Intraday Conditional Value at Risk: A Periodic Mixed-Frequency GAS Approach

Abstract

We propose a copula-based periodic mixed frequency 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 which 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 in-sample and out-of-sample ECoVaR forecasting performance for a large data set of intraday asset returns of the S&P500 index.