Dynamic CoVaR Modeling and Estimation

Timo Dimitriadis

doi:10.1080/07350015.2025.2590126

2025-12-01

Timo Dimitriadis *

Yannick Hoga

DOI：10.1080/07350015.2025.2590126

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摘要

En 中文

The popular systemic risk measure CoVaR (conditional Value-at-Risk) and its variants are widely used in economics and finance. In this article, we propose joint dynamic forecasting models for the Value-at-Risk (VaR) and CoVaR. The CoVaR version we consider is defined as a large quantile of one variable (e.g., losses in the financial system) conditional on some other variable (e.g., losses in a bank's shares) being in distress. We introduce a two-step M-estimator for the model parameters that draws on recently proposed bivariate scoring functions for the pair (VaR, CoVaR). We prove consistency and asymptotic normality of our parameter estimator and analyze its finite-sample properties in simulations. Finally, we apply a specific subclass of our dynamic forecasting models, which we call CoCAViaR models, to log-returns of large US banks. A formal forecast comparison shows that our CoCAViaR models generate CoVaR predictions that are superior to forecasts issued from current benchmark models.

Keyword:

CoVaR

Estimation

Forecasting

Modeling

Systemic risk

CoVaR

Estimation

Forecasting

Modeling

Systemic risk