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#### Year of publication

- 2007 (4) (remove)

#### Keywords

- Mixture Models (2)
- Geometric Ergodicity (1)
- Identifiability (1)
- Markov Chain (1)
- Neural networks (1)
- Nonparametric AR-ARCH (1)
- conditional quantile (1)
- image denoising (1)
- localization (1)
- neural network (1)

We consider the problem of estimating the conditional quantile of a time series at time \(t\) given observations of the same and perhaps other time series available at time \(t-1\). We discuss sieve estimates which are a nonparametric versions of the Koenker-Bassett regression quantiles and do not require the specification of the innovation law. We prove consistency of those estimates and illustrate their good performance for light- and heavy-tailed distributions of the innovations with a small simulation study. As an economic application, we use the estimates for calculating the value at risk of some stock price series.

In this paper we consider a CHARME Model, a class of generalized mixture of nonlinear nonparametric AR-ARCH time series. We apply the theory of Markov models to derive asymptotic stability of this model. Indeed, the goal is to provide some sets of conditions under which our model is geometric ergodic and therefore satisfies some mixing conditions. This result can be considered as the basis toward an asymptotic theory for our model.