Franke, Jürgen Stockis, Jean-Pierre Tadjuidje, Joseph
Year of Publication:
SFB 649 discussion paper 2007,005
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.
conditional quantile time series sieve estimate neural network qualitative threshold model uniform consistency value at risk