Kernel autocorrelogram for time-deformed processes

The purpose of the paper is to propose an autocorrelogram estimation procedure for irregularly spaced data which are modelled as subordinated continuous time-series processes. Such processes, also called time-deformed stochastic processes, have been proposed in a variety of contexts. Before entertaining the possibility of modelling such time series, one is interested in examining simple diagnostics and data summaries. With continuous-time processes this is a challenging task which can be accomplished via kernel estimation. The paper develops the conceptual framework, the estimation procedure and its asymptotic properties. An illustrative empirical example is also provided.