and communicated to the world. The back office does all the work, and the front office takes all the risks, but the middle office is where the risk is managed.
Communicating Risk
I’m often asked what the most important job of a risk manager is. The answer is simple but unexpected to most people: The risk manager’s most crucial task is to communicate a single vision of risk to all stakeholders: equity holders, creditors, customers, executives, regulators, employees, trading counterparties – everyone. It’s nice if that single vision happens to be accurate, but unfortunately you can only do your best in that respect. What you can promise is that the vision is the same for everyone.
Suppose that an entrepreneur lays out a proposed project. A lot of people take a look, and most have no interest. But some people are optimistic enough to lend money for the venture. Others are even more optimistic and are willing to put money in for a share of any profits after the lenders are paid. Some people want to work in the project for salary, or for equity options. Some people want to sign up to be suppliers to the project, or customers of it. The government probably gets into the act with various regulatory and tax interests. These people disagree by necessity; otherwise they would all be vying for the same role.
How do you keep the process honest? That is, how do you prevent the entrepreneur from telling creditors that the project will be run for maximum safety of repayment, telling the equity buyers that the project will be run for maximum upside, and the government that it will be run for social benefit? How do you prevent the owner from promising the same money to employees, suppliers and customers?
If you knew exactly how the project would turn out, an accountant could audit the projected books to make sure that each dollar went to exactly one place. However, given that many future scenarios are possible, that solution isn’t practical. Instead of an accountant, you need a risk manager to lay out the range of possible futures in a form that balances simplicity (so stakeholders can understand it) with detail (so it captures the important contingencies and decisions). Each stakeholder makes an informed decision to participate based on a consistent promise of how the project will be run.
Of course, the actual outcome of the project will differ from all the risk manager’s projections, perhaps in crucial ways. Some stakeholders may prosper while others suffer. After the fact, it’s impossible to say whether the outcomes were fair or not. However, as long as everyone had the same risk information going in and as long as the project was run consistent with the promises made, then the responsibility for any gain or loss rests with the stakeholders’ choices, and is fair in that sense. (I talk about communication in Chapter 18.)
This isn’t to say that communication is the only duty of a financial risk manager. You can do things to make risk taking more productive and successful. You can encourage good risk (innovation, opportunity, experimentation, creativity, attractive bets) and discourage bad risk (carelessness, recklessness, unnecessary danger, chasing unattractive bets). You can build a positive risk culture, and gain consensus behind a shrewd risk strategy. But consistent risk communication is job one.
Chapter 2
Understanding Risk Models
▶ Flipping coins and betting on possibilities
▶ Spinning the wheel in predicting outcomes
▶ Exploring scientific risk theories
Risk management is a quantitative discipline, which means that it works with models of risk rather than risk directly. Choosing the right model is crucial. Most people make errors in risk management because they’ve no quantitative model of risk. Experts, by contrast, often make errors by being wedded to an inappropriate model of risk.
Risk managers must understand the common risk models, especially their flaws. This chapter explains many of the risk models you can use to support your risk management decisions, and how to spot errors in existing risk management frameworks.
Comparing Frequentism and Bayesianism
A famous scene in the film Zero Dark Thirty involves the director of the Central Intelligence Agency conferring with some subordinates about whether Osama bin Laden is in a house the agency has identified in Pakistan. “I’d say there’s a 60 per cent probability he’s there,” says the deputy director. What exactly does that mean?
The most common interpretation of probability statements among quantitative people is frequentism. In this view, the deputy means that given 100 potential missions with the same quality intelligence as is available for this one, he would guess about 60 of them would have the target’s location identified correctly.
The second favourite interpretation is that the deputy would bet $60 against $40 that Osama bin Laden is in the house. This goes by the name Bayesianism.
Financial risk managers make use of both models, although Bayesianism is generally more useful than frequentism. But they use many other models as well. Most importantly, they pay careful attention to precisely which model is in use. Never make a probability statement without being sure about the model you’re using, and never fall in love with one particular model so that you ignore evidence from other approaches.
The next sections discuss these two risk models. Despite the deep philosophic gulf between the two camps, frequentist and Bayesian statisticians mostly use the same tools and mostly come to the same conclusions. When the data clearly indicate a conclusion, any reasonable method works. If a drug immediately cures 90 per cent of the people who take it, philosophic subtleties don’t matter. The drug works for frequentists and Bayesians and everyone else. On the other hand, if 51 out of 100 people survive after taking a drug, but 50 per cent survive untreated, and a few ambiguous cases come to light and some people experience serious side effects, statisticians cannot help. You need more data and doctors and other subject-matter experts to examine the details of the experiment – not a better analysis.
In cases where there’s moderate but not overwhelming evidence in favour of a proposition, statisticians have something to offer and may disagree. However, you don’t find that frequentists are more apt to agree with other frequentists, nor Bayesians more apt to agree with other Bayesians. Different conclusions depend on the models and forms of analysis and on adjustments to the data or assumptions, not on the fundamental approach to risk.
Early risk theory was based on a limited idea of uncertainty. It models risk as a casino game that can be played over and over, with the range of outcomes and their probabilities known to all. The early view didn’t allow for the possibility of someone being able to get superior information about outcomes or to influence those outcomes. This type of risk simply doesn’t exist except in casinos and other gambling places where extreme care is taken to create it (and as I show in “Analysing Roulette” later in the chapter, it really doesn’t even exist there). The dice games and lotteries used in the early study of risk are poor models for the uncertainty
