Financial Risk Management For Dummies. Aaron Brown

are the risk of rolling a seven while trying to make your point in craps (one chance in six) or the chance of rain tomorrow. The more general risk covers situations where you can’t even specify all the possible outcomes, such as starting a war or embarking on a course of scientific research, and have no basis to estimate the probabilities of the outcomes you can foresee.

      University of Chicago professor Frank Knight famously labelled the calculated risk as risk and the second, more general condition, as uncertainty. Risk management is about the uncertainty that remains after front-office risk takers – traders, portfolio managers, lending officers and others – make the calculations that are possible. If you can calculate a risk, you almost always want to minimise it, subject to constraints. For example, a portfolio manager may select a portfolio that minimises annual volatility subject to a constraint that the expected annual return be 8 per cent or better.

      

Minimising risk isn’t managing risk. This point is important because not many people know it beyond those with extensive day-to-day experience making significant financial decisions from a risk management – as opposed to a portfolio management – perspective.

      Financial risk management is based on a different mathematical tradition than the one used in most economics and statistics. The conventional academic analysis of risk uses gambling games as models, and works only if the solution to the simplified game is a good approximation to the solution to the real-world decision. That works pretty well sometimes, and you don’t need a risk manager to help you with it. But in other cases it leads to disastrous decisions, even when done properly and carefully. Risk management doesn’t assume you know enough about possible outcomes and probabilities to treat decisions like actions in a casino game, and that you instead need to draw on concepts from information theory and other fields to improve your chances of long-term success.

      Planning and plunging

      The quotations here about planning and results emphasise a few of the ideas that a risk manager should absorb:

      ✔ ‘In preparing for battle I have always found that plans are useless, but planning is indispensable.’ General Dwight D. Eisenhower

      ✔ ‘Everybody’s got plans… until they get hit.’ Boxer Mike Tyson

      ✔ ‘If you wait until the right time to have a child you’ll die childless, and I think filmmaking is very much the same thing. You just have to take the plunge and just start shooting something even if it’s bad.’ Filmmaker James Cameron

      ✔ ‘Plunge, don’t plan.’ Instruction for commandos

      ✔ ‘Earlier theorists aimed to equip the conduct of war with principles, rules, or even systems, and thus considered only factors that could be mathematically calculated (e.g., numerical superiority; supply; the base; interior lines). All these attempts are objectionable, however, because they aim at fixed values. In war everything is uncertain and variable, intertwined with psychological forces and effects, and the product of a continuous interaction of opposites.’ General Carl von Clausewitz

      ✔ ‘Plunge boldly into the thick of life, and seize it where you will, it is always interesting.’ Philosopher Johann Wolfgang von Goethe

      Careful planning is necessary, but don’t count on anything ever going to plan, and recognise that success in anything requires risks.

      I spare you most of the gory details of the calculations you use to manage risk – or at least segregate them in technical sections with clear warning signs posted. You don’t need to do the maths to understand the ideas. However, you do need to know that maths is an option. In other words, you need to understand that you can bring powerful mathematical tools to bear on incalculable uncertainty just as you can on calculated risk.

      In my experience, people who are good at calculations tend to overanalyse the calculated risks and pretend that their models are an approximation to reality, which leads to disastrous risk management. People who aren’t good at calculations tend to emphasise the unknown unknowns (in Donald Rumsfeld’s famous phrase) – the deficiencies in the data, the un-modelled complexities of the situation and all kinds of other things that cause the calculated risks to be unreliable. This attitude is less problematic than the first, but is far from optimal. Risk managers provide a clear third voice, one that says, ‘We may not be able to calculate enough of the risks to be useful, but we can calculate our actions. We may not be able to measure the risk, but we can manage it.’

       Regenerating dinosaurs

      The movie Jurassic Park does a great job of illustrating how risk management differs from conventional approaches to uncertainty. In the book, the point is even clearer. (Author Michael Crichton should be an honorary risk manager for the many insights peppered through his fiction. I consider him the most intellectually stimulating popular fiction writer of the 20th century. He was also an outstandingly successful director and producer for movies and television.) When investors in a park that brings extinct dinosaur species back to life get concerned about the risks of the venture, they demand a report from three experts: a palaeontologist (Sam Neill), a palaeobiologist (Laura Dern) and a ‘mathematician with a deplorable excess of personality’ (Jeff Goldblum).

      A number of movie reviewers remarked on the implausibility of sending a mathematician, especially one calling himself a chaotician. But the palaeo-people can only calculate and analyse factors about dinosaurs; they have no particular training in risk and are unlikely to have the kind of life experiences that build risk wisdom. All they can do is double-check the calculations of the palaeo-experts who designed the park (which were probably double- and triple-checked already). Although some people tell you that an extra check is always prudent, I disagree. One person with clear responsibility for a decision is often more reliable than three people who all think someone else will catch any error.

      The mathematician 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.

      

Suppose someone offers you a proposal that has a 50 per cent chance of a +20 per cent return and a 50 per cent chance of a –18 per cent return. A standard approach in economics for analysing this choice begins by asking how much happier a 20 per cent increase in wealth would make you and how much unhappier an 18 per cent decrease in wealth would make you. Because the probabilities are equal, you take this gamble if the happiness increase from 20 per cent is greater than the happiness decrease from –18 per cent. With certain qualifications, this approach can be reasonable for front-office