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Assessing Financial Model Risk [PDF]
European Journal of Operational Research, 2013 Model risk has a huge impact on any risk measurement procedure and its quantification is therefore a crucial step. In this paper, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the ...
Barrieu, Pauline, Scandolo, Giacomo
core +10 more sources
Model risk on credit risk [PDF]
Risk and Decision Analysis, 2016 This paper develops the Jungle model in a credit portfolio framework. The Jungle model is able to model credit contagion, produce doubly-peaked probability distributions for the total default loss and endogenously generate quasi phase transitions, potentially leading to systemic credit events which happen unexpectedly and without an underlying single ...
Molins, J. +1 more
openaire +7 more sources
Model Risk of Risk Models [PDF]
SSRN Electronic Journal, 2014 This paper evaluates the model risk of models used for forecasting systemic and market risk. Model risk, which is the potential for different models to provide inconsistent outcomes, is shown to be increasing with and caused by market uncertainty. During calm periods, the underlying risk forecast models produce similar risk readings, hence, model risk ...
Danielsson, Jon +3 more
openaire +6 more sources
Model Risk in Portfolio Optimization [PDF]
Risks, 2014 We consider a one-period portfolio optimization problem under model uncertainty. For this purpose, we introduce a measure of model risk. We derive analytical results for this measure of model risk in the mean-variance problem assuming we have ...
David Stefanovits +2 more
doaj +5 more sources
Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning [PDF]
Natural Hazards and Earth System Sciences, 2023 This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets, while also leveraging recent advances in machine-learning algorithms.
A. Rao +6 more
doaj +1 more source
Bulletin of Applied Economics, 2021 20 pages; to appear in Bulletin of Applied Economics (in press)
Zura Kakushadze, Willie Yu
openaire +3 more sources
A Gentle Introduction to Model Risk Quantification in Commercial Banking [PDF]
Risk Management Magazine, 2022 Model risk is investigated from a commercial banking viewpoint. We firstly analyze model misspecification. Then, the focus shifts towards model sensitivity. Finally, interactions among various models are scrutinized.
Tiziano Bellini
doaj +1 more source
Backward Deep BSDE Methods and Applications to Nonlinear Problems
Risks, 2023 We present a pathwise deep Backward Stochastic Differential Equation (BSDE) method for Forward Backward Stochastic Differential Equations with terminal conditions that time-steps the BSDE backwards and apply it to the differential rates problem as a ...
Yajie Yu +2 more
doaj +1 more source
Financial Innovation, 2022 Implementing new machine learning (ML) algorithms for credit default prediction is associated with better predictive performance; however, it also generates new model risks, particularly concerning the supervisory validation process.
Andrés Alonso Robisco +1 more
semanticscholar +1 more source
Predicting take-up of home loan offers using tree-based ensemble models: A South African case study
South African Journal of Science, 2021 We investigated different take-up rates of home loans in cases in which banks offered different interest rates. If a bank can increase its take-up rates, it could possibly improve its market share.
Tanja Verster +3 more
doaj +1 more source