doesn’t do the careful observation of the other two experts – the palaeontologist who scrutinises the pack dynamics of running gallimimus or the palaeobiologist who sticks her arms into triceratops excrement. However, he correctly predicts disaster, without knowing anything about dinosaurs, genetics or park security. He understands that evolution is a powerful force powered by risk – far too powerful to be controlled by electric fences. (Evolution is also known as natural selection of random variation, and both random and variation are essential risk concepts.) He did not predict the specifics of disaster, only that the imperatives of life would easily win over the calculations of human experts.
Risk managers understand that risk is a powerful force that can be harnessed for great success or that can blast apart the best-laid schemes. Risk is not about laying better schemes; it’s about making sure that risk is the wind in your sails, not the approaching hurricane that will swamp your boat. And generally speaking (although certainly not always), experts in specialised fields are bad at recognising risk. Experts usually get paid to take the risk out of decisions – or at least to reduce the risk by making things more predictable. Doing so is certainly worthwhile, but it never works perfectly, so you need risk managers as well. More importantly, experts often get paid to reduce the appearance of risk, not risk itself. And most important of all, reflexively taking the risk out of decisions eliminates opportunities as well as dangers.
Adding a little maths
As I say, you need no maths to understand this book. However, if you’re willing to dip your toe into mathematical waters, you can get a deeper understanding of risk management more quickly. Feel free to skip this section if you’re not interested in the maths at all.
Most non-economists would find such a gamble too risky for 100 per cent of their wealth, but the risk gets more attractive if it can be repeated many times. With many repetitions, this gamble seems like being the casino – statistically certain to win in the long run due to a built-in edge.
The chart in Figure 1-2 shows a random simulation of 20 risk takers who repeat this bet 250 times, starting with initial wealth of 1. The solid black curve shows the growth of wealth at the expected rate of 1 per cent per bet (maths alert: 50 per cent probability times 20 per cent plus 50 per cent probability times –18 per cent equals 10 per cent – 9 per cent = 1 per cent expected growth of wealth) and the 20 other lines show individual paths.
© John Wiley & Sons, Inc.
Figure 1-2: Charting growth in wealth.
Most paths go quickly to near zero. A few soar up far beyond the expected one per cent rate for a while, but all eventually crash. If you run the simulation longer, all paths would become indistinguishable from zero. To a risk manager, this bet is terrible – one that leads to certain disaster. The more times you repeat it, the worse it gets, not the better. Your psychology, your risk appetite, has nothing to do with it. This bet is worse than just losing all your money quickly because the paths that soar attract imitators and cause all kinds of foolish overreactions.
The problem is simple. If you win half your bets, you lose money. If you win 20 per cent, you turn £1.00 into £1.20. If you then lose 18 per cent, your £1.20 falls to £0.984. (The order doesn’t matter. If you first lose 18 per cent to turn £1.00 into £0.82, then a 20 per cent win turns £0.82 to the same £0.984.) Every pair of win and loss costs you 0.6 per cent of your wealth. In the long run, you’re virtually certain to have nearly 50 per cent wins and losses, so you’re virtually certain to wipe out your wealth.
How does the median 0.3 per cent loss per bet square with the expected 1 per cent return? It’s absolutely true that your expected wealth increases 1 per cent each time you repeat this bet, but in the long run this fact results from a microscopic probability of winning an astronomical amount of money. You’re virtually certain to be broke, but theoretically have enough chance of winning far more money than exists in the universe that your expected value is positive.
This example is oversimplified, of course. With real risks, you never know the exact probabilities and outcomes. You don’t repeat them an infinite number of times, and the results are not independent of each other. You don’t bet constant fractions of your wealth each time. I use the example only to make the point that you can ask two different questions about any risk:
✔ The line risk taker, the person making risk decisions, asks some version of, ‘Will I be happier on average, or will the organisation be better off on average, if I take this specific bet once?’
✔ The risk manager asks, ‘Will a long-term strategy of taking this kind of bet lead with average luck to exponential growth or to disaster?’
The answers to these two questions are independent. Some risks increase average utility if taken once but can’t be accepted as part of a systematic strategy that leads to success, and some risks fit perfectly into systematic strategies but are unattractive as individual propositions. The only risks worth taking are the ones that make sense on their own and as steps in the long-term strategy. That’s why you need both line risk takers to ensure the first, and risk managers to ensure the second.
I emphasize that this is a practical result discovered by experience, not a theoretical one. The mathematical example was invested to illustrate the idea; it's not the source of it. Quantitative risk managers learned that it was possible to analyse real risk-taking histories of real risk takers without assuming anything about probabilities or future possibilities or risk preferences and determine accurately whether they were on paths to riches or ruin. First they learned with their own risk taking, often from bitter experience, and then they learned it was possible to prove their contentions to risk takers, even when markets were in the peaks of success or the depths of slumps. This was the birth of the modern field of quantitative risk management.
Working with Financial Risk
The topic of this book is financial risk. Financial risk is created by people. It can represent natural risk: for example, an insurance company writing hurricane insurance or a venture capital investor taking on some of the economic risk of a start-up company. But most financial risk is entirely contained within the financial system, such as a futures trader making zero-sum bets with other futures traders or a government bond portfolio manager speculating on changes in interest rates.
Even when financial risk represents physical risk, it represents the virtual version, not the real thing. An insurance company writes checks after a hurricane; it isn’t pinned under a fallen tree without fresh water available. A venture capitalist writes off his investment if the company fails; he doesn’t fire people and auction off the office furniture.
The idea of converting physical and economic risk to virtual form and trading it is revolutionary. It allows people to shed their excess concentrated risk, such as that their company will fail or their house
