the measure rather than a definitional choice.

This isn’t just theoretical precision:

• Capital should protect against unexpected losses
• Provisions/pricing handle expected losses
• Risk measurement needs to align with this framework
• Different time horizons need consistent treatment, or at least everyone should be aware of why approximations are allowed and when they break down.

The key is being explicit about your treatment of expected values and ensuring consistency between your risk measures and their intended use.

A conclusion?

So is VaR useful or not? I still believe it can be, but it definitely presents dangers. Better understanding is the first step.

Before reading on, pause and consider: What strategy would you use if you were playing this game?

The Optimal Group Strategy

If we’re working as a team to find the number as quickly as possible, what’s our best approach? We need a systematic way to eliminate as many possibilities as we can with each question.

The optimal strategy is binary search – repeatedly halving the possible range by asking if the number is in one half or the other. For a number between 1 and n, this takes approximately logâ‚‚(n) questions. This is provably optimal from an information theory perspective – each yes/no question can at best halve our uncertainty. (This approach might be familiar if you’ve ever studied or used binary sort algorithms in computer science – the same principle of dividing the search space in half each time.)

Why does this work so well? Each question eliminates half the possible numbers, regardless of the answer. After k questions, we’ve reduced the possible range by a factor of 2^k. For example, with a starting range of 1-1000:

• Question 1: >500? Reduces to 500 numbers
• Question 2: >750 or >250? Reduces to 250 numbers
• Question 3: >875 or >375? Reduces to 125 numbers
  And so on…

The Competitive Dynamic

But here’s where it gets interesting. The class isn’t actually trying to find the number as quickly as possible. Each child is competing to be the first to identify it. This transforms our optimization problem into a game theory challenge.

Consider child A’s decision when it’s their turn. They now face a trade-off:

1. Ask a “narrowing” question that helps everyone by reducing the possible range
2. Make a direct guess at the number

This becomes a competitive sequential search problem, touching on three related areas of study:

1. Competitive Search Problems: How do agents search through a solution space when competing to find something first? This appears in various contexts, from R&D races between firms to parallel search algorithms in computer science.
2. Information Cascade Theory: How do individuals make sequential decisions while observing others’ choices? This typically studies how public information influences private decisions, leading to potential herding behavior.
3. Strategic Information Revelation: How do agents decide what information to reveal when that information might help competitors? This is crucial in contexts like patent disclosures and auction bidding.

The optimal individual strategy shifts as the game progresses:

• Early game: The search space is too large for guessing to be rational
• Mid game: There’s a tipping point where direct guesses become more attractive
• Late game: Once the range is sufficiently narrow, direct guesses become optimal

For a rational player, the decision at each turn should be based on:

1. Calculate probability of winning with a direct guess: p = (current range size)^-1
2. Calculate probability of winning later after a narrowing question, accounting for:

• Reduced range size
• Number of other players who might guess correctly before your next turn
• Number of turns until you go again

1. Choose the action with higher expected value

Crucially, this assumes all other players are also playing rationally – a strong assumption that might not hold in practice, especially with younger players!

The Finite Guesses Twist

Here’s another interesting variation: what if players are limited to a fixed number of questions? This creates a fascinating optimization problem even in non-competitive scenarios.

At some point, if you have k questions left and n possible numbers, random guessing becomes better than binary search. The intuition is that binary search might narrow down the range significantly but leave you unable to identify the specific number.

For example, with just two questions left and a range of five numbers, you might be better off making two direct guesses (probability of success = 2/5) rather than two binary search questions that could narrow it to 2 numbers but not identify which one.

The optimal strategy becomes a dynamic programming problem, where at each stage you need to calculate the expected probability of success for:

1. Using a binary search question
2. Making a direct guess

The mathematics behind this optimization is fascinating, but I’ll leave that for another post!

An Interesting Parallel

This competitive dynamic reminds me of the board game Cluedo (Clue in North America). While the optimal strategy is usually to gather information methodically, the game dynamic shifts dramatically when you suspect another player is close to solving the mystery. At this point, making an educated guess at the solution, even with incomplete information, might be your best play despite the penalty for guessing incorrectly.

This perfectly mirrors our number game – when you suspect other players are close to identifying the number, the rational strategy might be to take a calculated risk with a direct guess, even if you’d normally prefer to gather more information.

Final Thoughts

What started as a simple classroom game reveals layers of mathematical and game theoretical complexity. It demonstrates how individual incentives can lead to strategies that are suboptimal for the group – a common theme in game theory.

Have you encountered similar games or puzzles that seem simple at first but reveal hidden complexity? I’d love to hear about them in the comments.

As the parametric insurance market evolves, so too must our approach to capital modeling. The challenges are significant, but so are the opportunities for innovation and more accurate risk assessment.