His point is this: once agents like Meta’s Muse can move money, they could sweep household cash out of US checking accounts paying around 0.1% into accounts paying 3.3% to 5.0%.¹ ² Banks could lose some of the cheap deposits they rely on to lend, and as this is adopted more widely, could significantly change the funding patterns for banks. His example looks to the US, but similar analysis applies in many markets, including South Africa.

The first implication is agents enforcing better information and competition – more efficiency the micro economists will be pleased to note. Superficially, that might put upwards pressure on deposit rates, providing better value for banking customers (at least the depositors) at the cost of the bank or their borrowing customers. Lending rates take time to adjust. In the interim, higher deposit rates being offered may erode bank deposit spread or net interest margin. Thinner margins raise risk for banks too. (Higher risk because lower baseline profits makes it slower to rebuild capital, more likely to make losses, but also because it may prompt greater risk-taking to grow the margin.) Whether you want banks to be more or less profitable is your call. I’m conflicted (in both senses of the word) so I’ll stay quiet on that point.

Next: speed. Lower friction and constant comparison mean small rate differences can drive large flows. Deposits become less sticky. Some of this money stays in the banking system, moving from one bank to another, whereas others may go to money market accounts or that other major 2020s development, the stablecoin, and thus into US treasuries or other pool of generally low risk non-banking assets. The damage is to each individual bank’s funding stability, but potentially also the total available deposit base for the banking system as a whole. (Stablecoins, at least in the US, can’t pay interest, but there are grey areas here related to funds and rewards.)

Slok’s run is yield-driven sorting. But I want to take it a step further – and into risk.

If deposits are now less sticky, banks are more exposed to liquidity shortfalls. Greater risk of failure is definitely relevant to the decision of where to place funds, and agents are likely better suited to evaluating and re-evaluating this quickly. So agents won’t just chase yield. They will become risk-aware too. (I said risk-aware, not self-aware.)

As an individual, it is sensible to want an agent that moves your money into the right risk-adjusted accounts or funds, and quickly away from a bank perceived to be risky. What is logical for you is logical for everyone else. Today, our different information sources, different viewpoints, distractions and delays mean these decisions are for the most part poorly coordinated. Tomorrow, many agents, acting on the same triggers and signals, could pull money out of a single bank at something closer to the speed of high-frequency trading than of a Sunday newspaper reporting last quarter’s filings. Humans have already been getting faster – and I’m not talking about Usain Bolt. On 9 March 2023 Silicon Valley Bank lost over $40 billion of deposits in a day, and expected over $100 billion more the next.³

So agents optimising for yield could raise the risk of a run on certain banks, then respond in a correlated (if not coordinated) manner to that higher risk, amplifying and accelerating the risk.

Deposit insurance limits this, but only partly. A risk-aware agent might keep balances within the insured limit (US$250,000 in the US, R100,000 per depositor per bank under South Africa’s Corporation for Deposit Insurance).⁴ But just because deposit protection exists doesn’t mean there isn’t a frictional cost to being a depositor that wants money here, today and right now rather than wait for regulatory processes. Besides, deposit insurance has its own economics. There isn’t some infinite well of funds to bail out depositors. The point of deposit insurance is to reduce the failures in the first place. But at the limits imposed, it only protects small savers. It concentrates run risk in uninsured balances. Just to explain the scale of the mismatch of funds and insured funds: at Silicon Valley Bank, about 94% of deposits were uninsured.³ To be fair, SVB was an outlier. The peer average was around 41% at the time.

Banks face pressure from two directions. They must pay competitive rates to hold on to deposits. But this newly-volatile deposit funding is riskier than in the past, so they may prefer longer-term funding and be prepared to pay more for it, and less for the now unattractive overnight deposit. Deposit rates might therefore settle lower or higher than today. That depends on risk appetite and on supply and demand, including the impact of non-bank alternatives, the so called shadow banking world, and the impact of stablecoins (and central bank digital currencies in some parts of the world). I don’t think anyone can really know the equilibrium of this complex system in advance. Or if there even is something that could be described as an equilibrium rather than a dynamic moving target.

There is some familiar prisoner’s dilemma stuff going on here. For an individual, using an agent looks clearly positive. Up until many people use the same or correlated agents. Then it may be bad for the system as a whole. One outcome is more concentration in “too big to fail” banks, which carries its own costs. Another is individual bank failures. Either way the transition may be disruptive. But back to the individual prisoner – if the system is newly unstable and risky, isn’t that just a further argument to use an agent?

The difference this time is that every prisoner has hired the same lawyer.

¹ Torsten Slok, “Is an Agentic Bank Run Coming?”, Apollo, The Daily Spark, 27 September 2026. https://www.apollo.com/wealth/insights-news/insights/daily-spark/is-an-agentic-bank-run-coming

² Plaid, “Plaid powers Meta’s new AI agent, Muse”, 8 September 2026. https://plaid.com/blog/meta-muse/

³ Board of Governors of the Federal Reserve System, “Review of the Federal Reserve’s Supervision and Regulation of Silicon Valley Bank”, April 2023. https://www.federalreserve.gov/publications/files/svb-review-20230428.pdf

⁴ South African Reserve Bank, “CODI Frequently Asked Questions”. https://resbank.co.za/en/home/what-we-do/Deposit-insurance/CODI-Frequently-Asked-Questions

Except taxes aren’t certain. Not the amounts, not the timing, not the rules. And once you start pulling on that thread, the list of things we treat as certain, in our models and in our heads, gets uncomfortably long.

This is that list. It is not comprehensive, and it is not a claim that any of these things will change soon. The point is narrower and more useful: each item is something that felt impossible to imagine changing, right up until it changed. That feeling of impossibility is what this post is about, because the feeling is not evidence.

1. Taxes

South Africa introduced capital gains tax in 2001. Secondary tax on companies became dividends tax in 2012, at 15%, which became 20% in 2017. The corporate rate moved from 28% to 27% in 2023. VAT went from 14% to 15% in 2018, and in 2025 an announced further increase was scrapped after the political fallout, which is its own lesson: even the changes aren’t certain.

For life insurers the changes cut deeper. The four-fund tax basis arrived in 1993, and the risk policy fund was added from 2016, moving individual risk business from the I minus E basis to a corporate-style tax on profits. Anyone who had projected the tax cash flows of a risk book beyond 2016 on the old basis was simply wrong, through no fault of their modelling.

Yet most projection models take this year’s Income Tax Act and apply it to 2060. The amount of tax, the timing of tax, and the rules of tax all change, and they change more often than almost any other assumption we hold fixed.

2. Currency pegs

I worked in Lebanon in 2007. Insurers there held US dollar assets against Lebanese pound benefits, and pound assets against dollar benefits, and told me not to worry. There was no currency risk. The pound was pegged.

They had history on their side. The pound had been fixed at 1,507.5 to the dollar since 1997, a decade of perfect stability by the time I arrived, and it went on to hold for another twelve years. Then, from 2019, it lost more than 98% of its value, and the collapse took the banking system and those mismatched balance sheets with it. Dollar deposits became “lollars”, visible on the bank statement, inaccessible in practice.

Lebanon is not an isolated case. Sterling left the gold standard in 1931. The United States closed the gold window in 1971, ending Bretton Woods. Sterling was forced out of the ERM in 1992. Argentina’s one-to-one convertibility, in place for a decade and written into law, collapsed in 2002. And the Swiss franc’s floor against the euro, not even a peg, just a floor, was abandoned in January 2015 and the franc moved close to 20% in minutes, against the largest currency bloc in the world, in the middle of a trading day.

Against that record sits the Namibian dollar, pegged to the rand since 1993, along with the loti and lilangeni. Those pegs are fine. So far. Every peg on the broken list above was also fine, so far, right up until it wasn’t.

Judging pegs by the ones still standing is exactly the error. These are right truncated observations on data periods of hundreds of years. A peg that hasn’t broken is a censored data point, not a proof of safety. “Still pegged” only means the failure hasn’t been observed yet.

3. The biggest companies

None of the ten largest US companies of 50 years ago is in the top ten today. In 1975 the list was Exxon, General Motors, Ford, Texaco, Mobil, Chevron, Gulf Oil, General Electric, IBM and ITT. Oil and cars and machines, each one an institution nobody expected to be displaced.

Some fell further than merely out of the top ten. Kodak, a giant of that era, went bankrupt in 2012. Sears followed in 2018. General Electric, an original member of the Dow in 1896 and a continuous member from 1907, was removed from the index in 2018 after more than a century.

The investment crowd knows this statistic. It appears in every presentation on diversification and every argument for passive investing. But knowing it and believing it about today’s top ten are different exercises. Each of the 1975 giants looked as permanent then as the current ten look now, and the current ten look very permanent indeed.

4. Government debt

An A rating is meant to correspond to roughly a one in a thousand chance of default within three years. That figure comes from corporate rating transition studies, because the sovereign sample is too small to measure properly. Which is itself the point: there are so few rated sovereigns, and so few defaults among the well-rated ones, that a single Greece breaks the statistics.

And Greece happened. Rated A in 2009, restructured in 2012, with private creditors taking a haircut of more than half in the largest sovereign restructuring in history. Russia was investment grade weeks before its 2022 default. And for anyone who finds those examples comfortably foreign: South Africa defaulted in September 1985, freezing $13.6bn of foreign debt in the standstill that followed the Rubicon speech, with the financial rand resurrected to trap capital at home. The final payment on that 1985 debt was made in 2001, sixteen years later.

The usual response is that a government borrowing in its own currency cannot default, because it can print. Russia defaulted on its rouble-denominated GKOs in 1998. Printing was available. Default happened anyway, because default is a political decision as much as an arithmetic one.

We still discount liabilities on the government curve, hold government bonds at a zero capital charge for spread and default risk, and call the whole arrangement risk free. For most purposes that is a reasonable working convention. It is worth remembering that it is a convention.

5. Regulation

Not just the rules changing, which we half expect and occasionally even get consulted on. The interpretation and application of regulation can shift while the words stay identical.

Foreseeable dividends under SAM is a live example. The prudential standard has not changed. But the expected treatment has moved from deducting dividends from own funds once declared and near certain, to accruing an allowance for the coming year’s dividends through the quarterly QRTs. The SCR is unchanged, own funds are lower, and the reported cover ratio drops, on the same standard, for the same insurer, with the same balance sheet. Europe has seen the same dynamic: EIOPA has revised its guidance on matters like contract boundaries without any change to the underlying Directive.

This kind of shift is difficult to explain to a board. The regulation stayed the same but the answer moved. It sits in almost nobody’s risk register, precisely because the words on the page look so solid, and it arrives through industry letters, technical observations and supervisory feedback rather than through anything a legal review would catch.

6. Which power runs the world

Yes, this one is obvious. Persians, Greeks, Romans, Ottomans, British. Everyone knows empires end.

But obvious is not the same as internalised. A Roman in CE 50 could not have imagined Rome falling, and Rome had another four centuries in the west, and fourteen in Constantinople, of not falling to prove them right. In 1913 Britain ruled the largest empire the world had seen, close to a quarter of the map and of its people, and did not expect it to be dismantled within 50 years. India was gone by 1947 and most of the African colonies by the mid-1960s. The Soviet Union was a superpower until 1991, and then, within months, not a country.

Reserve currency status has already changed hands within the span of a long life: sterling held the position the dollar holds now, and lost it over the middle decades of the twentieth century.

Today, with the dollar in every reserve, US capital markets in every portfolio and Silicon Valley in every pocket, it is hard to feel that any of it could end. That difficulty is the whole point. It is exactly what certainty felt like in Rome, in London, in Moscow. I have no prediction to offer about when or how American primacy ends, and I would distrust anyone who does. The exercise is not prediction. The exercise is noticing that “I cannot imagine it” describes the limits of my imagination, not the limits of the world.

7. Whether to give your kids peanut butter

A palate cleanser, and a serious one. For years the official advice was to avoid peanuts in infancy, especially for high-risk children. The American Academy of Pediatrics recommended avoidance until age three in 2000. That guidance was withdrawn in 2008, and then the LEAP study in 2015 showed the opposite of the original advice: early exposure cut peanut allergy in high-risk infants by just over 80%, and by 2017 the formal guidance recommended deliberate early introduction.

Settled science on feeding children, followed conscientiously by a generation of parents, turned out to be not just wrong but inverted. If that can flip, the confident consensus in your own field deserves at least an occasional raised eyebrow.

The pattern

The pattern is not that things change. Everyone knows things change, and a list of changes is just trivia. The pattern is that each change was unimaginable the day before, to serious people with good information, and “unimaginable” turned out to be a statement about their imagination rather than about the world.

For those of us who build and rely on models, this has a practical edge. “Certain” in a model usually means “we chose not to model it”. Tax rules, the peg, the government curve, the supervisory interpretation: each is a variable somebody decided to hold constant. Sometimes that is a sensible choice. Modelling everything is neither possible nor useful, and a model that treats everything as uncertain tells you nothing.

But it is a choice, and it should be a conscious one. A worthwhile exercise, for an ORSA or simply for an afternoon: write down the certainties your model assumes, pick the one that feels most absurd to question, and ask what would happen if it moved. The one that feels most absurd is the interesting one. That feeling is the tell.

Even death only makes the certain list on outcome, not timing. Modelling the gap between the two is most of what actuaries do.

What belongs at number 8?

Hyperinflation, Currency Crises & Insurance Industry Collapse

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

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

How Inflation Impacts Insurance

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

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

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

My (Cautious) View on Blockchain

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

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

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

Key Arguments & Concerns

1 Inflation as an ‘Efficient’ Tax?

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

The Problem? Inflation isn’t a neutral mechanism:

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

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

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

2 Bitcoin as a Predictable Alternative to Fiat?

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

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

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

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

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

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

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

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

Key Risks:

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

Future Opportunity:

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

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

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

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

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

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

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

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

Regulatory Ambiguity: When Is a Dividend Foreseeable?

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

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

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

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

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

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

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

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

The Problem of Capital Permanence, Availability, and Loss Absorption

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

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

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

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

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

Divergent Industry Practice and Alternative Approaches

Given this uncertainty, industry practice varies widely:

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

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

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

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

Determining SCR Cover Targets: A Practical Approach

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

Rather than simply applying rigid deductions, insurers should consider:

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

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

“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

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

Why Bitcoin as a national currency will damage the economy

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

Deflation

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

But… negative interest rates?

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

Alternative view, Bitcoins are infinitely substitutable

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

What to do about lost Bitcoins?

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

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

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

There have been plenty of debate around the regulations, in part because of the dramatic financial and operational impact they will have, and partly because of how imperfectly worded they are and the scope for interpretation.

I’ll be posting about this more in the coming days.

Basing the premium on initial or outstanding balance

First, a real anomaly is the ability for insurers  to charge the capped premium rate either on initial loan balance or on the declining outstanding balance.

There are good practical reasons to want to charge a single, known amount to policyholders. It is easier to administer and policyholders have greater clarity on what they are paying.

The actual premium charged over the lifetime of a loan can be substantially higher where it is based on the initial balance rather than the declining balance, particularly for longer term loans. How a cap designed to moderate profits and improve value for money can allow such disparity is bizarre.

There is an interesting quirk here, which I hope is exploited to drive value for money and increased competition in the market. The credit life regulations require lenders to permit substitute policies where the policy meets the minimum regulatory required benefits. Where an insurer (or in practical terms, usually the lender) is charging a premium based on the initial loan balance, it becomes easier for a third party insurance company to offer a substitute policy at a cheaper rate, based on the lower actual sum assured partway through the loan or policy term.

I am not a fan of outright caps, although I recognise there are times when it might be the least bad regulatory intervention. The holy grail is a competitive market where consumers have access to information and providers compete for the business.  This will drive profit margins down to reasonable returns for the risk and capital required, and drive business into the arms of the operational cost (and distribution cost) competitive providers.

Those entities still charging on initial balance will actually help to drive this competitive market.

As much as I believe in the right of businesses to make money and make good money, value for money will be driven by competition and more of it is still needed.

The book is called BitCon: The Naked Truth About Bitcoin and it doesn’t pull punches in deriding the would-be currency. If you don’t know anything about Bitcoins, it may skip over some of the introductions necessary to hold your own in conversation. This isn’t a primer on Bitcoins or crypto-currencies, but it also doesn’t spend chapters on involved technical details so you won’t be completely lost.

I described the book as “engaging”. For me, already very sceptical of the long-term chances of success for Bitcoin and specifically  critical of its suitability as real “currency”, it had me nodding in agreement with many sections. Frankly, I don’t know how persuasive it would be to a fervent supporter (not that much anything would be).

I did enjoy the insights into some of the personalities behind Bitcoin and the histories of different supporters and how this has changed over the short time Bitcoins have been around. I learnt more about the Dark Web than I knew before, gaining a new appreciation for how dark the underbelly of the web and Bitcoins are.

Robinson ignored what I think is a key limitation on Bitcoin. Supporters claim its value derives in large part from the limited supply, but without any intrinsic value, other crypto-currencies are near-perfect substitutes. I’ve blogged about this before and was looking forward to seeing another take on it.

I enjoyed the book, reading through it fairly quickly and without wanting to switch to something else, suggesting Robinson hit the target with length and balance of information vs entertainment.

Go grab a copy from Amazon - BitCon: The Naked Truth About Bitcoin.

Norway has new banknotes or at least they will be 2017. Standard on one side and abstract, competition winning pixelated art on the other. Feels appropriately Scandinavian to me.