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Credit risk management has become a must in this era due to the increase in the number of businesses defaulting. Building upon the legacy of Kealhofer, McQuown, and Vasicek (KMV), a mathematical model is introduced based on Merton model called KMV-Merton model to predict the credit risk of firms. The KMV-Merton model is commonly used in previous de...
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... is the weightage of the credit risk indicators i calculated using equation (3). Meanwhile is the score assigned as the default probability and dnancial ratios were estimated and then mapped into Tables 5 and 6. Tables 5 and 6 show the score given for the default probability and dnancial ratios. The max is the maximum score that can be obtained for the default probability and dnancial ratios as given in Tables 5 and 6. The scores given in Tables 5 and 6 were assigned by (Credit, 2014;Caracota et al., 2010) to indicate the strength of frms based on a certain ...Context 2
... using equation (3). Meanwhile is the score assigned as the default probability and dnancial ratios were estimated and then mapped into Tables 5 and 6. Tables 5 and 6 show the score given for the default probability and dnancial ratios. The max is the maximum score that can be obtained for the default probability and dnancial ratios as given in Tables 5 and 6. The scores given in Tables 5 and 6 were assigned by (Credit, 2014;Caracota et al., 2010) to indicate the strength of frms based on a certain level of credit risk. In this case, the worst score given is zero, while the excellent score can be varied from two to ten. ...Citations
... Wibowo (2017) in his research said that banks can use the Merton Model compared to Altman's Z score and the Ohlson Model because the Merton Model is widely recognised as a model that has a strong theoretical basis even though it has its own problems in its implementation because the variables it uses are unobservable. Some other researchers who use the KMV-Merton Model as the basis for calculating the probability of default include Bharath et al. (2004), (Chen & Chu, 2014), (Canh & Khoa, 2014), (Malasari et al., 2020) and (Yusof et al., 2021). ...
Pada masa pandemi COVID-19, ekonomi Indonesia mengalami pertumbuhan negatif 2,19 persen (2020). Kondisi ini mempengaruhi penurunan bisnis di Indonesia yang berdampak pada peningkatan Non Performing Loan perbankan. Umumnya, penilaian kredit oleh perbankan dilakukan menggunakan informasi yang terdapat pada laporan keuangan sehingga pencatatan akuntansi yang benar dapat mempengaruhi kualitas kredit. Penelitian ini bertujuan untuk mempelajari apakah manajemen laba (earnings management) yang dilakukan oleh perusahaan selama krisis pandemi COVID-19 memiliki pengaruh signifikan terhadap kemungkinan gagal bayar (probability of default), khususnya bagi perusahaan sektor non keuangan di Indonesia. Data penelitian diperoleh dari Bursa Efek Indonesia periode 2019 – 2021 dimana probability of default dihitung dengan menggunakan KMV-Merton Model dan earnings management menggunakan metode F-score Dechow. Hasil penelitian menunjukkan bahwa terdapat peningkatan jumlah perusahaan yang memiliki probabilitas default pada masa pandemi COVID-19, namun jumlah perusahaan yang terindikasi melakukan earnings management mengalami penurunan. Hasil uji menunjukan bahwa pengaruh earnings management terhadap probability of default tidak signifikan. Namun demikian, pihak yang berkepentingan harus dapat sedini mungkin mengidentifikasi adanya manajemen laba yang dapat berdampak buruk terhadap kualitas kredit dan mengantisipasi kemungkinan gagal bayar di kemudian hari. During the COVID-19 pandemic, the Indonesian economy experienced negative growth of 2.19 percent (2020). This condition affects the decline in business in Indonesia, which impacts the increase in bank Non-Performing Loans. Generally, credit assessment by banks is performed based on the information figured in financial statements and therefore the accounting records can affect credit quality. This research aims to study whether earnings management carried out by companies during the COVID-19 pandemic crisis has a significant effect on the probability of default, especially for non-financial sector companies in Indonesia. The data was obtained from the Indonesia Stock Exchange for the period 2019 – 2021. The probability of default was calculated using the KMV-Merton Model and earnings management using the F-score Dechow method. The results showed that there was an increase in the number of companies that had a probability of default during the COVID-19 pandemic, but but the number of companies that indicated earnings management decreased. The result showed that the effect of earnings management on the probability of default is not significant. However, interested parties must be able to identify earnings management as early as possible, which can have an adverse impact on credit quality and anticipate possible defaults in the future.
... The monitoring methods based on market information mainly include KMV model, VAR model, Credit Metrics model, and decision tree model. First, KMV model combines Merton option pricing principle and Black-Scholes formula and mainly considers the economic value of the target entity, the asset structure, and the environmental changes of the external market, to preliminarily evaluate the value system and asset health of the target entity and further evaluate the fluctuation of risk (Yusof et al. 2021). This model combines the risk and customer information and fully considers the dynamic characteristics of the target entity, improving the accuracy of credit risk monitoring, which is conducive to the dynamic monitoring of green credit risk. ...
Under the background of the new development pattern, the green credit market has ushered in a huge space for development. The monitoring and control of green credit risk is conducive to solving the negative impact of green credit risk and promoting the healthy development and smooth operation of the green credit market. This study first takes different provinces in China as samples; takes the data of government departments, financial institutions, enterprises, and other stakeholders as the monitoring content; and then uses the hybrid gray correlation degree-TOPSIS method to analyze. Next, this paper compares the green credit risk monitoring values of different regions at the same time and the same regions at different times. And it compares the green credit risk monitoring values of green finance pilot cities before and after the pilot. The research results show that there are obvious differences in eastern, central, and western regions in different time periods, and the green credit risk is also different in green finance pilot cities before and after the pilot. Stakeholders such as government departments, financial institutions, and enterprises need to take active measures to improve the efficiency of green credit risk management and achieve the sustainability of green credit development.
Global investors have been allocating resources for developing quantitative credit risk models for forecasting credit risk and estimating the cost associated with defaults in order to arrive at the credit derivatives which may handle the risks. In this chapter, the authors propose a hybrid Merton model for measuring credit risk. They estimate market volatility using an iterative annualized historical volatility approach and corporate asset value using the Merton model. For corporate assets, actual default probability and risk neutral probability are correlated. Monte Carlo simulation predictions of the real-time asset price of S&P global-listed Tesla Inc. support the approach. The derived book asset value is 0.44% and the simulated asset value is 0.43%. Model convergence is shown by the minimal difference between the past three iterations. The hybrid strategy to select risk neutral stock value captures volatility variance. Comparative analysis with real-time data confirms the approach's correctness.
