| Abstract: | Statistical applications of classical parametric max-stable processes are still sparse mostly due to lack of 1) efficiency of statistical estimation of many parameters in the processes, 2) flexibility of concurrently modeling asymptotic independence and asymptotic dependence among variables, and 3) capability of fitting real data directly. This paper studies a more flexible model, i.e. a class of copula structured M4 (multivariate maxima and moving maxima) processes, and hence CSM4 for short. CSM4 processes are constructed by incorporating sparse random coefficients and structured extreme value copulas in asymptotically (in)dependent M4 (AIM4) processes. As a result, the new model overcomes all of the aforementioned constraints. The paper illustrates these new features and advantages of the CSM4 model using simulated examples and real data of intra-daily maxima of high-frequency financial time series. The paper also studies probabilistic properties of the proposed model, statistical estimators and their properties. |
