Intraday conditional value at risk: A periodic mixed-frequency generalized autoregressive score approach

Abstract

We propose a copula-based periodic mixed frequency generalized autoregressive (GAS) framework in order to model and forecast the intraday exposure conditional value at risk (ECoVaR) for an intraday asset return and the corresponding market return. In particular, we analyze GAS models that account for long-memory-type of dependencies, periodicities, asymmetric nonlinear dependence structures, fat-tailed conditional return distributions, and intraday jump processes for asset returns. We apply our framework in order to analyze the ECoVaR forecasting performance for a large data set of intraday asset returns of the S&P500 index.