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
Cryptocurrencies
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
The macro-micro conflict
20 October 2015
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

Short and long-term risk

3 December 2018

The riskiest year in human history was 1962. The year of the Cuban missile crisis, the closest we ever came to a nuclear war. The mother of all tail events, where all prices go to zero. Volatility that year was average — 16.5%

How can market risk be average when tail risk is at its highest?

The following figure shows the annualized S&P 500 volatility from 1928 until today, along with the median, the quarter smallest and the 10% smallest volatility. 1962, is highlighted in red along with the two runners-up.

Volatility and nuclear war

Volatility in 1962 was average and slightly above the median. The following year Kennedy was assassinated, and then volatility was at its 10% low. The following year, the Vietnam proxy war between the Soviet Union, China and the United States was really heating up, while volatility was at its historically low.

Tail risk in those three years was extremely high, while market risk was very low. How can that be? Elementary. Risk is not a unified concept.

Volatility is the risk of a short-term and frequent change in prices, while a nuclear war resulting from the Cuban missile crisis would be an extreme and once off event. Volatility isn’t simply designed to capture such risk. And neither are most other market risk measures, like CDS spreads, VaR, ER, SRISK, CoVaR, and the like.

Market data, and hence market risk measurements, are most informative about the short run and innocuous. High-frequency small events.

As time passes, day-to-day risk market ceases to be relevant. Fist, institution idiosyncratic risk is what matters, and then systemic risk. As time horizons become longer, the macroeconomy is increasingly driving risk, only to be replaced by politics at the very longest horizons.

The most damaging financial crises are invariably caused by politics or the macroeconomy, not by excessive amounts of financial risk.

The relationship between the drivers of risk, time and what matters is shown in the following figure.

What we care about should determine where we look for risk. Day-to-day market risk, like volatility, is usually what matters for the trading floor and microprudential regulations. If we care about solvency of individual institutions, or financial crises, the concern of the macroprudential regulators, market risk is irrelevant.

That is not how most risk methodologies see it. While the drivers of long-term risk are predominantly political, almost all risk models only measure short-term risk.

Why is that? Because it is much easier to measure. All we need is market data and a model, and voilà, we have a risk forecast.

We, therefore, are in the perverse or amusing situation where what we spend most of our time measuring is what matters the least, and what we care most about is unmeasured.

More formally, when we measure risk, we get perceived risk, while we care most about actual risk.

It reminds me of the old joke about the policeman who encounters a drunk man crawling on four legs under a lamp post. The policeman asks what are you doing. The drunk responds by saying he’s looking for his keys. The policeman asks why are you looking there. The drunk responds “because that’s where the light is.”

I was in a risk conference recently where a number of participants maintained that systemic risk was increasing, pointing to a large number of reasons why. I disagree.

Is long-term systemic risk is going up or down? Taking a historical perspective, there are many periods in history where systemic risk was much higher than today. 1962 is one, but there are many others, as you can see on my website extremerisk.org that links significant events to market risk.

So, much of our efforts to contain systemic risk are wasted. Even worse, all the metrics and dashboards might give us the illusion that everything is under control. They got it wrong in 2007 and also today.

© All rights reserved, Jon Danielsson, 2020