It has long been known that maximum likelihood estimation in a Poisson model reproduces the chain-ladder technique. We revisit this model. A new canonical parametrisation is proposed to circumvent the inherent identification problem in the parametrisation. The maximum likelihood estimators for...

This paper is concerned with the Bayesian estimation of non-linear stochastic differential equations when only discrete observations are available. The estimation is carried out using a tuned MCMC method, in particular a blcked Metropolis-Hastings algorithm, by introducing auxiliary points and...

The log normal reserving model is considered. The contribution of the paper is to derive explicit expressions for the maximum likelihood estimators. These are expressed in terms of development factors which are geometric averages. The distribution of the estimators is derived. It is shown that...

Stochastic volatility models present a natural way of working with time-varying volatility. However the difficulty involved in estimating these types of models has prevented their wide-spread use in empirical applications. In this paper we exploit Gibbs sampling to provide a likelihood framework...