Determination of Default Probability by Loss Given Default

Mišanková, Mária; Spuchľáková, Erika; Michalíková, Katarína Frajtová

doi:10.1016/s2212-5671(15)00815-1

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…25 Non-litigation settlement is possible through preventive efforts, namely actions taken to anticipate the emergence of bad loans and early warning, namely an initial effort to deal with defaulting debtors' bad loans. 26 There are eight negative things listed in the standard clauses that business owners are not permitted to apply to consumers under article 18 paragraph 1 of Law Number 8 of 1999 concerning consumer protection, including: a. Declare the transfer of business actors' responsibility.…”

Section: Bad Loans Settlement Efforts At Federal International Finance Ltd Bandar Lampung Citymentioning

confidence: 99%

LOANS SETTLEMENT AT FEDERAL INTERNATIONAL FINANCE Ltd. (FIF) BANDAR LAMPUNG CITY

Hendriani

2021

cepalo

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There are many ways to purchase a vehicle, including a credit system. However, various problems arise in its application regarding the settlement of bad loans caused by default debtors. This study aims to analyse the efforts made by Federal International Finance Ltd. (FIF) Bandar Lampung City in resolving motor vehicle bad loans due to default debtors. This study uses empirical normative legal research methods and qualitative descriptive methods. The data is obtained from direct observations and interviews, then linked to legal regulations concerning the default problem. The results indicate that Federal International Finance Ltd. has an ideal solution to resolve bad Loans through litigation and non-litigation. Efforts to settle bad loans through non-litigation could be made by giving a subpoena to the debtor. Furthermore, the creditor could take litigation by filing a civil lawsuit against the debtor for default according to Article 1243 of the Civil Code.

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Section: Bad Loans Settlement Efforts At Federal International Finance Ltd Bandar Lampung Citymentioning

confidence: 99%

LOANS SETTLEMENT AT FEDERAL INTERNATIONAL FINANCE Ltd. (FIF) BANDAR LAMPUNG CITY

Hendriani

2021

cepalo

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“…Logistic regression is particularly useful when predicting binary outputs based on continuous independent variables. It should be noted that discrimination analysis may be more useful for small data, however logistic regression is more appropriate for larger samples [39]. However, even logit models have their weakness, which is the sensitivity to remote observation.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of the Results of a Data Envelopment Analysis Model and Logit Model in Assessing Business Financial Health

Horváthová

Mokrišová

2020

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This paper focuses on business financial health evaluation with the use of selected mathematical and statistical methods. The issue of financial health assessment and prediction of business failure is a widely discussed topic across various industries in Slovakia and abroad. The aim of this paper was to formulate a data envelopment analysis (DEA) model and to verify the estimation accuracy of this model in comparison with the logit model. The research was carried out on a sample of companies operating in the field of heat supply in Slovakia. For this sample of businesses, we selected appropriate financial indicators as determinants of bankruptcy. The indicators were selected using related empirical studies, a univariate logit model, and a correlation matrix. In this paper, we applied two main models: the BCC DEA model, processed in DEAFrontier software; and the logit model, processed in Statistica software. We compared the estimation accuracy of the constructed models using error type I and error type II. The main conclusion of the paper is that the DEA method is a suitable alternative in assessing the financial health of businesses from the analyzed sample. In contrast to the logit model, the results of this method are independent of any assumptions.

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“…Dengan demikian dapat disimpulkan bahwa variabel probability of default dan loss given default sebagai penentu variabel CKPN. Hasil ini senada dengan penelitian yang dilakukan oleh Misankova, Spuch, & Frajtova, (2015) dan Ali, et al (2015). Penelitian Misankova, Spuch, & Frajtova, (2015) menjelaskan bahwa CKPN merupakan risiko manajemen pembiayaan yang dapat ditentukan melalui besarnya kemungkinan mitra aktif gagal membayar pada saat jatuh tempo (probability of default) dan jumlah nilai aset yang gagal diakuisisi (loss given default).…”

Section: Diskusiunclassified

“…Pengetahuan terhadap probability of default dan loss given default sebagai penentu kebijakan CKPN merupakan peringatan dini bagi manajer bank mengurangi kegagalan memperoleh pendapatan yang berasal dari pembiayaan musyaraka. Penentuan probability of default dan loss given default merupakan cara efektif bagi manajemen bank untuk meningkatkan pendapatan pembiayaan (Misankova et al, 2015).…”

Section: Diskusiunclassified

Erratum: Kebijakan Loan Loss Provision pada Pembiayaan Mudharaba dan Dampaknya pada Kinerja Keuangan Bank Umum Syariah di Indonesia

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Determination of Default Probability by Loss Given Default

Cited by 12 publications

References 8 publications

LOANS SETTLEMENT AT FEDERAL INTERNATIONAL FINANCE Ltd. (FIF) BANDAR LAMPUNG CITY

LOANS SETTLEMENT AT FEDERAL INTERNATIONAL FINANCE Ltd. (FIF) BANDAR LAMPUNG CITY

Comparison of the Results of a Data Envelopment Analysis Model and Logit Model in Assessing Business Financial Health

Erratum: Kebijakan Loan Loss Provision pada Pembiayaan Mudharaba dan Dampaknya pada Kinerja Keuangan Bank Umum Syariah di Indonesia

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