Abstract (EN)

This article presents and compares two different Kalman filters. These methods provide a very interesting way to cope with the presence of non-observable variables, which is a frequent problem in finance. They are also very fast even in the presence of a large information volume. The first filter presented, which corresponds to the simplest version of a Kalman filter, can be used solely in the case of linear models. The second filter - the extended one - is a generalization of the first one, and it enables one to deal with non-linear models. However, it also introduces an approximation in the analysis, whose possible influence must be appreciated. The principles of the method and its advantages are first presented. It is then explained why it is interesting in the case of term structure models of commodity prices. Choosing a well-known term structure model, practical implementation problems are discussed and tested. Finally, in order to appreciate the impact of the approximation introduced for non-linear models, the two filters are compared.