Persistence-Robust Break Detection in Predictive CoVaR Regressions

Fuente: arXiv
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Auteur principal: Hoga, Yannick
Format: Preprint
Publié: 2024
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author Hoga, Yannick
author_facet Hoga, Yannick
contents Forecasting risk (as measured by quantiles) and systemic risk (as measured by Adrian and Brunnermeiers's (2016) CoVaR) is important in economics and finance. However, past research has shown that predictive relationships may be unstable over time. Therefore, this paper develops structural break tests in predictive quantile and CoVaR regressions. These tests can detect changes in the forecasting power of covariates, and are based on the principle of self-normalization. We show that our tests are valid irrespective of whether the predictors are stationary or near-stationary, rendering the tests suitable for a range of practical applications. Simulations illustrate the good finite-sample properties of our tests. Two empirical applications concerning equity premium and systemic risk forecasting models show the usefulness of the tests.
format Preprint
id arxiv_https___arxiv_org_abs_2410_05861
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Persistence-Robust Break Detection in Predictive CoVaR Regressions
Hoga, Yannick
Methodology
Econometrics
Forecasting risk (as measured by quantiles) and systemic risk (as measured by Adrian and Brunnermeiers's (2016) CoVaR) is important in economics and finance. However, past research has shown that predictive relationships may be unstable over time. Therefore, this paper develops structural break tests in predictive quantile and CoVaR regressions. These tests can detect changes in the forecasting power of covariates, and are based on the principle of self-normalization. We show that our tests are valid irrespective of whether the predictors are stationary or near-stationary, rendering the tests suitable for a range of practical applications. Simulations illustrate the good finite-sample properties of our tests. Two empirical applications concerning equity premium and systemic risk forecasting models show the usefulness of the tests.
title Persistence-Robust Break Detection in Predictive CoVaR Regressions
topic Methodology
Econometrics
url https://arxiv.org/abs/2410.05861