Masters of Nothing: How the crash will happen again unless we understand human nature by Matthew Hancock & Nadhim Zahawi

Author:Matthew Hancock & Nadhim Zahawi [Hancock, Matthew]
Language: eng
Format: mobi
ISBN: 9781849542081
Publisher: Biteback Publishing
Published: 2011-09-08T14:00:00+00:00

The Rise of the Machines

For centuries, mankind has tried to manage risk. Fear over the future is part of our survival instinct, whether you hoard food, save money, or arm yourself for defence. We all want a world where there is less uncertainty. We all know that bad things can happen, but if we can understand how likely they are to happen, we start to feel more in control of our own destiny. We feel even more in control if we know that we can buy insurance to compensate for the risk. From this basic human desire arose the insurance market: by creating a market for risk, people could protect themselves against the slings and arrows the world throws at us.

Demand for this kind of protection fuelled the growth of financial markets. If risk could be measured and priced, then you could mitigate the impact when something bad happened. Of course this did not prevent bad things from happening: we still had stock market crashes, or company losses. We all instinctively know that insurers, casinos, and bookmakers still lose out once in a while. Risk management cannot stop destabilising events, but it can reduce their impact. The world therefore became used to the idea that certain risks could be hedged away.

To understand why computers were so beguiling, let us consider something known as ‘Monte Carlo simulation’. It’s a method used for trying to predict how the world might look in the future, by running lots of different random scenarios. It was invented in the 1940s, by nuclear physicists, but it went on to be applied to many other fields, including financial markets. As the name suggests, its inventors were inspired by the casino of Monte Carlo, where gamblers would be prey to many different scenarios depending on the probabilities they faced. There are some gambles where we all know the probabilities: the chance of a coin landing either heads or tails, for example. But what if we had to toss 100 coins in a row? And then play a round of poker, followed by Blackjack? The possible future paths grow exponentially and we need help to work our way through the maths. Monte Carlo simulation provided a way to deal with the vast numbers of different scenarios: the computer has the power to see all the different paths simultaneously and then spit out a prediction within seconds.

This kind of power was like a drug to the derivatives industry. Derivatives are all about what might happen in the future, and the future can be complicated. Rather than buying insurance now, you might want to buy it in the future if something else happens: so you want to buy an option on an option, for example. This kind of second derivative was beyond the capabilities of the Black–Scholes model. But computers could figure out what these new kinds of options might cost. Computers could look at what might happen if an earthquake were to hit at the same time as a collapse

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