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?

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

“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.

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.

Even for a leanly capitalised (re)insurer (SCR cover 1.2x) this implies a CQS of 8, better than the largest, most conservatively capitalised insurers in South Africa.

No externally rated insurer or reinsurer in South Africa has a CQS of better than 11 or 12. The table in 10.9 ignores sovereign or country risk in the default risk assessment. Therefore, it significantly understates spread and default risk.

The question here is not whether there is an absolute sovereign cap that no South African entity can be rated above. The issue is that applying this table almost certainly understates the risk and CQS relative to rated entities because it ignores sovereign risk.

In terms of the sovereign cap, the risk of exposure to South Africa is (and should be) factored into the rating for entities with significant exposure (asset, operations, profit sources, regulatory risk, inflation, appropriation et al) in South Africa. This usually results in predominantly South African businesses not having a credit rating better than the sovereign.

There is more to reinsurance optimisation that interpreting the FSIs. The application of the sovereign cap is also mostly a distraction from the choices of reinsurer and reinsurance programme to manage risk and capital.

#capitalmanagement #reinsurance #CQS #optimisation #sovereigncap #creditrisk

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

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

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

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

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

At a minimum, the contribution to group surplus/deficit Own Funds (in excess of, or the deficit where Own Funds don’t cover the SCR), should be shocked for currency risk. This makes sense as soon as you think about what the risk to the group’s SCR cover is on currency depreciation. (Where there is a deficit, foreign currency appreciation is the risk, not depreciation. The opposite is true – and more intuitive – when there is a deficit.)

The final answer is that ICS will likely not be applied to everyone in South Africa, but it may inform the development of SAM group reporting. It may also be the basis of choice for subsidiaries in other jurisdictions. ICS is probably more relevant than you thought.

It outlines the requirement to stress life IBNR provisions using the standard life underwriting module. I was integral in the authoring of the APN, so I thought it might be helpful to create this FAQ as a reference.

Frequently Asked Questions

Question: Is there a regulatory requirement to comply with the APN?

This is actuarial guidance issued by the Actuarial Society of South Africa. It is not regulation issued by the Prudential Authority (PA). The PA is the only one that can create regulations.

The APN is an Advisory Practice Note and is therefore recommended practice. It applies to actuaries rather than insurers. Departure from an APN requires motivation by the actuary. Since a large part of the reason for issuing the APN is to ensure consistency of application of the regulations by actuaries, I don’t expect there to be valid reasons to not comply.

The APN is consistent with the Financial Soundness Standards for Insurers (FSIs), although it does provide more specific guidance in areas where the FSIs are not clear.

Question: Will this require me to change what I have been doing in the past?

That depends on whether you were stressing life IBNR provisions when determining your Solvency Capital Requirement (SCR) in the past. Many insurers were already doing this. Some were not, and actuaries calculating or reviewing the SCR for these insurers will need to change their practices in order to comply with the APN.

Question: Will this have a large impact?

For many insurers, the financial impact will be small. For some, it may change your SCR cover ratio by enough to change decisions such as dividend payments, or the cost of capital used in pricing and performance measurement. Insurers more likely to be affected include those with:

• Significant retrenchment risk exposure. The retrenchment stress (50%) is large and IBNRs for these lines can be meaningful. If there is already significant retrenchment exposure, the impact of diversification may be modest.
• Those with significant IBNR provisions relative to overall provisions. This includes group risk, risk premium reinsurance, and sometimes short contract boundary funeral. (Credit life usually has fairly short claim delays, aside from retrenchment, but can also generate significant IBNR provisions depending on business model and reporting processes.)

The operational process to include a stress for the provisions is not onerous, although valuation processes and documentation may need to be updated to reflect the changes.

Question: Is this the correct stress? Surely IBNR provisions are exposed to different uncertainties than future claims?

The SCR standard formula is an approximate formula intended to be risk-based and broadly proportionate to the risks. It is a fair comment that the risk inherent in IBNR reporting delays and claim amounts is different from the risk of future claim events. However, it is clear that there is uncertainty within the IBNR and in line with the principles of holding capital against potential future change in Basic Own Funds (BOF) some stress is required.

There can be some potential double counting of risk for policies that have incurred a claim, but since it has not yet been reported it is also included in the prospective Best Estimate Liability (BEL) and SCR stresses on that. However, there is no evidence that this will reflect the correct stress either. It is easy to show that with high lapses, or a closed book, the uncertainty inherent in the IBNR is understated (or assumed to be zero) when the risk remains.

Overall, stressing the life IBNR in line with the standard formula life underwriting stresses is appropriate and in line with the FSIs.

If your risk is meaningfully different, it may be appropriate to consider an internal model. It seems incredibly unlikely that differences on life IBNR SCR alone with be enough to justify this complex and expensive decision.

Question: Non-life IBNRs are stressed and have a whole methodology to stress them. Why don’t we follow that approach?

Correct. Non-life IBNRs are stressed as part of the claim reserve risk. This also demonstrates the necessity of stressing IBNRs as a general principle. There are differences in the nature of the uncertainty between life and non-life. In general, non-life has additional uncertainty around the final claim settlement amount (including claim, allocated loss adjustment expenses, and legal expense), whereas many life insurance claims are for a defined amount. However, some life insurance claims (e.g. disability or retrenchment) can have an uncertain claim amount, and all life insurance claims have a probability of being repudiated. Both life and non-life have uncertainty in the number of claims that have occurred, with estimation uncertainty linked to variable reporting delays.

The FSIs for life underwriting risk do not allow for a non-life claim reserve SCR approach. ASSA cannot issue guidance to create new regulations. A dedicated claim reserve module would also require additional calibration, which has not been performed.

I do not expect changes to the FSIs to require a non-life style approach to life IBNRs.

Question: Should reporting claims provisions also be stressed?

Life insurers hold provisions for reported claims that haven’t been settled yet. For the most part, reported claims are assessed and settled promptly. However, there is still residual uncertainty around repudiation rates.

• If your practice is to raise the full provision for reported claims, the risk is only of over-provisioning. In this case, it is likely not appropriate to hold additional capital anyway.

• If your provisioning process already allows for the probability of repudiating claims, there is some downside risk possible. However, APN116 does not require you to stress this provision. You may want to consider whether the risk is meaningful.

Some claim provisions such as retrenchment claims in payment are exposed to uncertainty, yet there is no claim termination stress for these claims. This likely represents a separate limitation of the standard formula. This issue is also not covered by the Information Note on retrenchment risk. If retrenchment risk is significant for you, it may be appropriate to assess this when you assess the appropriateness of the standard formula and when conducting your Own Risk and Solvency Assessment (ORSA).

APN116 for reference