What this chapter is about

This chapter introduces some key measures of risk and of a company's exposure to certain identified risk factors.

It introduces the use of the normal and lognormal distributions, which are employed repeatedly in later chapters.

It introduces the concept of slicing up the probability distribution, which is an important aspect of the revolution in finance that is risk management.

The chapter introduces the calculation of derivative exposures from an underlying exposure.

The chapter introduces factor models, which are a key construct for later discussions of multiple sources of risk and how a company's risks can be segmented.

Finally, the chapter discusses the many practical complications that arise in estimating risk exposures, and the dangers involved in exaggerating our ability to nail down the right model of risk and exposure.

Current events and hot topics

Underestimating and Overestimating Low Probability Events

New York Times business columnist David Leonhardt had an article in the May 31, 2010 Magazine section using the BP oil spill in the Gulf of Mexico as a case in point of our systematic tendency to underestimate certain low probability events. On the other hand, we overestimate some other low probability events. Are these just two manifestations of our fallibility? Or is there something systematic here? Is there a certain identifiable type of events that we cannot readily imagine and so we discount the likelihood of them? And are there other identifiable types of events that we all too readily imagine, and so we overestimate the likelihood of them? In horse racing there is a familiar bias known as the favorite-longshot bias which describes the often observed fact that betting odds provide biased estimates of the probability of a horse winning. Longshots are overbet, while favorites are underbet.

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