Chapter 13

Parameter estimation in discretely observed SDEs

13.1 Introduction

This chapter describes methods for estimating parameters in stochastic differential equations (SDEs). A brief introduction to the GMM method is given, but a major part of the presentation is devoted to a class of maximum likelihood methods which can be used for estimation parameters in both linear and non-linear SDEs.

It is clear that a method for estimating parameters of non-linear stochastic differential equations can also be used for estimating the parameters of a linear stochastic differential equation. If a linear model is considered, it is, however, advantageous to take the linearity into account at the estimation procedure. Likewise it is beneficial to simplify ...

Get Statistics for Finance now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.