Prediction of default risk: An options-based approach applied to the Brazilian banking sector

被引:1
|
作者
Takami M.Y. [1 ]
Tabak B.M. [2 ,3 ]
机构
[1] Graduate Program in Economics, Universidade Catolica de Brasilia
[2] Banco Central Do Brasil, DEPEP, Brasilia, DF, 70074-900
关键词
banking; credit risk; default probability; early warning; emerging markets; financial regulation;
D O I
10.1057/jbr.2010.21
中图分类号
学科分类号
摘要
This article proposes a methodological framework to construct an early warning system for the Banking sector. It employs an options-based methodology to estimate default risk for six major Brazilian banks and shows that these measures have informational content. In addition, the options-based indicator is compared with market-based financial fragility indicators. Results show that these indicators are useful for risk managers and regulators, especially during crisis. Furthermore, option-based methods are preferable to classify banks. Finally, there is some evidence of lack of market discipline in the early 1990s. © 2011 Macmillan Publishers Ltd.
引用
收藏
页码:167 / 179
页数:12
相关论文
共 50 条
  • [41] Empirical Study on Firm Credit Risk Prediction Based on Default Distance
    Zhou, Hong
    Wang, Jingyi
    Qiu, Yilin
    [J]. EMERGING COMPUTATION AND INFORMATION TECHNOLOGIES FOR EDUCATION, 2012, 146 : 687 - +
  • [42] Measuring systemic risk in the global banking sector: A cross-quantilogram network approach
    Baumohl, Eduard
    Bouri, Elie
    Hoang, Thi-Hong-Van
    Shahzad, Syed Jawad Hussain
    Vyrost, Tomas
    [J]. ECONOMIC MODELLING, 2022, 109
  • [43] Construction of a financial default risk prediction model based on the LightGBM algorithm
    Gao, Bo
    Balyan, Vipin
    [J]. JOURNAL OF INTELLIGENT SYSTEMS, 2022, 31 (01) : 767 - 779
  • [44] A study on the prediction of electricity consumption considering the energy efficiency measures—applied in case of the Brazilian public sector
    Douglas Bortolassi Filgueiras
    Felipe Leite Coelho da Silva
    [J]. Energy Efficiency, 2023, 16
  • [45] AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector
    Kumar, Vikas
    Saheb, Shaiku Shahida
    Preeti
    Ghayas, Atif
    Kumari, Sunil
    Chandel, Jai Kishan
    Pandey, Saroj Kumar
    Kumar, Santosh
    [J]. BIG DATA MINING AND ANALYTICS, 2023, 6 (04) : 478 - 490
  • [46] Visual Cryptography and Image Processing Based Approach for Secure Transactions in Banking Sector
    Jain, Aaditya
    Soni, Sourabh
    [J]. 2017 2ND INTERNATIONAL CONFERENCE ON TELECOMMUNICATION AND NETWORKS (TEL-NET), 2017, : 301 - 305
  • [47] Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector
    Domingos, Edvaldo
    Ojeme, Blessing
    Daramola, Olawande
    [J]. COMPUTATION, 2021, 9 (03)
  • [48] Towards a Type-2 Fuzzy Logic Based System for Decision Support to Minimize Financial Default in Banking Sector
    Salih, Ahmed
    Hagras, Hani
    [J]. 2018 10TH COMPUTER SCIENCE AND ELECTRONIC ENGINEERING CONFERENCE (CEEC), 2018, : 46 - 49
  • [49] DEPICTING RISK PROFILE OVER TIME: A NOVEL MULTIPERIOD LOAN DEFAULT PREDICTION APPROACH
    Wang, Zhao
    Jiang, Cuiqing
    Zhao, Huimin
    [J]. MIS QUARTERLY, 2023, 47 (04) : 1455 - 1485
  • [50] SPILLOVER EFFECTS OF ASIAN BANKING SECTOR ON SYSTEMIC RISK IN THE INSURANCE SECTOR: BASED ON A DOUBLE-CoVaR MODEL
    Wang, Lizhen
    Hao, Yilin
    Jing, Zhongbo
    Zhang, Jiandi
    [J]. SINGAPORE ECONOMIC REVIEW, 2022,