Predicting Default Probabilities for Stress Tests: A Comparison of Models
Since the Great Financial Crisis (GFC), the use of stress tests as a tool for assessing the resilience of financial institutions to adverse financial and economic developments has increased significantly. One key part in such exercises is the translation of macroeconomic variables into default probabilities for credit risk by using macrofinancial linkage models. A key requirement for such models is that they should be able to properly detect signals from a wide array of macroeconomic variables in combination with a mostly short data sample. The aim of this paper is to compare a great number of different regression models to find the best performing credit risk model. We set up an estimation framework that allows us to systematically estimate and evaluate a large set of models within the same environment. Our results indicate that there are indeed better performing models than the current state-of-the-art model. Moreover, our comparison sheds light on other potential credit risk models, specifically highlighting the advantages of machine learning models and forecast combinations.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
The two defaults scenario for stressing credit portfolio loss distributions
The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate…
Vocal Bursts Valence PredictionBenchmarking Machine Learning Models to Predict Corporate Bankruptcy
Using a comprehensive sample of 2,585 bankruptcies from 1990 to 2019, we benchmark the performance of various machine learning models in predicting financial distress of publicly traded U.S. firms. We find that gradient …
BenchmarkingWhat do central counterparties default funds really cover? A network-based stress test answer
In the last years, increasing efforts have been put into the development of effective stress tests to quantify the resilience of financial institutions. Here we propose a stress test methodology for central counterpartie…
CausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment
The interconnected nature of global financial systems makes them vulnerable to systemic risks, where the failure of a few institutions can trigger catastrophic cascading defaults. Traditional risk models often fail to ca…
Graph Neural NetworkComputing the Probability of a Financial Market Failure: A New Measure of Systemic Risk
This paper characterizes the probability of a market failure defined as the default of two or more globally systemically important banks (G-SIBs) in a small interval of time. The default probabilities of the G-SIBs are c…