Central bank digital currencies
13 January 2021
Erasmus and Turing
11 January 2021
Brexit and Marxism
3 January 2021
What to do about the Covid financial system bailouts?
22 December 2020
The crypto-technical response to the Covid-19 bailouts
13 December 2020
The libertarian response to the Covid-19 financial turmoil
9 December 2020
The socialist response to the Covid-19 financial turmoil
4 December 2020
On the response of the financial authorities to Covid-19
2 December 2020
The Covid-19 bailouts and the future of the capitalist banking system
26 November 2020
Which programming language is best for economic research: Julia, Matlab, Python or R?
20 August 2020
ARM on AWS for R
15 June 2020
Low vol strategies
8 May 2020
Of Julia and R
8 May 2020
How to manipulate risk forecasts 101
30 April 2020
The five principles of correct riskometer use
27 April 2020
The problem with Backtesting
25 April 2020
The magic of riskometers
24 April 2020
Risk and scientific socialism
23 April 2020
Financial crises and epidemics
19 April 2020
Hayek and Corona
17 April 2020
Hayek et Corona
17 April 2020
Ignoring the Corona analysis
15 April 2020
The coronavirus crisis is no 2008
26 March 2020
Artificial intelligence as a central banker
6 March 2020
Systemic consequences of outsourcing to the cloud
2 December 2019
The dissonance of the short and long term
12 August 2019
Central banks and reputation risk
6 August 2019
The Brexit culture war
5 May 2019
All about BoB — The Bank of England Bot
29 April 2019
My tiny, tiny contribution to Apple's fall in profits
6 January 2019
The 2018 market in a 250 year context
1 January 2019
Short and long-term risk
3 December 2018
Perceived and actual risk
2 December 2018
Cryptocurrencies: Financial stability and fairness
9 November 2018
The October 2018 stock market in a historical context
1 November 2018
The hierarchy of financial policies
12 September 2018
Which numerical computing language is best: Julia, MATLAB, Python or R?
9 July 2018
26 June 2018
What are risk models good for?
3 June 2018
The McNamara fallacy in financial policymaking
1 June 2018
VIX, CISS and all the political uncertainty
20 May 2018
Here be dragons
30 March 2018
Low risk as a predictor of financial crises
26 March 2018
Cryptocurrencies don't make sense
13 February 2018
Yesterday's mini crash in a historical context
6 February 2018
Artificial intelligence and the stability of markets
15 November 2017
European bank-sovereign doom loop
30 September 2017
Do the new financial regulations favour the largest banks?
27 September 2017
The ECB Systemic Risk Indicator
24 September 2017
Finance is not engineering
22 September 2017
University of Iceland seminar
14 June 2017
Brexit and systemic risk
31 May 2017
Should macroprudential policy target real estate prices?
12 May 2017
Learning from history at LQG
13 April 2017
Is Julia ready for prime time?
12 March 2017
With capital controls gone, Iceland must prioritise investing abroad
12 March 2017
Competing Brexit visions
25 February 2017
Systemic consequences of Brexit
23 February 2017
Why macropru can end up being procyclical
15 December 2016
The fatal flaw in macropru: It ignores political risk
8 December 2016
Why it doesn't make sense to hold bonds
27 June 2016
On the financial market consequences of Brexit
24 June 2016
Cyber risk as systemic risk
10 June 2016
Big Banks' Risk Does Not Compute
24 May 2016
Interview on þjóðbraut on Hringbraut
21 May 2016
Farewell CoCos
26 April 2016
Will Brexit give us the 1950s or Hong Kong?
18 April 2016
Of Brexit and regulations
16 April 2016
IMF and Iceland
12 April 2016
Stability in Iceland
7 April 2016
Everybody right, everybody wrong: Plural rationalities in macroprudential regulation
18 March 2016
Of tail risk
12 March 2016
Models and regulations and the political leadership
26 February 2016
Why do we rely so much on models when we know they can't be trusted?
25 February 2016
Does a true model exist and does it matter?
25 February 2016
The point of central banks
25 January 2016
Volatility, financial crises and Minsky's hypothesis
2 October 2015
Impact of the recent market turmoil on risk measures
28 August 2015
Iceland, Greece and political hectoring
13 August 2015
A proposed research and policy agenda for systemic risk
7 August 2015
Are asset managers systemically important?
5 August 2015
Objective function of macro-prudential regulations
24 July 2015
Risky business: Finding the balance between financial stability and risk
24 July 2015
Regulators could be responsible for next financial crash
21 July 2015
How Iceland is falling behind. On Sprengisandur
12 July 2015
Greece on Sprengisandur
12 July 2015
Why Iceland can now remove capital controls
11 June 2015
Market moves that are supposed to happen every half-decade keep happening
14 May 2015
Capital controls
12 May 2015
What do ES and VaR say about the tails
25 April 2015
Why risk is hard to measure
25 April 2015
Post-Crisis banking regulation: Evolution of economic thinking as it happened on Vox
2 March 2015
The Danish FX event
24 February 2015
On the Swiss FX shock
24 February 2015
Europe's proposed capital markets union
23 February 2015
What the Swiss FX shock says about risk models
18 January 2015
Model risk: Risk measures when models may be wrong
8 June 2014
The new market-risk regulations
28 November 2013
Solvency II: Three principles to respect
21 October 2013
Political challenges of the macroprudential agenda
6 September 2013
Iceland's post-Crisis economy: A myth or a miracle?
21 May 2013
The capital controls in Cyprus and the Icelandic experience
28 March 2013
Towards a more procyclical financial system
6 March 2013
Europe's pre-Eurozone debt crisis: Faroe Islands in the 1990s
11 September 2012
Countercyclical regulation in Solvency II: Merits and flaws
23 June 2012
The Greek crisis: When political desire triumphs economic reality
2 March 2012
Iceland and the IMF: Why the capital controls are entirely wrong
14 November 2011
Iceland: Was the IMF programme successful?
27 October 2011
How not to resolve a banking crisis: Learning from Iceland's mistakes
26 October 2011
Capital, politics and bank weaknesses
27 June 2011
The appropriate use of risk models: Part II
17 June 2011
The appropriate use of risk models: Part I
16 June 2011
Lessons from the Icesave rejection
27 April 2011
A prudential regulatory issue at the heart of Solvency II
31 March 2011
Valuing insurers' liabilities during crises: What EU policymakers should not do
18 March 2011
Risk and crises: How the models failed and are failing
18 February 2011
The saga of Icesave: A new CEPR Policy Insight
26 January 2010
Iceland applies for EU membership, the outcome is uncertain
21 July 2009
Bonus incensed
25 May 2009
Not so fast! There's no reason to regulate everything
25 March 2009
Modelling financial turmoil through endogenous risk
11 March 2009
Financial regulation built on sand: The myth of the riskometer
1 March 2009
Government failures in Iceland: Entranced by banking
9 February 2009
How bad could the crisis get? Lessons from Iceland
12 November 2008
Regulation and financial models: Complexity kills
29 September 2008
Blame the models
8 May 2008

Finance is not engineering

22 September 2017

Regulations change behaviour and outcomes. It is seductively attractive to say that someone misbehaves, therefore we need the rule to prevent the misbehaviour. However, human beings, being human, don’t just comply, their behaviour changes. That is why regulating the financial system is infinitely more complex than engineering.

Suppose an engineer designs a bridge. She takes on board the laws of nature, the likelihood of earthquakes and severe weather and figures out with reasonable accuracy the various cost-benefit scenarios presented to those commissioning the bridge. If she makes the supporting pillars one metre thick, the likelihood of the bridge collapsing is once every 400 years.

What about a bank? If the quantity of concrete in a bridge is directly related to the stability of the bridge, then surely the amount of capital a bank holds is also directly related to its stability. That gives us a simple cost-benefit analysis.

The more capital a bank holds, the safer it is, but at the expense of the amount of interest it needs to charge for loans.

So, there is a trade-off between financial stability and economic growth. All the policymakers have to do is to find right balance and all is good. There is a large number of impact studies aiming to do exactly that. The problem is that those from the industry minimise the safety angle and maximise the economic impact while those from the government agencies do the opposite. We can pick and choose the cost-benefit of bank capital simply based on our political or ideological or financial interests, with minimal reference to actual reliable facts. It is a post-truth approach to financial policy.

This is quite different from that of the cost-benefit study for our bridge. When the engineer designs a bridge, she can reliably consider the laws of nature and the environment as exogenous because nature is neutral. In finance, nature is not neutral, it is malevolent and the risk is endogenous.

Immediately after our regulator comes up with rules for determining bank capital, the bankers look for a way around the rules. The will try their best to make capital look very high as far as the outside world is concerned while actually keeping it really low.

The technical name for this is capital structure arbitrage. Many of the banks that failed in 2008 had some of the highest levels of capital going, but that capital turned out to be illusionary. Of course, the regulators now say that they learned their lesson, and today’s capital ratios are much more reliable than they were before 2007.

There is always a cat-and-mouse game going on between the authorities and the banks. While the bankers may verily read regulations in order to comply, they are much more enthusiastic about finding loopholes. Regulations are inherently backward looking and change very slowly, giving fast-moving and forward-looking bankers every incentive to look for risk-taking where the authorities are not looking. Because the financial system is almost infinitely complex, it is a technical impossibility to regulate anything but a very small part of it, leaving plenty of room for misbehaviour. The bankers know this and take advantage. They spend considerable resources trying to find loopholes because when they find them they have one or two decades to profitably exploit them. That creates at least three problems.

First, regulations may move profitable and risky activities to the shadow banking system or abroad.

The second problem is that when we regulate the financial system, we often just drive risk-taking behaviour away from the spotlight into the shadows, where it is much harder to detect.

I can think of more than one financial crisis with that as the main cause, for example the tequila crisis in Mexico in 1993. This happened because the Mexican banks were borrowing in New York in US dollars to lend to Mexican borrowers in pesos. Because the currency risk was taken by the Mexican banks, the authorities were justifiably concerned and forbade the banks from borrowing in New York to lend to Mexico. The Mexican banks found it easy to get around the rules by creating derivative transactions with the New York banks. Because the Banco de Mexico did not see these transactions, it did not realise what was happening. It is much better if risk-taking is visible rather than hidden.

The third, and even more insidious problem, is that precisely because the financial authority is trying to protect us from the financial system, banks have an extra incentive to take on more risk. And even worse, take risk in a way that maximises the chance of a bailout if things go wrong. This is an example of Minsky’s dictum “stability is destabilising”. If the central banks are successful in reducing risk, they perversely create incentives for risk-taking that eventually leads to a crisis.

This is an example of endogenous risk. The reason is because risk is hard to measure, and all the riskometers can pick up is perceived risk. If the road is smooth, and perceived risk is low, it creates incentives to take more risk. Because after all, if everything is safe, what is wrong with a little bit more risk? The problem is that such risk taking is not immediately visible, but only seen much later. It was decisions taken in the supposedly low risk environment at the start of the 2000s that created the conditions for the subsequent crisis. From a statistical point of view, it is usually impossible to detect such hidden buildup of risk.

More often than not, these latter two problems come together, amplifying each other. Because perceived risk is low, we take more risk, but because we are not supposed to, we do it away from the spotlight. We therefore don’t know how much actual risk is building up, further encouraging more risk-taking in the shadows.

© All rights reserved, Jon Danielsson, 2021