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The need for control strategies that can address dynamic system uncertainty is becoming increasingly important. In this work, we propose a Model Predictive Control by quantifying the risk of failure in our system model. The proposed control…

Systems and Control · Electrical Eng. & Systems 2023-02-17 Mostafa Tavakkoli Anbarani , Efe C. Balta , Rômulo Meira-Góes , Ilya Kovalenko

In this paper, an approach to facilitate the treatment with variabilities in system families is presented by explicitly modelling variants. The proposed method of managing variability consists of a variant part, which models variants and a…

Software Engineering · Computer Science 2010-09-28 Shamim Hasnat Ripon , Kamrul Hasan Talukder , Khademul Islam Molla

In this paper, an approach to facilitate the treatment with variabilities in system families is presented by explicitly modelling variants. The proposed method of managing variability consists of a variant part, which models variants and a…

Software Engineering · Computer Science 2013-10-02 Shamim H. Ripon

We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from…

General Economics · Economics 2019-11-21 Torsten Heinrich , Juan Sabuco , J. Doyne Farmer

A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus, in credit scoring, very simple predictive models such as…

Machine Learning · Statistics 2020-09-30 Michael Bücker , Gero Szepannek , Alicja Gosiewska , Przemyslaw Biecek

We consider the problem of forecasting debt recovery from large portfolios of non-performing unsecured consumer loans under management. The state of the art in industry is to use stochastic processes to approximately model payment behaviour…

Computation · Statistics 2022-10-26 Sam Baynes , Simon Cotter , Paul Russell , Edmund Ryan , Timothy Waite

This paper is concerned with portfolio optimization models for creating high-quality lists of recommended items to balance the accuracy and diversity of recommendations. However, the statistics (i.e., expectation and covariance of ratings)…

Information Retrieval · Computer Science 2024-10-01 Tomoya Yanagi , Shunnosuke Ikeda , Yuichi Takano

We propose a tractable unified framework to study the evolution and interaction of model-misspecification concerns and complexity aversion in repeated decision problems. This aims to capture environments where decision makers worry that…

Theoretical Economics · Economics 2026-02-18 Drew Fudenberg , Florian Mudekereza

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…

Econometrics · Economics 2022-02-08 Martin Guth

It has been for a long time to use big data of autonomous vehicles for perception, prediction, planning, and control of driving. Naturally, it is increasingly questioned why not using this big data for risk management and actuarial…

Risk Management · Quantitative Finance 2021-09-16 Jiamin Yu

In this paper we examine the claims reserving problem using Tweedie's compound Poisson model. We develop the maximum likelihood and Bayesian Markov chain Monte Carlo simulation approaches to fit the model and then compare the estimated…

Risk Management · Quantitative Finance 2009-04-10 Gareth W. Peters , Pavel V. Shevchenko , Mario V. Wüthrich

Integrated Assessment Models (IAMs) are pivotal tools that synthesize knowledge from climate science, economics, and policy to evaluate the interactions between human activities and the climate system. They serve as essential instruments…

General Economics · Economics 2025-11-04 Yongyang Cai

We propose a credit risk model for portfolios composed of green and brown loans, extending the ASRF framework via a two-factor copula structure. Systematic risk is modeled using potentially skewed distributions, allowing for asymmetric…

Risk Management · Quantitative Finance 2025-06-17 Alessandro Ramponi , Sergio Scarlatti

Solving cybersecurity issues requires a holistic understanding of components, factors, structures and their interactions in cyberspace, but conventional modeling approaches view the field of cybersecurity by their boundaries so that we are…

Cryptography and Security · Computer Science 2020-01-17 Dingyu Yan

Financial markets have developed a lot of strategies to control risks induced by market fluctuations. Mathematics has emerged as the leading discipline to address fundamental questions in finance as asset pricing model and hedging…

Probability · Mathematics 2008-12-10 Nicole El Karoui

Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using…

Machine Learning · Statistics 2019-05-28 Aliaksandr Hubin , Geir Storvik

The paper examines the potential of deep learning to support decisions in financial risk management. We develop a deep learning model for predicting whether individual spread traders secure profits from future trades. This task embodies…

Risk Management · Quantitative Finance 2019-11-19 Yaodong Yang , Alisa Kolesnikova , Stefan Lessmann , Tiejun Ma , Ming-Chien Sung , Johnnie E. V. Johnson

The instability of historical risk factor correlations renders their use in estimating portfolio risk extremely questionable. In periods of market stress correlations of risk factors have a tendency to quickly go well beyond estimated…

Adaptation and Self-Organizing Systems · Physics 2008-12-10 Vineer Bhansali , Mark B. Wise

Estimating and assessing the risk of a large portfolio is an important topic in financial econometrics and risk management. The risk is often estimated by a substitution of a good estimator of the volatility matrix. However, the accuracy of…

Applications · Statistics 2013-02-06 Jianqing Fan , Yuan Liao , Xiaofeng Shi

In statistics and machine learning, when we train a fitted model on available data, we typically want to ensure that we are searching within a model class that contains at least one accurate model -- that is, we would like to ensure an…

Statistics Theory · Mathematics 2025-06-06 Manuel M. Müller , Yuetian Luo , Rina Foygel Barber
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