Category: Risk and uncertainty

Navigating a radically uncertain world

The distinction between risk and uncertainty is a long running area of interest for me so I have enjoyed reading John Kay and Mervyn King’s book “Radical Uncertainty: Decision-Making for an Unknowable Future”. My initial post on the book offered an overview of the content and a subsequent post explored Kay and King’s analysis of why the world is prone to radical uncertainty.

This post looks at how Kay and King propose that we navigate a world that is prone to radical uncertainty. Kay and King start (Ch 8) with the question of what it means to make rational choices.

No surprises that the answer from their perspective is not the pursuit of maximum expected value based on a priori assumptions of what is rational in a world ruled by probability (“axiomatic reasoning”). They concede that there are some problems that can be solved this way. Games of chance where you get repeated opportunities to play the odds is one, but Kay and King are firmly in the camp that the real world is, for the most part, too complex and unknowable to rely on this approach for the big issues.

It is not just that these models do not offer any useful insight into these bigger world choices. They argue, convincingly I think, that these types of precise quantitative models can also tend to create an illusion of knowledge and control that can render the systems we are seeking to understand and manage even more fragile and more prone to uncertainty. An obvious example of this risk is the way in which the advanced measures of bank capital requirements introduced under Basel II tended to encourage banks to take (and bank supervisors to approve) more leverage.

Their argument broadly makes sense to me but there was nothing particularly new or noteworthy in this part of the book. It goes over familiar ground covered equally well by other writers – see for example these posts Epsilon Theory, Bank Underground, Paul Wilmott and David Orrell, Andrew Haldane which discuss contributions these authors have made to the debate.

However, there were two things I found especially interesting in their analysis.

  • One was the argument that the “biases” catalogued by behavioural finance were not necessarily irrational when applied to a radically uncertain world.
  • The other was the emphasis they place on the idea of employing abductive reasoning and reference narratives to help navigate this radically uncertain future.

Behavioural Finance

Kay and King argue that some of the behaviours that behavioural finance deems to be irrational or biased might be better interpreted as sensible rules of thumbs that people have developed to deal with an uncertain world. They are particularly critical of the way behavioural finance is used to justify “nudging” people to what behavioural finance deems to be rational.

Behavioural economics has contributed to our understanding of decision-making in business, finance and government by introducing observation of how people actually behave. But, like the proselytisers for the universal application of probabilistic reasoning, practitioners and admirers of behavioural economics have made claims far more extensive than could be justified by their findings…

…. a philosophy of nudging carries the risk that nudgers claim to know more about an uncertain world than they and their nudgees do or could know.

I struggled with this part of the book because I have generally found behavioural finance insights quite useful for understanding what is going on. The book reads at times like behavioural finance as a whole was a wrong turn but I think the quote above clarifies that they do see value in it provided the proponents don’t push the arguments too far. In particular they are arguing that rules of thumb that have been tested and developed over time deserve greater respect.

Abductive Reasoning and Reference Narratives

The part of Kay and King’s book I found most interesting was their argument that “abductive reasoning” and “reference narratives” are a useful way of mapping our understanding of what is going on and helping us make the right choices to navigate a world prone to enter the domain of radical uncertainty.

If we go back to first principles it could be argued that the test of rationality is that the decisions we make are based on reasonable beliefs about the world and internal consistency. The problem, Kay and King argue, is that this approach still does not address the fundamental question of whether we can ever really understand a radically uncertain world. The truely rational approach to decision making has to be resilient to the fact that our future is shaped by external events taking paths that we have no way of predicting.

The rational answer for Kay and King lies in an “abductive” approach to reasoning. I must confess that I had to look this up (and my spell checker still struggles with it) but it turns out that this is a style of reasoning that works with the available (not to mention often incomplete and ambiguous) information to form educated guesses that seek to explain what we are seeing.

Abduction is similar to induction in that it starts with observations. Where it differs is what the abductive process does with the evidence. Induction seeks to derive general or universal principles from the evidence. Abduction in contrast is context specific. It looks at the evidence and tries to fit “an explanation” of what is going on while being careful to avoid treating it as “the explanation” of what is going on.

Deductive, inductive and abductive reasoning each have a role to play in understanding the world, and as we move to larger worlds the role of the inductive and abductive increases relative to the deductive. And when events are essentially one-of-a-kind, which is often the case in the world of radical uncertainty, abductive reasoning is indispensable.

Reference Narratives

If I have understood their argument correctly, the explanations or hypotheses generated by this abductive style of reasoning are expressed in “reference narratives” which we use to explain to ourselves and others what we are observing. These high level reference narratives can then provide a basis for longer term planning and a framework for day-to-day choices.

Deductive, inductive and abductive reasoning each have a role to play in understanding the world, and as we move to larger worlds the role of the inductive and abductive increases relative to the deductive. And when events are essentially one-of-a-kind, which is often the case in the world of radical uncertainty, abductive reasoning is indispensable.

Kay and King acknowledge that this approach is far from foolproof and devote a considerable part of their book to what distinguishes good narratives from bad and how to avoid the narrative being corrupted by groupthink.

Good and Bad Reference Narratives

Kay and King argue that credibility is a core feature distinguishing good and bad narratives. A good narrative offers a coherent and internally consistent explanation but it also needs to avoid over-reach. A warning sign for a bad narrative is one that seeks to explain everything. This is especially important given that our species seems to be irresistibly drawn to grand narratives – the simpler the better.

Our need for narratives is so strong that many people experience a need for an overarching narrative–some unifying explanatory theme or group of related themes with very general applicability. These grand narratives may help them believe that complexity can be managed, that there exists some story which describes ‘the world as it really is’. Every new experience or piece of information can be interpreted in the light of that overarching narrative.

Kay and King use the fox and the hedgehog analogy to illustrate their arguement that we should always be sceptical of the capacity of any one narrative to explain everything,

…. The hedgehog knows one big thing, the fox many little things. The hedgehog subscribes to some overarching narrative; the fox is sceptical about the power of any overarching narrative. The hedgehog approaches most uncertainties with strong priors; the fox attempts to assemble evidence before forming a view of ‘what is going on here’.

Using Reference Narratives

Kay and King cite the use of scenario based planing as an example of using a reference narrative to explore exposure to radical uncertainty and build resilience but they caution against trying too hard to assign probabilities to scenarios. This I think is a point well made and something that I have covered in other posts (see here and here).

Scenarios are useful ways of beginning to come to terms with an uncertain future. But to ascribe a probability to any particular scenario is misconceived…..

Scenario planning is a way of ordering thoughts about the future, not of predicting it.

The purpose is … to provide a comprehensive framework for setting out the issues with which any business must deal: identifying markets, meeting competition, hiring people, premises and equipment. Even though the business plan is mostly numbers–many people will describe the spreadsheet as a model–it is best thought of as a narrative. The exercise of preparing the plan forces the author to translate a vision into words and numbers in order to tell a coherent and credible story.

Kay and King argue that reference narratives are a way of bringing structure and conviction to the judgment, instinct and emotion that people bring to making decisions about an uncertain future

We make decisions using judgement, instinct and emotions. And when we explain the decisions we have made, either to ourselves or to others, our explanation usually takes narrative form. As David Tuckett, a social scientist and psychoanalyst, has argued, decisions require us ‘to feel sufficiently convinced about the anticipated outcomes to act’. Narratives are the mechanism by which conviction is developed. Narratives underpin our sense of identity, and enable us to recreate decisions of the past and imagine decisions we will face in the future.

Given the importance they assign to narratives, Kay and King similarly emphasise the importance of having a good process for challenging the narrative and avoiding groupthink.

‘Gentlemen, I take it we are all in complete agreement on the decision here. Then, I propose we postpone further discussion of this matter until the next meeting to give ourselves time to develop disagreement, and perhaps gain some understanding of what the decision is all about.’

Alfred P. Sloan (Long time president chairman and CEO of General Motors Corporation) quoted in the introduction to Ch 16: Challenging Narratives

These extracts from their book nicely captures the essence of their argument

Knowledge does not advance through a mechanical process of revising the probabilities people attach to a known list of possible future outcomes as they watch for the twitches on the Bayesian dial. Instead, current conventional wisdom is embodied in a collective narrative which changes in response to debate and challenge. Mostly, the narrative changes incrementally, as the prevalent account of ‘what is going on here’ becomes more complete. Sometimes, the narrative changes discontinuously – the process of paradigm shift described by the American philosopher of science Thomas Kuhn.

the mark of the first-rate decision-maker confronted by radical uncertainty is to organise action around a reference narrative while still being open to both the possibility that this narrative is false and that alternative narratives might be relevant. This is a very different style of reasoning from Bayesian updating.

Kay and King argue that the aim in challenging the reference narrative is not simply to find the best possible explanation of what is going on. That in a sense is an almost impossible task given the premise that the world is inherently unpredictable. The objective is to find a narrative that seems to offer a useful guide to what is going on but not hold too tightly to it. The challenge process also tests the weaknesses of plans of action based on the reference narrative and, in doing so, progressively secures greater robustness and resilience.


The quote below repeats a point covered above but it does nicely capture their argument that the pursuit of quantitative precision can be a distraction from the broader objective of having a robust and resilient process. By all means be as rigorous and precise as possible but recognise the risk that the probabilities you assign to scenarios and “risks” may end up simply serving to disguise inherent uncertainties that cannot be managed by measurement.

The attempt to construct probabilities is a distraction from the more useful task of trying to produce a robust and resilient defence capability to deal with many contingencies, few of which can be described in any but the sketchiest of detail.

robustness and resilience, not the assignment of arbitrary probabilities to a more or less infinite list of possible contingencies, are the key characteristics of a considered military response to radical uncertainty. And we believe the same is true of strategy formulation in business and finance, for companies and households.

Summing Up

Overall a thought provoking book. I am not yet sure that I am ready to embrace all of their proposed solutions. In particular, I am not entirely comfortable with the criticisms they make of risk maps, bayesian decision models and behavioural finance. That said, I do think they are starting with the right questions and the reference narrative approach is something that I plan to explore in more depth.

I had not thought of it this way previously but the objective of being “Unquestionably Strong” that was recommended by the 2014 Australian Financial System Inquiry and subsequently fleshed out by APRA can be interpreted as an example of a reference narrative that has guided the capital management strategies of the Australian banks.

Tony – From The Outside

Why we fail to prepare for disasters

Tim Harford (The Undercover Economist) offers a short and readable account here of some of the reasons why, faced with clear risks, we still fail to act. We can see the problem, typically one of many, but don’t do enough to manage or mitigate the risk. New Orleans’ experiences with severe weather events features prominently as does (not surprisingly) COVID 19.

This, then, is why you and I did not see this coming: we couldn’t grasp the scale of the threat; we took complacent cues from each other, rather than digesting the logic of the reports from China and Italy; we retained a sunny optimism that no matter how bad things got, we personally would escape harm; we could not grasp what an exponentially growing epidemic really means; and our wishful thinking pushed us to look for reasons to ignore the danger.

Why we fail to prepare for disasters; Tim Harford (The Undercover Economist)

Another big part of the problem is that the cost of being fully prepared can be more than we are willing to pay. Especially when there is continuous pressure to find cost economies in the here and now

Serious scenarios are useful, but … no use if they are not taken seriously. That means spending money on research that may never pay off, or on emergency capacity that may never be used. It is not easy to justify such investments with the day-to-day logic of efficiency.

So the key points I took from his post:

  • Sometimes it can be something genuinely new and unexpected (i.e. Black Swan events) but risks we are well aware of can be equally damaging
  • Part of the problem is that we are social animals and take our cues from what the rest of the herd is doing (“normalcy bias” or “negative panic”)
  • Even where we understand the statistics and know that someone will be impacted, we tend to assume it will be someone else or someone else’s family (“optimism bias”)
  • We are especially bad at understanding risks that have an exponential driver (“exponential myopia”)
  • We are also quite good at finding reasons to justify ignoring risks we want to ignore or otherwise find inconvenient (“wishful thinking”)
  • Last, but far from least, efficiency is the enemy of resilience.

We need to remember that most of the factors listed above can also be useful in many other contexts (arguably most of the time). A tendency not to panic can be pretty useful and optimism has helped dreamers and ordinary people achieve many great things that have benefited the herd. Efficiency as a rule seems like a good thing to strive for.

Harford does not offer any easy answers but his post touches on issues that I have also been considering in Kay and King’s book titled “Radical Uncertainty: Decision-Making for an Unknowable Future”. I have done a couple of posts on that book already (here and here) and am working on a final one that focuses on Chapters 8-16 which set out their ideas for how we navigate a world prone to radical uncertainty.

Tony – From the Outside

The why of Radical Uncertainty

A recent post offered an overview of a book by John Kay and Mervyn King titled “Radical Uncertainty: Decision-Making for an Unknowable Future”. It is a rich topic and this post covers the underlying drivers that tend to result in radically uncertain outcomes.

Kay and King nominate “reflexivity” as a key driver of radical uncertainty

The sociologist Robert K. Merton identified reflexivity as a distinctive property of social systems–the system itself is influenced by our beliefs about it. The idea of reflexivity was developed by the Austrian émigré philosopher Karl Popper and became central to the thinking of Popper’s student, the highly successful hedge fund manager George Soros. And it would form part of the approach to macroeconomics of the Chicago economist Robert Lucas and his followers … although their perspective on the problem and its solution would be very different.

Reflexivity undermines stationarity. This was the essence of ‘Goodhart’s Law’–any business or government policy which assumed stationarity of social and economic relationships was likely to fail because its implementation would alter the behaviour of those affected and therefore destroy that stationarity.

Kay and King, Chapter 3: Radical Uncertainty is Everywhere”

Radical uncertainty also features in Richard Bookstaber’s book “The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction”. Bookstaber identifies four broad phenomena he argues are endemic to financial crises

Emergent phenomena.
“When systemwide dynamics arise unexpectedly out of the activities of individuals in a way that is not simply an aggregation of that behavior, the result is known as emergence”.

Non-ergodicity.
“An ergodic process … is one that does not vary with time or experience.
Our world is not ergodic—yet economists treat it as though it is.”

Radical uncertainty.
“Emergent phenomena and non-ergodic processes combine to create outcomes that do not fit inside defined probability distributions.”

Computational irreducibility.
“There is no formula that allows us to fast-forward to find out what the result will be. The world cannot be solved; it has to be lived.“

Bookstaber, Chapter 2: Being Human

If you want to delve into the detail of why the world can be radically uncertain then Bookstaber arguably offers the more detailed account; albeit one couched in technical language like emergent phenomena, ergodicity and computational irreducibility. In Chapter 10 he lays out the ways in which an agent based modelling approach to the problem of radical uncertainty would need to specify the complexity of the system in a structured way that takes account of the amount of information required to describe the system and the connectedness of its components. Bookstaber also offers examples of emergent phenomena in seemingly simple systems (e.g. Gary Conways’s “Game of Life”) which give rise to surprisingly complex outcomes.

I am not sure if either book makes this point explicitly but I think there is also an underlying theme in which the models that provide the illusion of control over an uncertain future create an incentive to “manage” risk in ways that increases the odds of bad outcomes based on insufficient resilience. That seems to be the clear implication of Kay and King’s discussion of the limits of finance theory (Chapter 17: The World of Finance). They acknowledge the value of the intellectual rigour built on the contributions of Harry Markowitz, William Sharpe and Eugene Fama but highlight the ways in which it has failed to live up to its promiseI .

We note two very different demonstrations of that failure. One is that the models used by regulators and financial institutions, directly derived from academic research in finance, not only failed to prevent the 2007–08 crisis but actively contributed to it. Another is to look at the achievements of the most successful investors of the era – Warren Buffett, George Soros and Jim Simons. Each has built fortunes of tens of billions of dollars. They are representative of three very different styles of investing.

Kay and King, Chapter 17 The World of Finance

I plan to do one more post exploring the ways in which we navigate a world of radical uncertainty.

Tony (From the Outside)

Worth reading – “Radical Uncertainty: Decision-Making for an Unknowable Future” by John Kay and Mervyn King

I have covered some of the ideas in the book in previous posts (here and here) but have now had the chance the read the book in full and can recommend it. I have included more detailed notes on the book here but this post offers a short introduction to some of the key ideas.

Kay and King cover a lot of ground but, simply put, their book is about

“… how real people make choices in a radically uncertain world, in which probabilities cannot meaningfully be attached to alternative futures.” 

One of the things that makes the book interesting is that they were once true believers in decision making models based on rational economic agents seeking to maximise or optimise expected value.

As students and academics we pursued the traditional approach of trying to understand economic behaviour through the assumption that households, businesses, and indeed governments take actions in order to optimise outcomes. We learnt to approach economic problems by asking what rational individuals were maximising. Businesses were maximising shareholder value, policy-makers were trying to maximise social welfare, and households were maximising their happiness or ‘utility’. And if businesses were not maximising shareholder value, we inferred that they must be maximising something else – their growth, or the remuneration of their senior executives.

The limits on their ability to optimise were represented by constraints: the relationship between inputs and outputs in the case of businesses, the feasibility of different policies in the case of governments, and budget constraints in the case of households. This ‘optimising’ description of behaviour was well suited to the growing use of mathematical techniques in the social sciences. If the problems facing businesses, governments and families could be expressed in terms of well-defined models, then behaviour could be predicted by evaluating the ‘optimal’ solution to those problems.

Kay and King are not saying that these models are useless. They continue to see some value in the utility maximisation model but have come to believe that it is not the complete answer that many economists, finance academics and politicians came to believe.

Although much can be learnt by thinking in this way, our own practical experience was that none of these economic actors were trying to maximise anything at all. This was not because they were stupid, although sometimes they were, nor because they were irrational, although sometimes they were. It was because an injunction to maximise shareholder value, or social welfare, or household utility, is not a coherent guide to action.

They argue that the approach works up to a point but fails to deal with decisions that are in the domain of radical uncertainty

But we show in this book that the axiomatic approach to the definition of rationality comprehensively fails when applied to decisions made by businesses, governments or households about an uncertain future. And this failure is not because these economic actors are irrational, but because they are rational, and – mostly – do not pretend to knowledge they do not and could not have. Frequently they do not know what is going to happen and cannot successfully describe the range of things that might happen, far less know the relative likelihood of a variety of different possible events.

There are many factors that explain the current state of affairs but a key inflexion point in Kay and King’s account can be found in what they label “A Forgotten Dispute” (Chapter 5) between Frank Knight and John Maynard Keynes on one side and Frank Ramsey and Bruno de Frinetti on the other, regarding the distinction between risk and uncertainty. Knight and Keynes argued that probability is an objective concept confined to problems with a defined and knowable frequency distribution. Ramsey argued that “subjective probability” is equally valid and used the mathematics developed for the analysis of frequency based probabilities to apply these subjective probabilities.

“Economists (used to) distinguish risk, by which they meant unknowns which could be described with probabilities, from uncertainty, which could not….. over the last century economists have attempted to elide that historic distinction between risk and uncertainty, and to apply probabilities to every instance of our imperfect knowledge of the future.”

Keynes and Knight lost the debate

Ramsey and de Finetti won, and Keynes and Knight lost, that historic battle of ideas over the nature of uncertainty. The result was that the concept of radical uncertainty virtually disappeared from the mainstream of economics for more than half a century. The use of subjective probabilities, and the associated mathematics, seemed to turn the mysteries of radical uncertainty into puzzles with calculable solutions. 

Ramsey and de Finetti laid the foundations for economists to expand the application of probability based thinking and decision making. Milton Friedman picked up the baton and ran with it.

There is a lot more to the book than interesting historical anecdotes on the history of economic ideas. The subject matter is rich and it crosses over topics covered previously in this blog including:

There are also overlaps with a book by Richard Bookstaber titled “The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction”. I am yet to review this book but have some detailed notes here.

One quibble with the book is that I think their critique of the Bayesian method is a bit harsh. I understand their concern to push back on the idea that Bayes solves the problem of using probability to understand uncertainty. At times however it reads like Bayes has no value at all. Read “The Theory that Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy” by Sharon Bertsch McGrayne for an alternative perspective.

Bayes may not help with mysteries but its application in puzzles should not be undervalued. I don’t entirely agree with their perspective on behavioural finance either.

I want to come back to the topics of risk and uncertainty in a future post but it will take time to process all of the overlapping pieces. In the interim, I hope you found the overview above useful.

Tony (From the Outside)

Why the real economy needs a prudential authority too

Isabella Kaminska (FT Alphaville) offers an interesting perspective on ways in which prudential initiatives in the areas of capital, liquidity and bail-in that have strengthened the banking sector post GFC might be applied to the “real economy”.

The global financial crisis taught us that laissez-faire finance, when left to its own devices, tends to encourage extreme fragility by under capitalising the system for efficiency’s sake and making it far more systemically interdependent.

Pre-2008, banks operated on the thinnest of capital layers while taking extreme liquidity risk due to the presumption that wholesale liquidity markets would always be open and available to them. It was in this way that they saved on capital and liquidity costs and increased their return on equity.  

Regulatory responses to the crisis understandably focused on boosting resilience by hiking capital buffers, liquidity ratios and also by introducing new types of loss absorbing structures. While it’s still too early to claim regulatory efforts were a definitive success, it does seem by and large the measures have worked to stymie a greater financial crisis this time around.

But what the 2008 crisis response may have overlooked is that bolstering banks to protect the economy means very little if the underlying real economy remains as thinly spread and interconnected as the financial sector always used to be.

The assessment that these banking initiatives “means very little” is possibly overstating the case.  The problems we are facing today would be an order of magnitude greater if the banking system was not able to plays its part in the solution.

The core point, however, I think is absolutely on the money, the focus on efficiency comes at the expense of resilience. More importantly, a free market system, populated by economic agents pursuing their own interests shaped by a focus on relatively short term time horizons, does not seem to be well adapted for dealing with this problem on its own. The lessons prudential regulators learned about the limits of efficient markets and market discipline also apply in the real world.

Isabella looks at the way prudential capital and liquidity requirements operate in banking and draws analogies in the real economy. With respect to liquidity, she notes for example,

“… the just-in-time supply chain system can be viewed as the real economy’s version of a fractional reserve system, with reserves substitutable for inventories.  

Meanwhile, the real economy’s presumption that additional inventories can be sourced from third party wholesale suppliers at a price as and when demand dictates, is equivalent to the banking sector’s presumption that liquidity can always be sourced from wholesale markets.

Though there is obviously one important difference.

Unlike the banking sector, the real economy has no lender of last resort that can magically conjure up more intensive care beds or toilet paper at the stroke of a keyboard when runs on such resources manifest unexpectedly.  

So what are our options? Companies could increase their inventories (analogous to holding more liquid assets) or build excess capacity (analogous to building up a capital buffer) but it is very difficult for companies acting independently to do this if their competitors choose the short term cost efficient play and undercut them on price. The Prisoner’s Dilemma trumps market discipline and playing the long game.

Isabella frames the problem as follows:

short-term supply shortages can only be responded to with real world manufacturing capability, which itself is constrained by physical availability To that extent crisis responses can only really take two forms: 1) immediate investment in the build-up of new manufacturing capacity that can address the specific system shortages or, 2) the temporary reallocation of existing resources (with some adaptation cost) to new production purposes.

The problem with the first option is that it is not necessarily time efficient. Not every country has the capability to build two new hospitals from scratch in just 10 days. Nor the capacity to create unexpected supply just-in-time to deal with the problem.

New investment may not be economically optimal either. What happens to those hospitals when the crisis abates? Do they stand empty and idle? Do they get repurposed? Who will fund their maintenance and upkeep if they go unused? And at what cost to other vital services and goods?

Isabella’s proposal …

That leaves the reallocation of existing assets as the only sensible and economically efficient mitigatory response to surge-demand related crises like pandemic flu. But it’s clear that on that front we can be smarter about how we anticipate and prepare for such reallocation shocks. An obvious thing to do is to take a leaf out of banking regulators’ books, especially with regards to bail-inable capital, capital ratios and liquidity profiles.

Isabella offers two examples to illustrate her argument; one is power companies and the other is the health system.

She notes that power utilities manage demand-surge or supply-shock risk with interruptible contracts to industrial clients. She argues that these contracts equate to a type of bail-inable capital buffer, since the contracts allow utilities to temporarily suspend services to clients (at their cost) if and when critical needs are triggered elsewhere and supplies must be diverted.

I think she has a good point about the value of real options but I am less sure that bail-in is the right analogy. Bail-in is a permanent adjustment to the capital structure in which debt is converted to equity or written off. Preferably the former in order to maintain the loss hierarchy that would otherwise apply in liquidation. A contract that enables a temporary adjustment to expenses is a valuable option but not really a bail-in style option.

What she is identifying in this power utility example is more a company buying real options from its customers that reduces operating leverage by enabling the company to reduce the supply of service when it becomes expensive to supply. Companies that have high operating leverage have high fixed costs versus revenue and will, all other things being equal, tend to need to run more conservative financial leverage than companies with low operating leverage. So reduced operating leverage is a substitute for needing to hold more capital.

Isabella then explores the ways in which the liquidity, capital and bail-in analogies might be applied in healthcare. I can quibble with some of the analogies she draws to prudential capital and liquidity requirements. As an example of a capital requirement being applied to health care she proposes that …

“… governments could mandate makers of non-perishable emergency goods (such as medicines, toilet paper, face masks, hand sanitiser) to always keep two-weeks’ worth of additional supply on hand. And companies could also be mandated to maintain some share of total supply chain production capability entirely domestically, making them more resilient to globalised shocks”

 Two weeks supply looks more like a liquidity buffer than a capital buffer but that does not make the ideas any the less worth considering as a way of making the real economy more resilient. The banking system had its crisis during the GFC and the real economy is being tested this time around. There are arguments about whether the changes to banking went far enough but it is clearly a lot better placed to play its part in this crisis than it was in the last. The question Isabella poses is what kinds of structural change will be required to make the real economy more resilient in the face of the next crisis.

Another example of FT Alphaville being a reliable source of ideas and information to help you think more deeply about the world.

Tony (From the Outside)

IFRS 9 loan loss provisioning faces its first real test

My long held view has been that IFSR 9 adds to the procyclicality of the banking system (see here, here, and here) and that the answer to this aspect of procyclicality lies in the way that capital buffers interact with loan loss provisioning (here, here, and here).

So it was interesting to see an article in the Financial Times overnight headlined “New accounting rules pose threat to banks amid virus outbreak”. The headline may be a bit dramatic but it does draw attention to the IFRS 9 problem I have been concerned with for some time.

The article notes signs of a backlash against the accounting rules with the Association of German Banks lobbying for a “more flexible handling” of risk provisions under IFRS 9 and warning that the accounting requirements could “massively amplify” the impact of the crisis. I agree that the potential exists to amplify the crisis but also side with an unnamed “European banking executive” quoted in the article saying “IFRS 9, I hate it as a rule, but relaxing accounting standards in a crisis just doesn’t look right”.

There may be some scope for flexibility in the application of the accounting standards (not my area of expertise) but that looks to me like a dangerous and slippery path to tread. The better option is for flexibility in the capital requirements, capital buffers in particular. What we are experiencing is exactly the kind of adverse scenario that capital buffers are intended to absorb and so we should expect them to decline as loan loss provisions increase and revenue declines. More importantly we should be seeing this as a sign that the extra capital put in place post the GFC is performing its assigned task and not a sign, in and of itself, indicating distress.

This experience will also hopefully reinforce the case for ensuring that the default position is that the Counter Cyclical Capital Buffer be in place well before there are any signs that it might be required. APRA announced that it was looking at this policy in an announcement in December 2019 but sadly has not had the opportunity to fully explore the policy initiative and implement it.

Tony

Probabilities disguising uncertainty – Part II

This behavior makes one blind to all sorts of things. 

The first blind spot … is that it treats uncertain events – items of unknowable incidence and severity – as if they were risks that could be estimated probabilistically. 

Epsilon Theory ; “Lack of Imagination” 14 March 2020

One of my recent posts drew attention to an article by John Kay promoting a book he has co-written with Mervyn King on the topic of “radical uncertainty”. Epsilon Theory offers another useful perspective on the ways in which extending probabilistic thinking beyond its zone of validity can cause us to miss the big picture.

The Epsilon Theory post focusses on the Covid 19 fallout currently playing out but is also worth reading for the broader challenges it offers anyone trying to use models and probabilities to manage real world outcomes …

Tony

Probabilities disguising uncertainty

‘In this situation, what you started getting was probabilities that disguised uncertainty as opposed to actually providing you with more useful information.’

Barack Obama commenting on making the decision whether to attack a target which evidence suggested could be Osama Bin Laden

This quote is a drawn from an article that John Kay published on his website under the title “The point of probabilities”. The point he is making is

  • Similar to one touched on in a Bank Underground post that I discussed in a recent post on my blog.
  • Short and worth reading

Tony