Estimating probabilities of default of different firms and the statistical tests

Abstract

The probability of default (PD) is the essential credit risks in the finance world. It provides an estimate of the likelihood that a borrower will be unable to meet its debt obligations. Purpose: This paper computes the probability of default (PD) of utilizing marketbased data which outlines their convenience for monetary reconnaissance. There are numerous models that provide assistance to analyze credit risks, for example, the probability of default, migration risk, and loss gain default. Every one of these models is vital for estimating credit risk, however, the most imperative model is PD, i.e., employed in this paper. Design/methodology/approach: In this paper, the Black-Scholes Model for European Call Option (BSM-CO) is utilized to gauge the PD of the Jammu and Kashmir Bank, Bank of Baroda, Indian Overseas Bank, and Canara Bank. The information has been taken from a term of 5 years on a yearly premise from 2012 to 2016. This paper demonstrates how d 2 in Black Scholes displayed help in assessing the PD of the various firms. Findings: The fundamental findings of this paper are whether there are any mean contrasts between the mean differences of PD between the organizations utilizing ANOVA and the Tukey strategy.

Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation

“…Merton's model for corporate default uses the vanilla options rationale to calculate the default probability. Papers from Tudela and Young [30]; Dar, Anuradha, and Qadir [31]; Afik, Arad and Galil [32] are examples of the use of this method.…”

Determinan Probability of Default Dalam Perhitungan Expected Credit Loss Perbankan

“…Penelitian sebelumnya mengenai PSAK 71 (IFRS 9), topik yang dibahas terutama mengenai Probability of Default (PD) seperti Dar et al (2019) serta Sugiarto dan Suroso (2020). Hal ini karena PSAK 71 bersifat principle based, sehingga perhitungan PD ini masih membuka peluang dalam pengembangan modelnya karena tidak ada standarisasi.…”

Automated credit assessment framework using ETL process and machine learning

“…Apart from that, other authors also have estimated different types of risk models introduced by Frydman et al [ 33 ], Li [ 41 ] and Shumway [ 51 ]. Broadly credit risk modeling is categorized into four groups [ 24 ]. The first group is based on ’Merton’ structural approach for valuing risky debt.…”

“…PD model [ 24 ] is a logistic regression model with a binary indicator for good or bad as a dependent variable and only dummy variables as independent variables. Logistic regression estimates the relationship between a dependent variable and independent variables.…”