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Calculating the Probability of Returning a Loan with Binary Probability Models

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  • Julian Vasilev

    (Varna University of Economics, Bulgaria)

Abstract

The purpose of this article is to give a new approach in calculating the probability of returning a loan. A lot of factors affect the value of the probability. In this article by using statistical and econometric models some influencing factors are proved. The main approach is concerned with applying probit and logit models in loan management institutions. A new aspect of the credit risk analysis is given. Calculating the probability of returning a loan is a difficult task. We assume that specific data fields concerning the contract (month of signing, year of signing, given sum) and data fields concerning the borrower of the loan (month of birth, year of birth (age), gender, region, where he/she lives) may be independent variables in a binary logistics model with a dependent variable “the probability of returning a loan”. It is proved that the month of signing a contract, the year of signing a contract, the gender and the age of the loan owner do not affect the probability of returning a loan. It is proved that the probability of returning a loan depends on the sum of contract, the remoteness of the loan owner and the month of birth. The probability of returning a loan increases with the increase of the given sum, decreases with the proximity of the customer, increases for people born in the beginning of the year and decreases for people born at the end of the year.

Suggested Citation

  • Julian Vasilev, 2014. "Calculating the Probability of Returning a Loan with Binary Probability Models," Romanian Statistical Review, Romanian Statistical Review, vol. 62(4), pages 55-71, December.
    • Handle: RePEc:rsr:journl:v:62:y:2014:i:4:p:55-71
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    References listed on IDEAS

    as
    1. Dilek Teker & Aynur Pala & Oya Kent, 2013. "Determination of Sovereign Rating: Factor Based Ordered Probit Models for Panel Data Analysis Modelling Framework," International Journal of Economics and Financial Issues, Econjournals, vol. 3(1), pages 122-132.
    2. Selen CAKMAKYAPAN & Atilla GOKTAS, 2013. "A Comparison Of Binary Logit And Probit Models With A Simulation Study," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 2(1), pages 1-17, JULY.
    3. Wongnaa, C. A. & Awunyo-Vitor, D., 2013. "Factors Affecting Loan Repayment Performance Among Yam Farmers in the Sene District, Ghana," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 5(2), pages 1-12, June.
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    Cited by:

    1. Vasilev Julian A., 2015. "Duration Models in Loan Management," Folia Oeconomica Stetinensia, Sciendo, vol. 15(1), pages 114-126, June.

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    More about this item

    Keywords

    credit risk analysis; econometrics; logit model; probability models; probit model; SPSS;
    All these keywords.

    JEL classification:

    • C - Mathematical and Quantitative Methods

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