arXiv - QuantFin - Risk Management最新文献_第10页

Deep Learning Based Measure of Name Concentration Risk 基于深度学习的名称集中风险衡量标准

arXiv - QuantFin - Risk Management Pub Date : 2024-03-25 DOI: arxiv-2403.16525

Eva Lütkebohmert, Julian Sester

引用次数: 0

Risk exchange under infinite-mean Pareto models 无限均值帕累托模型下的风险交换

arXiv - QuantFin - Risk Management Pub Date : 2024-03-24 DOI: arxiv-2403.20171

Yuyu Chen, Paul Embrechts, Ruodu Wang

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Spanning Multi-Asset Payoffs With ReLUs 用再循环单位跨越多资产报酬率

arXiv - QuantFin - Risk Management Pub Date : 2024-03-21 DOI: arxiv-2403.14231

Sébastien BossuLPSM, UPCité, Stéphane CrépeyLPSM, UPCité, Hoang-Dung NguyenLPSM, UPCité

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A Markov approach to credit rating migration conditional on economic states 以经济状态为条件的信用评级迁移马尔可夫方法

arXiv - QuantFin - Risk Management Pub Date : 2024-03-21 DOI: arxiv-2403.14868

Michael Kalkbrener, Natalie Packham

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Uncertainty in the financial market and application to forecastabnormal financial fluctuations 金融市场的不确定性及其在预测正常金融波动中的应用

arXiv - QuantFin - Risk Management Pub Date : 2024-03-19 DOI: arxiv-2403.12647

Shige Peng, Shuzhen Yang, Wenqing Zhang

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Default Resilience and Worst-Case Effects in Financial Networks 金融网络中的违约弹性和最坏情况效应

arXiv - QuantFin - Risk Management Pub Date : 2024-03-15 DOI: arxiv-2403.10631

Giuseppe Calafiore, Giulia Fracastoro, Anton Proskurnikov

{"title":"Default Resilience and Worst-Case Effects in Financial Networks","authors":"Giuseppe Calafiore, Giulia Fracastoro, Anton Proskurnikov","doi":"arxiv-2403.10631","DOIUrl":"https://doi.org/arxiv-2403.10631","url":null,"abstract":"In this paper we analyze the resilience of a network of banks to joint price\u0000fluctuations of the external assets in which they have shared exposures, and\u0000evaluate the worst-case effects of the possible default contagion. Indeed, when\u0000the prices of certain external assets either decrease or increase, all banks\u0000exposed to them experience varying degrees of simultaneous shocks to their\u0000balance sheets. These coordinated and structured shocks have the potential to\u0000exacerbate the likelihood of defaults. In this context, we introduce first a\u0000concept of {default resilience margin}, $epsilon^*$, i.e., the maximum\u0000amplitude of asset prices fluctuations that the network can tolerate without\u0000generating defaults. Such threshold value is computed by considering two\u0000different measures of price fluctuations, one based on the maximum individual\u0000variation of each asset, and the other based on the sum of all the asset's\u0000absolute variations. For any price perturbation having amplitude no larger than\u0000$epsilon^*$, the network absorbs the shocks remaining default free. When the\u0000perturbation amplitude goes beyond $epsilon^*$, however, defaults may occur.\u0000In this case we find the worst-case systemic loss, that is, the total unpaid\u0000debt under the most severe price variation of given magnitude. Computation of\u0000both the threshold level $epsilon^*$ and of the worst-case loss and of a\u0000corresponding worst-case asset price scenario, amounts to solving suitable\u0000linear programming problems.}","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140169145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Fairness in Credit Lending Models using Subgroup Threshold Optimization 利用分组阈值优化提高信用借贷模型的公平性

arXiv - QuantFin - Risk Management Pub Date : 2024-03-15 DOI: arxiv-2403.10652

Cecilia Ying, Stephen Thomas

引用次数: 0

Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning 通过具有课程学习功能的模态保全图神经网络进行金融违约预测

arXiv - QuantFin - Risk Management Pub Date : 2024-03-11 DOI: arxiv-2403.06482

Daixin Wang, Zhiqiang Zhang, Yeyu Zhao, Kai Huang, Yulin Kang, Jun Zhou

{"title":"Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning","authors":"Daixin Wang, Zhiqiang Zhang, Yeyu Zhao, Kai Huang, Yulin Kang, Jun Zhou","doi":"arxiv-2403.06482","DOIUrl":"https://doi.org/arxiv-2403.06482","url":null,"abstract":"User financial default prediction plays a critical role in credit risk\u0000forecasting and management. It aims at predicting the probability that the user\u0000will fail to make the repayments in the future. Previous methods mainly extract\u0000a set of user individual features regarding his own profiles and behaviors and\u0000build a binary-classification model to make default predictions. However, these\u0000methods cannot get satisfied results, especially for users with limited\u0000information. Although recent efforts suggest that default prediction can be\u0000improved by social relations, they fail to capture the higher-order topology\u0000structure at the level of small subgraph patterns. In this paper, we fill in\u0000this gap by proposing a motif-preserving Graph Neural Network with curriculum\u0000learning (MotifGNN) to jointly learn the lower-order structures from the\u0000original graph and higherorder structures from multi-view motif-based graphs\u0000for financial default prediction. Specifically, to solve the problem of weak\u0000connectivity in motif-based graphs, we design the motif-based gating mechanism.\u0000It utilizes the information learned from the original graph with good\u0000connectivity to strengthen the learning of the higher-order structure. And\u0000considering that the motif patterns of different samples are highly unbalanced,\u0000we propose a curriculum learning mechanism on the whole learning process to\u0000more focus on the samples with uncommon motif distributions. Extensive\u0000experiments on one public dataset and two industrial datasets all demonstrate\u0000the effectiveness of our proposed method.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"68-69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Geometrically Convex Risk Measures 论几何凸度风险度量

arXiv - QuantFin - Risk Management Pub Date : 2024-03-10 DOI: arxiv-2403.06188

Mücahit Aygün, Fabio Bellini, Roger J. A. Laeven

引用次数: 0

A machine learning workflow to address credit default prediction 解决信用违约预测问题的机器学习工作流程

arXiv - QuantFin - Risk Management Pub Date : 2024-03-06 DOI: arxiv-2403.03785

Rambod Rahmani, Marco Parola, Mario G. C. A. Cimino

{"title":"A machine learning workflow to address credit default prediction","authors":"Rambod Rahmani, Marco Parola, Mario G. C. A. Cimino","doi":"arxiv-2403.03785","DOIUrl":"https://doi.org/arxiv-2403.03785","url":null,"abstract":"Due to the recent increase in interest in Financial Technology (FinTech),\u0000applications like credit default prediction (CDP) are gaining significant\u0000industrial and academic attention. In this regard, CDP plays a crucial role in\u0000assessing the creditworthiness of individuals and businesses, enabling lenders\u0000to make informed decisions regarding loan approvals and risk management. In\u0000this paper, we propose a workflow-based approach to improve CDP, which refers\u0000to the task of assessing the probability that a borrower will default on his or\u0000her credit obligations. The workflow consists of multiple steps, each designed\u0000to leverage the strengths of different techniques featured in machine learning\u0000pipelines and, thus best solve the CDP task. We employ a comprehensive and\u0000systematic approach starting with data preprocessing using Weight of Evidence\u0000encoding, a technique that ensures in a single-shot data scaling by removing\u0000outliers, handling missing values, and making data uniform for models working\u0000with different data types. Next, we train several families of learning models,\u0000introducing ensemble techniques to build more robust models and hyperparameter\u0000optimization via multi-objective genetic algorithms to consider both predictive\u0000accuracy and financial aspects. Our research aims at contributing to the\u0000FinTech industry in providing a tool to move toward more accurate and reliable\u0000credit risk assessment, benefiting both lenders and borrowers.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140056777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0