Thursday 14 May 2009

Ep 106: The Global Financial Crisis - The Mathematical Causes

This week on the podcast we are tackling something dear to all our hearts, money.

The Global Financial Crisis has hit many people hard, with the resultant economic recession causing job losses, stock market crashes and company failures. But what started it all? Why are we in the midst of the worst financial crisis since the Great Depression?

I spoke to Nick Davis from the World Economic Forum to answer some of these questions and to toss up ideas on how we might emerge from the crisis. The World Economic Forum is an independent international organisation committed to improving the state of the world by engaging leaders in partnerships to shape global, regional and industry agendas. Nick is based in Geneva, Switzerland and is Associate Director and a Global Leadership Fellow within the World Economic Forum Scenario Planning Team. The team examines possible world scenarios that could arise in the future. The scenarios are not attempts to predict the future; rather, they aim to sketch the boundaries of the plausible. They explore the possibly diverse eventualities of how the world might look if the most uncertain and important drivers unfold in different ways. Some of the scenarios they have looked at include the world's ageing population, the future of engineering and construction and what the world's economic systems might look like post-crisis.

Nick has worked extensively in understanding the causes of the global economic crisis and chatted to me down the phone from Geneva. It should be noted that the opinions he expressed are his and not necessarily those of the World Economic Forum.

Listen to this podcast here:

We noted a number of causes of the crisis, including the fact that the world financial system was metastable - that is, before the crisis it was at a delicate equilibrium and susceptible to collapse. The factors that built this metastability included:

  1. A worldwide expansion of credit since 1980 - money became cheap as international monetary policy kept interest rates low. Essentially, you could borrow heaps of money from within your own country and outside of it, and lots of people were giving out loans;
  2. The subprime mortgage crisis - a large percentage of housing buyers in the US could not securely finance their property loans;
  3. The false assumption that housing prices always go up and the notion that we should all become homeowners - this created a housing bubble;
  4. Globalisation - more and deeper connections between institutions were created. This meant that when one company went down, it would trigger a collapse like a house of cards;
  5. Securitisation of home loans to make them tradeable. This meant the inherent risk that banks take on when they give out a loan was spread across many financial instruments, all across the world. This obscured the level of risk people were holding. Even rating agencies got the risk levels wrong. Here is where the maths comes in - and we got it wrong.
So what we did we get wrong with the mathematical models? For a start, they assumed a high level of liquidity, which means that it was assumed that whenever you wanted to trade, you could find someone to trade with . But this was not true - companies were unable to trade their securitised mortgages due to a loss of investor confidence, and so they were stuck with assets with falling value. Homeowners also found this when the bubble burst.

The models also used normal distributions of stock market movements. What's wrong with this, you may ask. Well, the world of finance unfortunately doesn't work this way. It is worth here noting the distinction between risk, which is something we can model, and uncertainty, which is more difficult. You can fit a probability curve to risk based on past experience - for example, you toss a coin, bet on tails but heads come up. You took a quantifiable risk. Uncertainty refers however to things you can't even predict.

Nick used the examples of Mediocristan and Extremistan to illustrate the difference between risk and uncertainty. In Mediocristan, everything fits nicely under a bell curve. Extreme events are so rare that we can essentially ignore them. For example, if you surveyed 1000 people and plotted their weights, you would come up with something like a normal distribution. If you then found the heaviest person on Earth, he would be to the far right of our curve, but not so much that the normal distribution fit would become inappropriate. Finance, however, does not work this way. Imagine you surveyed the incomes of 1000 people. This may also look like a normal distribution - but then take the richest man on Earth and look at what it does to the distribution. Bill Gates would probably earn more than everyone you sampled put together. This is Extremistan. The worlds were the inventions of Nassim Nicholas Taleb who came up with the idea of the Black Swan. The name comes from the idea that before black swans were discovered, everyone thought swans were white - they had no reason to think otherwise. This is an example of uncertainty as you can not quantify the probability of finding a black swan when you don't even know they exist.

It's very difficult to deal with black swan events - like in Extremistan when we discovered someone a million times richer than anyone we have ever seen. The mathematical models dealing with securitised mortgages attempted to model risk in a far too simple way and did not take into account the uncertainty of the entire housing market collapsing (many defaulting on their loans at once - our black swan). The fact that uncertainty is so difficult to model is why we have the World Economic Forum Scenario Planning Team!

Listen to this podcast here:

And for all the Nick Davis fans out there, check out his blog Managing Uncertainty and watch the following video of Nick in action for the World Economic Forum. Thanks again Nick!

There is more from the World Economic Forum and the Global Financial Crisis on youtube here.


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