Decomposing and backtesting a flexible specification for CoVaR

摘要

We introduce the Conditional Autoregressive Quantile-Located VaR (QL-CoCaViaR), which extends the Conditional Value-at-Risk (Adrian and Brunnermeier, 2016) by using an estimation process capturing the state in which the financial system and a conditioning company are jointly in distress. Furthermore, we include autoregressive components of conditional quantiles to explicitly model volatility clustering and heteroskedasticity. We support our model with a large empirical analysis, in which we use both classical and novel backtesting methods. Our results show that the quantile-located relationships lead to relevant improvements in terms of predictive accuracy during stressed periods, providing a valuable tool for regulators to assess systemic events. (C) 2019 Elsevier B.V. All rights reserved.