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.

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

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

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

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

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

Short aside on government debt and balance sheets

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

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

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

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

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

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

Where are alternative assets used in insurance?

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

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

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

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

How are infrastructure assets treated for insurers for regulatory purposes

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

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

Technical Provisions adjustments for infrastructure assets

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

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

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

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

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

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

Solvency Capital Requirement (SCR) adjustment for infrastructure assets

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

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

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

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

Impact of SCR relief

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

Qualifying criteria

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

Non risk-based criteria

Risk-based criteria

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

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

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

Should insurers invest in infrastructure?

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

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

Many researchers are comfortable with a range for Equity Risk Premiums of between 3% and 5%. Many corporate finance practitioners use a range from 5% to 8% or even higher. My nearly eight-year-old blog post on mis-estimating the ERP covered these differences in detail.

This post is a little different. Forget about what theory says, what are the implications of using a high risk discount rate (RDR) when calculating embedded values and then trying to maximise value.

Solvency II and SAM suggest a 6% (excess over risk-free) cost of non hedgeable capital. Most South African insurers calculating real-world embedded values use risk-free + 3.5% as their RDR.

Some insurers want to use an RDR closer to 15% or even 20%. The problem here is one of conviction. If the cost of capital was truly felt to be 20%, then capital optimisation, value optimisation and therefore reinsurance decisions should be made with this in mind.

It will almost always be the case that reinsurance will have an implied cost of less than 20%. Thus, the consistent action would be to grab as much reinsurance as possible, at least up the point where the reinsurer was concerned about skin in the game.

I don’t see this happening in practice.

Some insurer will argue that they don’t want to give away all their profits to a reinsurer. This fundamentally misunderstands how reinsurance is priced and the impact of return and profit commissions to facilitate reasonable commercial terms.

Similarly, the pursuit of greater investment returns usually results in more risk and more capital required. At a 20% return on capital requirement, pretty much no avoidable market risk should be retained. Yet I still see insurers opting to take on more credit risk (even at current depressed credit spreads) in pursuit of a little extra yield.

We can have a debate about the range of reasonable RDRs to use. But there is a credibility problem if this rate isn’t also used to decide on reinsurance and investment strategies.

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

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

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

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

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

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

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

Take ancillary capital as an example. To my mind, the basic principle is clear. I’ve validated this principle in discussions with Capital Requirements Task Group members, SAM Pillar 1 Subcommittee members, multiple actuaries familiar with the Solvency II principles and delegated acts on which we have based on South African rules. Here is the practical definition of “Ancillary Own Funds†

Ancillary own funds are sources of capital that are not on the balance sheet, but could become Basic Own Funds in certain circumstances. As such, they can still sometimes be used to demonstrate solvency.

Basic Own Funds then are on balance sheet items that contribute capital. These are the excess of assets of total liabilities, with very specific types of subordinated liabilities “added back† because they can absorb losses and meet other criteria. There are a few specific rules about other regulatory deductions form Own Funds, but generally, that is it.

(As an aside, the tiering of capital has almost nothing to do with how your assets are invested, and almost everything to do with the sources of capital. This is another recurring puzzle I find myself explaining a couple of times a month for some reason.)

Here’s one odd thing. Since the Solvency Capital Requirement (SCR) is determined as the change in Basic Own Funds in various adverse scenarios, the possible change in creditworthiness or even outright default of a provider of a letter of credit or guarantee or undrawn loan facility has no impact on the SCR. This is part of the reason the use of Ancillary Own Funds requires explicit approval from the Prudential Authority.

If I were to change the formula, I would add change in Ancillary Own Funds to the SCR. I have yet to see a compelling reason to exclude it.