  One of the main goals in non-life insurance is to estimate the claims reserve
distribution. A generalized time series model, that allows for modeling the
conditional mean and variance of the claim amounts, is proposed for the claims
development. On contrary to the classical stochastic reserving techniques, the
number of model parameters does not depend on the number of development
periods, which leads to a more precise forecasting.
  Moreover, the time series innovations for the consecutive claims are not
considered to be independent anymore. Conditional least squares are used for
model parameter estimation and consistency of such estimate is proved. Copula
approach is used for modeling the dependence structure, which improves the
precision of the reserve distribution estimate as well.
  Real data examples are provided as an illustration of the potential benefits
of the presented approach.
