Yannick Hoga
Biographic Data
| ID | 8920076 |
|---|---|
| NAME | Yannick Hoga |
| GIVEN NAMES | Yannick |
| FAMILY NAME | Hoga |
| SIGNATURE | HOGA Y |
| AFFILIATIONS | University of Duisburg-Essen |
| ORCID | 0000-0002-6332-5561 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Regressions under Adverse Conditions
We introduce a new regression method that relates the mean of an outcome variable to covariates, under the “adverse condition” that a distress variable falls in its tail. This allows to tailor classical mean regressions to adverse scenarios, which receive increasing interest in economics and finance, among many others. In the terminology of the systemic risk literature, our method can be interpreted as a regression for the Marginal Expected Short…
How to Compare Copula Forecasts
Dynamic CoVaR Modeling and Estimation
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-…
Backtesting Systemic Risk Forecasts Using Multi-Objective Elicitability
Systemic risk measures such as CoVaR, CoES, and MES are widely-used in finance, macroeconomics and by regulatory bodies. Despite their importance, we show that they fail to be elicitable and identifiable. This renders forecast comparison and validation, commonly summarized as “backtesting,” impossible. The novel notion of multi-objective elicitability solves this problem by relying on bivariate scores equipped with the lexicographic order. Based …
On Testing Equal Conditional Predictive Ability Under Measurement Error
Loss functions are widely used to compare several competing forecasts. However, forecast comparisons are often based on mismeasured proxy variables for the true target. We introduce the concept of exact robustness to measurement error for loss functions and fully characterize this class of loss functions as the Bregman class. For such exactly robust loss functions, forecast loss differences are on average unaffected by the use of proxy variables …
Extremal Dependence-Based Specification Testing of Time Series
We propose a specification test for conditional location–scale models based on extremal dependence properties of the standardized residuals. We do so comparing the left-over serial extremal dependence—as measured by the pre-asymptotic tail copula—with that arising under serial independence at different lags. Our main theoretical results show that the proposed Portmanteau-type test statistics have nuisance parameter-free asymptotic limits. The tes…
Confidence Intervals for Conditional Tail Risk Measures in Arma–GARCH Models
ARMA–GARCH models are widely used to model the conditional mean and conditional variance dynamics of returns on risky assets. Empirical results suggest heavy-tailed innovations with positive extreme value index for these models. Hence, one may use extreme value theory to estimate extreme quantiles of residuals. Using weak convergence of the weighted sequential tail empirical process of the residuals, we derive the limiting distribution of extreme…
No prominent works on this page.
Confidence Intervals for Conditional Tail Risk Measures in Arma–GARCH Models
ARMA–GARCH models are widely used to model the conditional mean and conditional variance dynamics of returns on risky assets. Empirical results suggest heavy-tailed innovations with positive extreme value index for these models. Hence, one may use extreme value theory to estimate extreme quantiles of residuals. Using weak convergence of the weighted sequential tail empirical process of the residuals, we derive the limiting distribution of extreme…
On Testing Equal Conditional Predictive Ability Under Measurement Error
Loss functions are widely used to compare several competing forecasts. However, forecast comparisons are often based on mismeasured proxy variables for the true target. We introduce the concept of exact robustness to measurement error for loss functions and fully characterize this class of loss functions as the Bregman class. For such exactly robust loss functions, forecast loss differences are on average unaffected by the use of proxy variables …
Extremal Dependence-Based Specification Testing of Time Series
We propose a specification test for conditional location–scale models based on extremal dependence properties of the standardized residuals. We do so comparing the left-over serial extremal dependence—as measured by the pre-asymptotic tail copula—with that arising under serial independence at different lags. Our main theoretical results show that the proposed Portmanteau-type test statistics have nuisance parameter-free asymptotic limits. The tes…
Backtesting Systemic Risk Forecasts Using Multi-Objective Elicitability
Systemic risk measures such as CoVaR, CoES, and MES are widely-used in finance, macroeconomics and by regulatory bodies. Despite their importance, we show that they fail to be elicitable and identifiable. This renders forecast comparison and validation, commonly summarized as “backtesting,” impossible. The novel notion of multi-objective elicitability solves this problem by relying on bivariate scores equipped with the lexicographic order. Based …
Regressions under Adverse Conditions
We introduce a new regression method that relates the mean of an outcome variable to covariates, under the “adverse condition” that a distress variable falls in its tail. This allows to tailor classical mean regressions to adverse scenarios, which receive increasing interest in economics and finance, among many others. In the terminology of the systemic risk literature, our method can be interpreted as a regression for the Marginal Expected Short…
How to Compare Copula Forecasts
Dynamic CoVaR Modeling and Estimation
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-…
Financial Risk and Volatility Modeling (5 works) · Monetary Policy and Economic Impact (5 works) · Econometrics (4 works) · Mathematics (4 works) · Estimator (3 works) · Market Dynamics and Volatility (3 works) · Statistics (3 works) · Computer Science (2 works) · Economics (2 works) · Quantile (2 works)