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Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and non-measurable parameters, which have to be…

Quantitative Methods · Quantitative Biology 2021-05-27 Alejandro F. Villaverde , Dilan Pathirana , Fabian Fröhlich , Jan Hasenauer , Julio R. Banga

Microsoft Excel is the most ubiquitous analytical tool ever built. Companies around the world leverage it for its power, flexibility and ease of use. However, spreadsheets are manually intensive and prone to error, making it difficult for…

Software Engineering · Computer Science 2018-02-07 Steve Litt

Understanding mortgage prepayment is crucial for any financial institution providing mortgages, and it is important for hedging the risk resulting from such unexpected cash flows. Here, in the setting of a Dutch mortgage provider, we…

Risk Management · Quantitative Finance 2021-10-14 Emanuele Casamassima , Lech A. Grzelak , Frank A. Mulder , Cornelis W. Oosterlee

We propose a method for extending a given asset pricing formula to account for two additional sources of risk: the risk associated with future changes in market--calibrated parameters and the remaining risk associated with idiosyncratic…

Disordered Systems and Neural Networks · Physics 2008-12-02 T. R. Hurd

Effective credit risk management is fundamental to financial decision-making, requiring robust models to predict default probabilities and classify financial entities. Traditional machine learning approaches face significant challenges when…

Machine Learning · Computer Science 2026-03-31 Haibo Wang , Jun Huang , Lutfu S. Sua , Figen Balo , Burak Dolar

The business model represents an increasingly important management concept. However, progress in research related to the concept is currently inhibited from inconsistencies in terms of formalizing and therewith also empirically measuring…

Computers and Society · Computer Science 2015-03-05 Fredrik Hacklin , Nobuaki Minato , Toma Kobayashi

This paper studies the properties of the optimal portfolio-consumption strategies in a {finite horizon} robust utility maximization framework with different borrowing and lending rates. In particular, we allow for constraints on both…

Portfolio Management · Quantitative Finance 2018-12-06 Zhou Yang , Gechun Liang , Chao Zhou

Knowing the uncertainty in a prediction is critical when making expensive investment decisions and when patient safety is paramount, but machine learning (ML) models in drug discovery typically provide only a single best estimate and ignore…

Machine Learning · Computer Science 2021-06-03 Stanley E. Lazic , Dominic P. Williams

This paper proposes a dynamic process of portfolio risk measurement to address potential information loss. The proposed model takes advantage of financial big data to incorporate out-of-target-portfolio information that may be missed when…

Risk Management · Quantitative Finance 2022-02-17 Kwangmin Jung , Donggyu Kim , Seunghyeon Yu

In the current insurance literature, prediction of insurance claims in the regression problem is often performed with a statistical model. This model-based approach may potentially suffer from several drawbacks: (i) model misspecification,…

Machine Learning · Statistics 2025-09-30 Liang Hong

Trust is a crucial factor affecting the adoption of machine learning (ML) models. Qualitative studies have revealed that end-users, particularly in the medical domain, need models that can express their uncertainty in decision-making…

Machine Learning · Computer Science 2023-04-21 Andrew Houston , Georgina Cosma

In this paper we propose a framework for assessing the risk associated with deploying a machine learning model in a specified environment. For that we carry over the risk definition from decision theory to machine learning. We develop and…

The estimation of the amount of uncertainty featured by predictive machine learning models has acquired a great momentum in recent years. Uncertainty estimation provides the user with augmented information about the model's confidence in…

Machine Learning · Computer Science 2022-10-31 Ibai Laña , Ignacio , Olabarrieta , Javier Del Ser

For credit risk management purposes in general, and for allocation of regulatory capital by banks in particular (Basel II), numerical assessments of the credit-worthiness of borrowers are indispensable. These assessments are expressed in…

Other Condensed Matter · Physics 2008-12-02 Katja Pluto , Dirk Tasche

Robust control is a core approach for controlling systems with performance guarantees that are robust to modeling error, and is widely used in real-world systems. However, current robust control approaches can only handle small system…

Optimization and Control · Mathematics 2021-06-08 Dimitar Ho , Hoang M. Le , John C. Doyle , Yisong Yue

Macroeconomic factors have a critical impact on banking credit risk, which cannot be directly controlled by banks, and therefore, there is a need for an early credit risk warning system based on the macroeconomy. By comparing different…

Information Retrieval · Computer Science 2024-01-29 Hemlata Sharma , Aparna Andhalkar , Oluwaseun Ajao , Bayode Ogunleye

In robust optimization, the uncertainty set is used to model all possible outcomes of uncertain parameters. In the classic setting, one assumes that this set is provided by the decision maker based on the data available to her. Only…

Optimization and Control · Mathematics 2019-01-23 Trivikram Dokka , Marc Goerigk , Rahul Roy

The quality and correct functioning of software components embedded in electronic systems are of utmost concern especially for safety and mission-critical systems. Model-based testing and formal verification techniques can be employed to…

Formal Languages and Automata Theory · Computer Science 2019-01-08 Shahbaz Ali , Hailong Sun , Yongwang Zhao

The purpose of this research article is to discover how the econophysics analysis can complement the econometrics models in application to the risk management in the central banks and financial institutions, operating within the nonlinear…

General Finance · Quantitative Finance 2012-11-20 Dimitri O. Ledenyov , Viktor O. Ledenyov

Computational models in chemistry rely on a number of approximations. The effect of such approximations on observables derived from them is often unpredictable. Therefore, it is challenging to quantify the uncertainty of a computational…

Chemical Physics · Physics 2017-04-21 Gregor N. Simm , Jonny Proppe , Markus Reiher
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