But for many, this promise is being hindered by a foundational legal concept: The Principle of Indemnity.

By insisting that property insurance must always be a contract of indemnity (meaning you cannot recover more than your actual, audited loss) regulators have forced the industry into a structural kludge known as the “Dual Trigger.” It’s a legal “fix” that satisfies the status quo but creates a cascade of inefficiencies for insurers and consumers alike.

The Mechanism of the “Dual Trigger”

In a rational parametric model, the data event is the payout. In the regulated world, however, two hurdles must be cleared:

1. The Data Trigger: The physical event occurs (e.g., wind speed, rainfall).
2. The Indemnity Proof: The policyholder must provide evidence that their actual loss equals or exceeds the payout.

This second trigger creates what we might call the Indemnity Trap. It caps the payout at the lower of the two values, fundamentally changing the nature of the risk.

Where the Principle of Indemnity comes from – and why it is a good idea in traditional insurance

Traditional insurance needs indemnity. It ensures the contract restores you rather than enriching you. In the non-life market, we insure the uncertainty of a loss. We don’t just insure the occurrence of an event.

If you could collect a payout that far exceeded your actual loss, you’ve moved from a safety net to a lottery ticket. This “Lotto Effect” turns insurance into a legally sanctioned wager. That windfall potential creates a toxic moral hazard. It invites fraud like arson or staged theft. It also rewards negligence. Why protect an asset when you are worth more if it burns?

By capping payouts at the Ultimate Net Loss, we align the policyholder’s interests with the asset’s survival. Insurance remains a stabilizing force. It protects wealth. It doesn’t generate profit from destruction.

The Problem: Asymmetric Basis Risk

This structure creates a profound misalignment. When we layer an indemnity cap onto a parametric trigger, we create a one-way street of risk:

• When the data misses: If the storm causes massive damage but the sensor doesn’t hit the trigger, the policyholder gets nothing. This is the “Negative Basis Risk” everyone acknowledges.
• When the data hits: If the sensor hits the trigger but the physical damage is light (perhaps because the owner invested in resilience), the indemnity rule steps in and caps the payout.

The result is a structure where the payout can be lower than the data suggests, but never higher. This isn’t a malicious choice by insurers; it is a structural constraint that leaves the risk transfer incomplete. It also reintroduces the very thing parametrics were meant to kill: payout delays. The moment you require a loss audit, the “instant cash” benefit of the parametric model is lost to the administrative friction of the indemnity process.

The Pricing and Underwriting Friction

This isn’t just a headache for policyholders; it complicates pricing.

To price a “clean” parametric policy, an actuary only needs weather data. But to price a policy with an indemnity cap, they must also predict the probability of the cap being hit. This requires traditional, granular underwriting of the asset. We’ve replaced a low-cost, scalable model with a high-cost, bespoke one, simply to satisfy a legal definition.

Assessing the Regulatory Responses

Why do regulators cling to the indemnity requirement? While the intentions are often centered on market stability, the logic behind these defenses deserves a closer look.

Argument 1: The Mitigation Incentive The traditional logic is that indemnity prevents moral hazard. The fear is that if people “profit” from a disaster, they will want the disaster to happen. However, this overlooks a critical reality of resilience. Traditional indemnity insurance actually discourages mitigation. If you spend your own capital to save your factory with sandbags, your indemnity payout simply drops to match your lower loss. In a parametric model without an indemnity cap, you are rewarded for that foresight. You keep the surplus as a “resilience dividend.” The current rules are, in effect, a structural barrier to climate adaptation.

Argument 2: Speculation vs. Insurable Interest There is a concern that without a proof of loss, insurance becomes a “Lotto” or a wager on the weather. But the gatekeeper against speculation should be Insurable Interest, not Indemnity. If a buyer demonstrates a legitimate economic exposure to the event at the point of sale, the speculative element is already addressed. We do not need a cumbersome audit at the back-end to solve a licensing and gatekeeping question at the front-end.

Argument 3: The Life Insurance Precedent It is often argued that property must be treated differently from life insurance because assets have a market value that must not be exceeded. Yet, the Life, Disability, and Critical Illness sectors function perfectly well as “valued contracts.” These are multi-trillion dollar industries that rely on Insurable Interest and a Reasonable Sum Assured. There is no fundamental logical reason why a crop, a solar farm, or a retail business could not be treated with the same “valued contract” logic we already apply to human life.

The Path Forward: The “Ought”

We shouldn’t be trying to “fix” parametric insurance by adding indemnity caps. We should be updating the regulatory framework to recognize Index-Based Insurance as a distinct legal category.

A modern, rational framework would require three things:

1. Provable Insurable Interest (Ensuring the buyer has skin in the game).
2. Reasonable Sum Assured (A cap based on total economic exposure, not just physical damage).
3. Objective, Independent Data Triggers that are demonstrably correlated with the risk exposure

The current “Dual Trigger” system isn’t a design choice; it’s a symptom of a regulatory system that hasn’t changed fast enough. I’d argue the regulations are focused too much on the potential cost and risk of change, while glossing over the downsides of not changing.

Is it time to stop forcing 21st-century risk tools into a 19th-century legal box?

Maybe. But maybe not. Let me take the scenic route to explaining why.

Driving my daughter to school, I occasionally spot someone tailgating or cutting across a solid line. What’s striking is how often within seconds I’ll see them make a second and third thoughtless move. Weaving without indicating, pushing into the off-ramp queue at the last moment – all the sorts of things nobody does in the queue at Woolies when there is accountability. Ring of Gyges stuff – and frankly unfair that both types of people populate this universe.

Yes, there’s confirmation bias here. I’m sure I make mistakes that annoy others. Attribution bias too – we forgive our own lapses as innocent mistakes while judging others harshly. I don’t think that’s all of it though.

What we’re observing is that observations are usually not independent. Errors cluster. They share root causes, be it time pressure, risk tolerance, cell phone use, attitudes towards others, personality, upbringing and more. Whatever produces one lapse doesn’t reset between intersections. One observation carries information about an underlying factor you can’t directly see.

When you see a pattern, update your priors on what comes next.

Back to that not-at-fault claim. A claim happened. And empirically, one claim predicts the next. The insurer often doesn’t know why, but the signal is there in the data. Maybe your car is parked on a busy road. Maybe it’s closer to a corner than ideal, or near a distracting intersection. Maybe street lighting is poor. Maybe it’s just a high-traffic area where the probability of someone having a bad day near your vehicle is elevated. As more complex analysis tools (yes including AI…) are applied, with more data and more context, we may get closer to understanding the hidden risk factors underneath the high level outcomes. Either way, the correlation is there.

The customer likely did nothing wrong. The premium increase can still reflect rational Bayesian updating.

Now, is it always this principled? No. Sometimes it’s crude loss-ratio management. The insurer just wants to recover what they paid out over time. Sometimes the pricing model can’t even distinguish at-fault from not-at-fault, so everything gets the same treatment. These practices exist, and they’re harder to defend.

The legitimate version, where claims predict future claims for reasons the policyholder can’t fully observe or control, that’s real too. And if the signal is real, not adjusting the premium means other policyholders cross-subsidise the risk. The question isn’t whether someone pays, but who.

If you feel this is still unfair, well I think you’re correct on some level, albeit not yet a practical one:

All risk rating involves a choice about what “fair” means. Is it fair that you pay a price tailored to your risk, even if that risk stems from factors you didn’t choose? Or is it fairer that we all pay the same, pooling our luck and misfortune together? The young driver pays more not because they decided to be nineteen, but because nineteen-year-olds crash more often. Risk-based fairness says differentiate. Solidarity-based fairness says pool.

These aren’t the same definition, and better maths won’t reconcile them. We try to draw lines, some factors “feel” acceptable, others don’t, but those lines are social and political choices, not mathematical ones. Therefore they will differ between people, between firms, and over time.

So next time your premium moves in a way that feels unjust, ask whether the insurer is being lazy or seeing something real. But also ask who else would pay if you didn’t. Insurance is a group exercise, and the maths doesn’t care about fault. Only we do.

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.

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.

Understanding the Basics: Banks vs Insurers

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

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

Sources of Liquidity Risk for Insurers

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

Derivatives and Modern Asset Management

Modern investment strategies create significant liquidity demands:

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

Policy Surrenders and Lapses

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

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

Internal Hedging and Optimisation

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

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

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

Claims Concentration

Sudden spikes in claims can create liquidity pressure:

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

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

Premium Collection Disruption

Disruption to premium income can occur through:

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

Investment Portfolio Liquidity

Asset liquidity can become constrained through:

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

Regulatory plans for improved liquidity risk management and reporting for insurers

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

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

The High-Quality Liquid Assets (HQLA) Paradox

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

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

Systemic Risk and Money Market Funds

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

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

Testing Liquidity – Easier Said Than Done

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

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

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

When “Liquidity” Masks Solvency Issues

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

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

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

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

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

Implications for Risk Management

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

These complexities demand sophisticated risk management approaches:

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

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.

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

The Real Purpose of the Equity Symmetric Adjustment

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

Understanding Pro-cyclicality

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

For instance, during a market downturn:

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

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

How the Equity Symmetric Adjustment Works

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

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

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

For example:

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

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

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

Dispelling the Mean Reversion Myth

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

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

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

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

Market Performance After Significant Declines

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

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

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

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

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

Does the ESA increase or decrease risk?

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

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

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

Implications for Insurers: LACDT and DTA Recoverability

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

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

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

Conclusion

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

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

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

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

References

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