Canada innovates in the capital buffer space

The Canadian prudential regulator (OFSI) has made an interesting contribution to the capital buffer space via its introduction of a Domestic Stability Buffer (DSB).

Key features of the Domestic Stability Buffer:

  • Applies only to Domestic Systemically Important Banks (D-SIB) and intended to cover a range of systemic vulnerabilities not captured by the Pillar 1 requirement
  • Vulnerabilities currently included in the buffer include (i) Canadian consumer indebtedness; (ii) asset imbalances in the Canadian market and (iii) Canadian institutional indebtedness
  • Replaces a previously undisclosed Pillar 2 loading associated with this class of risks (individual banks may still be required to hold a Pillar 2 buffer for idiosyncratic risks)
  • Initially set at 1.5% of Total RWA and will be in the range of 0 to 2.5%
  • Reviewed semi annually (June and December); with the option to change more frequently in exceptional circumstances
  • Increases phased in while decreases take effect immediately

Implications for capital planning:

  • DSB supplements the Pillar 1 buffers (Capital Conservation Buffer, D-SIB surcharge and the Countercyclical Buffer)
  • Consequently, the DSB will not result in banks being subject to the automatic constraints on capital distributions that are applied by the Pillar 1 buffers
  • Banks will be required to disclose that the buffer has been breached and the OFSI will require a remediation plan to restore the buffer

What is interesting:

  • The OFSI argues that translating the existing Pillar 2 requirement into an explicit buffer offers greater transparency which in turn “… will support banks’ ability to use this capital buffer in times of stress by increasing the market’s understanding of the purpose of the buffer and how it should be used”
  • I buy the OFSI rationale for why an explicit buffer with a clear narrative is a more usable capital tool than an undisclosed Pillar 2 requirement with the same underlying rationale
  • The OFSI retains a separate Countercyclical Buffer but this Domestic Stability Buffer seems similar but not identical in its over-riding purpose (to me at least) to the approach that the Bank of England (BoE) has adopted for managing the Countercyclical Buffer.
  • A distinguishing feature of both the BoE and OFSI approaches is linking the buffer to a simple, coherent narrative that makes the buffer more usable by virtue of creating clear expectations of the conditions under which the buffer can be used.

Bottom line is that I see useful features in both the BoE and OFSI approach to dealing with the inherent cyclicality of banking.  I don’t see  either of the proposals doing much to mitigate the cyclicality of banking but I do see them offering more potential for managing the consequences of that cyclicality. Both approaches seem to me to offer material improvements over the Countercyclical Buffer as originally conceived by the BCBS.

It will be interesting to see if APRA chooses to adapt elements of this counter cyclical approach to bank capital requirements.

If I am missing something, please let me know …

From the Outside

The financial cycle and macroeconomics: What have we learnt? BIS Working Paper

Claudio Borio at the BIS wrote an interesting paper exploring the “financial cycle”. This post seeks to summarise the key points of the paper and draw out some implications for bank stress testing (the original paper can be found here).  The paper was published in December 2012, so its discussion of the implications for macroeconomic modelling may be dated but I believe it continues to have some useful insights for the challenges banks face in dealing with adverse economic conditions and the boundary between risk and uncertainty.

Key observations Borio makes regarding the Financial Cycle

The concept of a “business cycle”, in the sense of there being a regular occurrence of peaks and troughs in business activity, is widely known but the concept of a “financial cycle” is a distinct variation on this theme that is possibly less well understood. Borio states that there is no consensus definition but he uses the term to

“denote self-reinforcing interactions between perceptions of value and risk, attitudes towards risk and financing constraints, which translate into booms followed by busts. These interactions can amplify economic fluctuations and possibly lead to serious financial distress and economic disruption”.

This definition is closely related to the concept of “procyclicality” in the financial system and should not be confused with a generic description of cycles in economic activity and asset prices. Borio does not use these words but I have seen the term “balance sheet recession” employed to describe much the same phenomenon as Borio’s financial cycle.

Borio identifies five features that describe the Financial Cycle

  1. It is best captured by the joint behaviour of credit and property prices – these variables tend to closely co-vary, especially at low frequencies, reflecting the importance of credit in the financing of construction and the purchase of property.
  2. It is much longer, and has a much larger amplitude, than the traditional business cycle – the business cycle involves frequencies from 1 to 8 years whereas the average length of the financial cycle is longer; Borio cites a cycle length of 16 years in a study of seven industrialised economies and I have seen other studies indicating a longer cycle (with more severe impacts).
  3. It is closely associated with systemic banking crises which tend to occur close to its peak.
  4. It permits the identification of the risks of future financial crises in real time and with a good lead – Borio states that the most promising leading indicators of financial crises are based on simultaneous positive deviations of the ratio of private sector credit-to-GDP and asset prices, especially property prices, from historical norms.
  5. And it is highly dependent of the financial, monetary and real-economy policy regimes in place (e.g. financial liberalisation under Basel II, monetary policy focussed primarily on inflation targeting and globalisation in the real economy).

Macro economic modelling

Borio also argues that the conventional models used to analyse the economy are deficient because they do not capture the dynamics of the financial cycle. These extracts capture the main points of his critique:

“The notion… of financial booms followed by busts, actually predates the much more common and influential one of the business cycle …. But for most of the postwar period it fell out of favour. It featured, more or less prominently, only in the accounts of economists outside the mainstream (eg, Minsky (1982) and Kindleberger (2000)). Indeed, financial factors in general progressively disappeared from macroeconomists’ radar screen. Finance came to be seen effectively as a veil – a factor that, as a first approximation, could be ignored when seeking to understand business fluctuations … And when included at all, it would at most enhance the persistence of the impact of economic shocks that buffet the economy, delaying slightly its natural return to the steady state …”

“Economists are now trying hard to incorporate financial factors into standard macroeconomic models. However, the prevailing, in fact almost exclusive, strategy is a conservative one. It is to graft additional so-called financial “frictions” on otherwise fully well behaved equilibrium macroeconomic models, built on real-business-cycle foundations and augmented with nominal rigidities. The approach is firmly anchored in the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) paradigm.”

“The purpose of this essay is to summarise what we think we have learnt about the financial cycle over the last ten years or so in order to identify the most promising way forward…. The main thesis is that …it is simply not possible to understand business fluctuations and their policy challenges without understanding the financial cycle”

There is an interesting discussion of the public policy (i.e. prudential, fiscal, monetary) associated with recognising the role of the financial cycle but I will focus on what implications this may have for bank management in general and stress testing in particular.

Insights and questions we can derive from the paper

The observation that financial crises are based on simultaneous positive deviations of the ratio of private sector credit-to-GDP and asset prices, especially property prices, from historical norms covers much the same ground as the Basel Committee’s Countercyclical Capital Buffer (CCyB) and is something banks would already monitor as part of the ICAAP. The interesting question the paper poses for me is the extent to which stress testing (and ICAAP) should focus on a “financial cycle” style disruption as opposed to a business cycle event. Even more interesting is the question of whether the higher severity of the financial cycle is simply an exogenous random variable or an endogenous factor that can be attributed to excessive credit growth. 

I think this matters because it has implications for how banks calibrate their overall risk appetite. The severity of the downturns employed in stress testing has in my experience gradually increased over successive iterations. My recollection is that this has partly been a response to prudential stress tests which were more severe in some respects than might have been determined internally. In the absence of any objective absolute measure of what was severe, it probably made sense to turn up the dial on severity in places to align as far as possible the internal benchmark scenarios with prudential benchmarks such as the “Common Scenario” APRA employs.

At the risk of a gross over simplification, I think that banks started the stress testing process looking at both moderate downturns (e.g. 7-10 year frequency and relatively short duration) and severe recessions (say a 25 year cycle though still relatively short duration downturn). Bank supervisors  in contrast have tended to focus more on severe recession and financial cycle style severity scenarios with more extended durations. Banks’s have progressively shifted their attention to scenarios that are more closely aligned to the severe recession assumed by supervisors in part because moderate recessions tend to be fairly manageable from a capital management perspective.

Why does the distinction between the business cycle and the financial cycle matter?

Business cycle fluctuations (in stress testing terms a “moderate recession”) are arguably an inherent feature of the economy that occur largely independently of the business strategy and risk appetite choices that banks make. However, Borio’s analysis suggests that the decisions that banks make (in particular the rate of growth in credit relative to growth in GDP and the extent to which the extension of bank credit contributes to inflated asset values) do contribute to the risk (i.e. probability, severity and duration) of a severe financial cycle style recession. 

Borio’s analysis also offers a way of thinking about the nature of the recovery from a recession. A moderate business cycle style recession is typically assumed to be short with a relatively quick recovery whereas financial cycle style recessions typically persist for some time. The more drawn out recovery from a financial cycle style recession can be explained by the need for borrowers to deleverage and repair their balance sheets as part of the process of addressing the structural imbalances that caused the downturn.

If the observations above are true, then they suggest a few things to consider:

  • should banks explore a more dynamic approach to risk appetite limits that incorporated the metrics identified by Borio (and also used in the calibration of the CCyB) so that the level of risk they are willing to take adjusts for where they believe they are in the state of the cycle (and which kind of cycle we are in)
  • how should banks think about these more severe financial cycle losses? Their measure of Expected Loss should clearly incorporate the losses expected from business cycle style moderate recessions occurring once every 7-10 years but it is less clear that the kinds of more severe and drawn out losses expected under a Severe Recession or Financial Cycle downturn should be part of Expected Loss.

A more dynamic approach to risk appetite get us into some interesting game theory  puzzles because a decision by one bank to pull back on risk appetite potentially allows competitors to benefit by writing more business and potentially doubly benefiting to the extent that the decision to pull back makes it safer for competitors to write the business without fear of a severe recession (in technical economist speak we have a “collective action” problem). This was similar to the problem APRA faced when it decided to impose “speed limits” on certain types of lending in 2017. The Royal Commission was not especially sympathetic to the strategic bind banks face but I suspect that APRA understand the problem.

How do shareholders think about these business and financial cycle losses? Some investors will adopt a “risk on-risk off” approach in which they attempt to predict the downturn and trade in and out based on that view, other “buy and hold” investors (especially retail) may be unable or unwilling to adopt a trading approach.

The dependence of the financial cycle on the fiscal and monetary policy regimes in place and changes in the real-economy also has potential implications for how banks think about the risk of adverse scenarios playing out. Many of the factors that Borio argues have contributed to the financial cycle (i.e. financial liberalisation under Basel II, monetary policy focussed primarily on inflation targeting and globalisation in the real economy) are reversing (regulation of banks is much more restrictive, monetary policy appears to have recognised the limitations of a narrow inflation target focus and the pace of globalisation appears to be slowing in response to a growing concern that its benefits are not shared equitably). I am not sure exactly what these changes mean other than to recognise that they should in principle have some impact. At a minimum it seems that the pace of credit expansion might be slower in the coming decades than it has in the past 30 years.

All in all, I find myself regularly revisiting this paper, referring to it or employing the distinction between the business and financial cycle. I would recommend it to anyone interested in bank capital management. 

The rise of the normal distribution

“We were all Gaussians now”

This post focuses on a joint paper written in 2012 by Andrew Haldane and Benjamin Nelson titled “Tails of the unexpected”. The topic is the normal distribution which is obviously a bit technical but the paper is still readable even if you are not deeply versed in statistics and financial modelling. The condensed quote below captures the central idea I took away from the paper.

“For almost a century, the world of economics and finance has been dominated by randomness … But as Nassim Taleb reminded us, it is possible to be Fooled by Randomness (Taleb (2001)). For Taleb, the origin of this mistake was the ubiquity in economics and finance of a particular way of describing the distribution of possible real world outcomes. For non-nerds, this distribution is often called the bell-curve. For nerds, it is the normal distribution. For nerds who like to show-off, the distribution is Gaussian.”

The idea that the normal distribution should be used with care, and sometimes not at all, when seeking to analyse economic and financial systems is not news. The paper’s discussion of why this is so is useful if you have not considered the issues before but probably does not offer much new insight if you have.

What I found most interesting was the back story behind the development of the normal distribution. In particular, the factors that Haldane and Nelson believe help explain why it came to be so widely used and misused. Reading the history reminds us of what a cool idea it must have been when it was first discovered and developed.

“By simply taking repeat samplings, the workings of an uncertain and mysterious world could seemingly be uncovered”.
“To scientists seeking to explain the world, the attraction of the normal curve was obvious. It provided a statistical map of a physical world which otherwise appeared un-navigable. It suggested regularities in random real-world data. Moreover, these patterns could be fully described by two simple metrics – mean and variance. A statistical window on the world had been opened.”
Haldane and Nelson highlight a semantic shift in the 1870’s where the term “normal” began to be independently applied to this statistical distribution. They argue that adopting this label helped embed the idea that the “normal distribution” was the “usual” outcome that one should expect to observe. 
“In the 18th century, normality had been formalised. In the 19th century, it was socialised.”
“Up until the late 19th century, no statistical tests of normality had been developed.
Having become an article of faith, it was deemed inappropriate to question the faith.
As Hacking put it, “thanks to superstition, laziness, equivocation, befuddlement with tables of numbers, dreams of social control, and propaganda from utilitarians, the law of large numbers became a synthetic a priori truth. We were all Gaussians now.”

Notwithstanding its widespread use today, in Haldane and Nelson’s account, economics and finance were not early adopters of the statistical approach to analysis but eventually become enthusiastic converts. The influence of physics on the analytical approaches employed in economics is widely recognised and Haldane cites the rise of probability based quantum physics over old school deterministic Newtonian physics as one of the factors that prompted economists to embrace probability and the normal distribution as a key tool.

” … in the early part of the 20th century, physics was in the throes of its own intellectual revolution. The emergence of quantum physics suggested that even simple systems had an irreducible random element. In physical systems, Classical determinism was steadily replaced by statistical laws. The natural world was suddenly ruled by randomness.”
“Economics followed in these footsteps, shifting from models of Classical determinism to statistical laws.”
“Whether by accident or design, finance theorists and practitioners had by the end of the 20th century evolved into fully paid-up members of the Gaussian sect.”

Assessing the Evidence

Having outlined the story behind its development and increasingly widespread use, Haldane and Nelson then turn to the weight of evidence suggesting that normality is not a good statistical description of real-world behaviour. In its place, natural and social scientists have often unearthed behaviour consistent with an alternative distribution, the so-called power law distribution.
“In consequence, Laplace’s central limit theorem may not apply to power law-distributed variables. There can be no “regression to the mean” if the mean is ill-defined and the variance unbounded. Indeed, means and variances may then tell us rather little about the statistical future. As a window on the world, they are broken”
This section of the paper probably does not introduce anything new to people who have spent any time looking at financial models. It does however beg some interesting questions. For example, to what extent bank loan losses are better described by a power law and, if so, what does this mean for the measures of expected loss that are employed in banking and prudential capital requirements; i.e. how should banks and regulators respond if “…the means and variances … tell us rather little about the statistical future”? This is particularly relevant as banks transition to Expected Loss accounting for loan losses.
We can of course estimate the mean loss under the benign part of the credit cycle but it is much harder to estimate a “through the cycle” average (or “expected” loss) because the frequency, duration and severity of the cycle downturn is hard to pin down with any precision. We can use historical evidence to get a sense of the problem; we can for example talk about moderate downturns say every 7-10 years with more severe recessions every 25-30 years and a 75 year cycle for financial crises. However the data is obviously sparse so it does not allow the kind of precision that is part and parcel of normally distributed events.

Explaining Fat Tails

The paper identifies the following drivers behind non-normal outcomes:
  • Non- Linear dynamics
  • Self organised criticality
  • Preferential attachment
  • Highly optimised tolerance
The account of why systems do not conform to the normal distribution does not offer much new but I found reading it useful for reflecting on the practical implications. One of the items they called out is competition which is typically assumed by economists to be a wholly benign force. This is generally true but Haldane and Nelson note the capacity for competition to contribute to self-organised criticality.
Competition in finance and banking can of course lead to beneficial innovation and efficiency gains but it can also contribute to progressively increased risk taking (e.g. more lax lending standards, lower margins for tail risk) thereby setting the system up to be prone to a self organised critical state. Risk based capital requirements can also contribute to self organised criticality to the extent they facilitate increased leverage and create incentives to take on tail risk.

Where Next?

Haldane and Nelson add their voice to the idea that Knight’s distinction between risk and uncertainty is a good foundation for developing better ways of dealing with a world that does not conform to the normal distribution and note the distinguishied company that have also chosen to emphasise the importance of uncertainty and the limitations of risk.
“Many of the biggest intellectual figures in 20th century economics took this distinction seriously. Indeed, they placed uncertainty centre-stage in their policy prescriptions. Keynes in the 1930s, Hayek in the 1950s and Friedman in the 1960s all emphasised the role of uncertainty, as distinct from risk, when it came to understanding economic systems. Hayek criticised economics in general, and economic policymakers in particular, for labouring under a “pretence of knowledge.”
Assuming that the uncertainty paradigm was embraced, Haldane and Nelson consider what the practical implications would be. They have a number of proposals but I will focus on these
  • agent based modelling
  • simple rather than complex
  • don’t aim to smooth out all volatility

Agent based modelling

Haldane and Nelson note that …

In response to the crisis, there has been a groundswell of recent interest in modelling economic and financial systems as complex, adaptive networks. For many years, work on agent-based modelling and complex systems has been a niche part of the economics and finance profession. The crisis has given these models a new lease of life in helping explain the discontinuities evident over recent years (for example, Kirman (2011), Haldane and May (2011))
In these frameworks, many of the core features of existing models need to be abandoned.
  • The “representative agents” conforming to simple economic laws are replaced by more complex interactions among a larger range of agents
  • The single, stationary equilibrium gives way to Lorenz-like multiple, non-stationary equilibria.
  • Linear deterministic models are usurped by non linear tipping points and phase shifts
Haldane and Nelson note that these types of systems are already being employed by physicists, sociologists, ecologists and the like. Since the paper was written (2012) we have seen some evidence that economists are experimenting with “agent based modelling”. A paper by Richard Bookstabber offers a useful outline of his efforts to apply these models and he has also written a book (“The End of Theory”) promoting this path. There is also a Bank of England paper on ABM worth looking at.
I think there is a lot of value in agent based modelling but a few things impede their wider use. One is that the models don’t offer the kinds of precision that make the DSGE and VaR models so attractive. The other is that they require a large investment of time to build and most practitioners are fully committed just keeping the existing models going. Finding the budget to pioneer an alternative path is not easy. These are not great arguments in defence of the status quo but they do reflect certain realities of the world in which people work.

Simple can be more robust than complex

Haldane and Nelson also advocate simplicity in lieu of complexity as a general rule of thumb for dealing with an uncertain world.
The reason less can be more is that complex rules are less robust to mistakes in specification. They are inherently fragile. Harry Markowitz’s mean-variance optimal portfolio model has informed millions of investment decisions over the past 50 years – but not, interestingly, his own. In retirement, Markowitz instead used a much simpler equally-weighted asset approach. This, Markowitz believed, was a more robust way of navigating the fat-tailed uncertainties of investment returns (Benartzi and Thaler (2001)).
I am not a big fan of the Leverage Ratio they cite it as one example of regulators beginning to adopt simpler approaches but the broader principle that simple is more robust than complex does ring true.
The mainstay of regulation for the past 30 years has been more complex estimates of banks’ capital ratios. These are prone to problems of highly-optimised tolerance. In part reflecting that, regulators will in future require banks to abide by a far simpler backstop measure of the leverage ratio. Like Markowitz’s retirement portfolio, this equally-weights the assets in a bank’s portfolio. Like that portfolio, it too will hopefully be more robust to fat-tailed uncertainties.
Structural separation is another simple approach to the problem of making the system more resilient
A second type of simple, yet robust, regulatory rule is to impose structural safeguards on worst-case outcomes. Technically, this goes by the name of a “minimax” strategy (Hansen and Sargent (2011)). The firebreaks introduced into some physical systems can be thought to be playing just this role. They provide a fail-safe against the risk of critical states emerging in complex systems, either in a self-organised manner or because of man-made intervention. These firebreak-type approaches are beginning to find their way into the language and practice of regulation.
And a reminder about the dangers of over engineering
Finally, in an uncertain world, fine-tuned policy responses can sometimes come at a potentially considerable cost. Complex intervention rules may simply add to existing uncertainties in the system. This is in many ways an old Hayekian lesson about the pretence of knowledge, combined with an old Friedman lesson about the avoidance of policy harm. It has relevance to the (complex, fine-tuned) regulatory environment which has emerged over the past few years.
While we can debate the precise way to achieve simplicity, the basic idea does in my view have a lot of potential to improve the management of risk in general and bank capital in particular. Complex intervention rules may simply add to existing uncertainties in the system and the current formulation of how the Capital Conservation Ratio interacts with the Capital Conservation Buffer is a case in point. These two elements of the capital adequacy framework define what percentage of a bank’s earnings must be retained if the capital adequacy ratio is under stress.
In theory the calculation should be simple and intuitive but anyone who has had to model how these rules work under a stress scenario will know how complex and unintuitive the calculation actually is. The reasons why this is so are probably a bit too much detail for today but I will try to pick this topic up in a future post.

Don’t aim to eliminate volatility

Systems which are adapted to volatility will tend to be stronger than systems that are sheltered from it, or in the words of Haldane and Nelson …

“And the argument can be taken one step further. Attempts to fine-tune risk control may add to the probability of fat-tailed catastrophes. Constraining small bumps in the road may make a system, in particular a social system, more prone to systemic collapse. Why? Because if instead of being released in small bursts pressures are constrained and accumulate beneath the surface, they risk an eventual volcanic eruption.”

I am a big fan of this idea. Nassim Taleb makes a similar argument in his book “Antifragile” as does Greg Ip in “Foolproof”. It also reflects Nietzsche’s somewhat more poetic dictum “that which does not kills us makes us stronger”.

In conclusion

If you have read this far then thank you. I hope you found it useful and interesting. If you want to delve deeper then you can find my more detailed summary and comments on the paper here. If you think I have any of the above wrong then please let me know.

Looking under the hood – The IRB formula

This post is irredeemably technical so stop here if that is not your interest. If you need to understand some of the mechanics of the formula used to calculate credit risk weighted assets under the advanced Internal Ratings Based (IRB) approach then the BCBS published a paper in 2005 which offers an explanation:

  • describing the economic foundations
  • as well as the underlying mathematical model and its input parameters.

While a lot has changed as a result of Basel III, the models underlying the calculation of Internal Rating Based Capital (IRB) requirements are still based on the core principles agreed under Basel II that are explained in this BCBS paper.

The notes in the linked page below mostly summarise the July 2005 paper with some emphasis (bolded text) and comments (in italics) that I have added. The paper is a bit technical but worth reading if you want to understand the original thinking behind the Basel II risk weights for credit risk.

I initially found the paper useful for revisiting the foundation assumptions of the IRB framework as background to considering the regulatory treatment of Expected Loss as banks transition to IFRS9. The background on how the RW was initially intended to cover both Expected and Unexpected Loss, but was revised such that capital was only required to cover Unexpected Loss, is especially useful when considering the interaction of loan loss provisioning with capital requirements.

Reading the BCBS paper has also been useful for thinking through a range of related issues including:

  • The rationale for, and impact of, prudential conservatism in setting the risk parameters used in the IRB formula
  • The cyclicality of a risk sensitive capital requirement (and potential for pro cyclicality) and what might be done to mitigate the risk of pro-cyclical impacts on the economy

If you have read this far then my summary of the BCBS paper and my comments /observations can be found here (and thank you).

I am not a credit risk model expert, so the summary of the paper and my comments must be read with that in mind. I did this to help me think through some of the issues with bank capital adequacy. Hopefully others will find the notes useful. If you see something wrong or something you disagree with then let me know.