ABSTRACT
Monte Carlo methods are ubiquitous in applications in the finance and insurance industry. They are often the only accessible tool for financial engineers and actuaries when it comes to complicated price or risk computations, in particular for those that are based on many underlyings. However, as they tend to be slow, it is very important to have a big tool box for speeding them up or – equivalently – for increasing their accuracy. Further, recent years have seen a lot of developments in Monte Carlo methods with a high potential for success in applications. Some of them are highly specified (such as the Andersen algorithm in the Heston setting), others are general algorithmic principles (such as the multilevel Monte Carlo approach). However, they are often only available in working papers or as technical mathematical publications.
