Introduction

The hardest part of quantitative risk analysis is to find the stochastic models and judge their realism. This is discussed later. What is addressed now is how models are used once they are in place. Only a handful of probability distributions have been introduced, and yet a good deal can be achieved already. The present chapter is a primer introducing the main arenas and their first treatment computationally. We start with property insurance (an area of huge uncertainty) where core issues can be reached with very simple modelling. Life insurance is quickly reached too, but now something is very different. Once the stochastic model is given, there is little risk left! This doesn't rule out much uncertainty in the model itself, a topic discussed in Section 15.2. With financial risk there is again much randomness under the model assumed.

The target of this chapter is the general line. Many interesting points (demanding heavier modelling) are left out and dealt with later. A unifying theme is Monte Carlo as problem solver. By this we do not mean the computational technique which was treated in the preceding chapter (and in the next one too). What is on the agenda is the art of making the computer work for a purpose, how we arrange for it to chew away on computational obstacles and how it is utilized to get a feel for numbers. Monte Carlo is also an efficient way of handling the myriad of details in practical problems. Feed them into the computer and let simulation take over. Implementation is often straightforward, and existing programs might be reused with minor variations.