Erratum to: Credit Risk Management: Pricing, Measurement, and Modeling

Witzany, Jiří

doi:10.1007/978-3-319-49800-3_7

Cited by 11 publications

(15 citation statements)

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“…The concept of empirical data modeling is appropriate for numerous applications in the field of computer science 17,18 . Empirical data modeling involves an induction procedure to create a model of the scheme from which it can derive responses of the system which are to be tried or observed.…”

Section: Background and Related Workmentioning

confidence: 99%

Comparative study of support vector machines and random forests machine learning algorithms on credit operation

et al. 2020

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Summary Corporate insolvency has significant adverse effects on an economy. With the number of multinationals increasing rapidly, corporate bankruptcy can severely disrupt the global financial environment. However, multinationals do not fail instantaneously; objective strategies combined with a rigorous analysis of both qualitative and quantifiable data can go a long way in identifying an organization's financial risks. Recent advancements in information and communication technologies have made data collection and storage an easy task. The challenge becomes mining the appropriate data about a company's financial risks and implementing it in forecasting a company's insolvency probabilities. In recent years, machine learning has been incorporated into big data analytics owing to its massive success in learning complex models. Machine learning algorithms such as Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks, Gaussian Processes, and Adaptive Learning have been used in the analysis of Big Data to predict the financial risks of companies. In this paper, credit scoring is explored with regards to data processed using the collateral as an independent variable. The obtained results indicate that RF algorithm is promising for use in credit risk management. This research shows the advantages of the RF approach over the SVM algorithm are its speed and operational simplicity, and SVM has the benefit of higher classification accuracy than RF. The paper compares the SVM and RF algorithms to forecast the recovered value in a credit task. The execution of the projected intelligent systems uses tests and algorithms for authentication of the projected model.

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Section: Background and Related Workmentioning

confidence: 99%

Comparative study of support vector machines and random forests machine learning algorithms on credit operation

et al. 2020

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“…In the case of Stage 2 lifetime ECL, banks need to estimate not only the probability of default over the fi rst year of the loan (PD 1 ) but also that over the second year of the loan (PD 2 ), etc. The lifetime ECL (see Witzany, 2017) can be expressed as:…”

Section: Ifrs 9 Impairment Requirementsmentioning

confidence: 99%

Impact of Implementation of IFRS 9 on Czech Banking Sector

Pastiranová¹,

Witzany²

2021

Prague Economic Papers

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The aim of this study is to provide an overview of the principles of IFRS 9 implementation and to analyse its impact on the Czech banking sector. Unlike the previous IAS 39 standard, valid until the end of 2017, the new accounting rules require banks to estimate forward-looking expected credit losses (ECL) while considering relevant exposure level information as well as available macroeconomic predictions. Due to the increased complexity of the ECL models and changing macroeconomic expectations, we hypothesize that the new standard leads to increased volatility of loan loss allowances. This hypothesis is empirically tested and more or less confirmed by an analysis of the quarterly flows of allowances for a sample of large Czech banks from the years 2016-2017 under IAS 39 and from 2018-2019 under IFRS 9.

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“…The variable can be either interpreted as the quantile-to-quantile transformed time-to-default variable or as the standardized asset return (see e.g. Vasicek 1987or Witzany 2017. Hence, the parameter representing the correlation between the default factors of different obligors can be called the asset return correlation.…”

Section: The Single-factor Default Modelmentioning

confidence: 99%

Stressing of Migration Matrices for IFRS 9 and ICAAP Calculations

Witzany

2020

SSRN Journal

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Its aim is to provide a platform for fast dissemination, discussion, and feedback on preliminary research results before submission to regular refereed journals. The papers are peer-reviewed, but are not edited or formatted by the editors. Disclaimer:The views expressed in documents served by this site do not reflect the views of the Faculty of Finance and Accounting or any other University of Economics Faculties and Departments. They are the sole property of the respective authors.

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Erratum to: Credit Risk Management: Pricing, Measurement, and Modeling

Cited by 11 publications

References 0 publications

Comparative study of support vector machines and random forests machine learning algorithms on credit operation

Comparative study of support vector machines and random forests machine learning algorithms on credit operation

Impact of Implementation of IFRS 9 on Czech Banking Sector

Stressing of Migration Matrices for IFRS 9 and ICAAP Calculations

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