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Found 9 papers, 3 papers with code
Regressions under Adverse Conditions
We introduce a new regression method that relates the mean of an outcome variable to covariates, given the "adverse condition" that a distress variable falls in its tail.
Evaluating Probabilistic Classifiers: The Triptych
Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or confidence scores, are ubiquitous in science and society, and methods for evaluating and comparing them are in great demand.
Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models
This paper analyzes the benefits of sampling intraday returns in intrinsic time for the standard and pre-averaging realized variance (RV) estimators.
Dynamic CoVaR Modeling
In this article, we propose joint dynamic forecasting models for the Value-at-Risk (VaR) and CoVaR.
On Testing Equal Conditional Predictive Ability Under Measurement Error
For such exactly robust loss functions, forecast loss differences are on average unaffected by the use of proxy variables and, thus, inference on conditional predictive ability can be carried out as usual.
Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary
We propose forecast encompassing tests for the Expected Shortfall (ES) jointly with the Value at Risk (VaR) based on flexible link (or combination) functions.
Evaluating probabilistic classifiers: Reliability diagrams and score decompositions revisited
A probability forecast or probabilistic classifier is reliable or calibrated if the predicted probabilities are matched by ex post observed frequencies, as examined visually in reliability diagrams.
Testing Forecast Rationality for Measures of Central Tendency
We propose tests of forecast rationality when the measure of central tendency used by the respondent is unknown.
Forecast Encompassing Tests for the Expected Shortfall
We introduce new forecast encompassing tests for the risk measure Expected Shortfall (ES).
