Default risk is a risk that firms have to bear whenever they failed to meet their debt obligation as the debt matured. Providing an assessment to predict default risk is one of the ways to mitigate the risk. However, default risk tends to be overestimated or underestimated if the asset values are not calculated accurately. This can be the reason to the public misconception towards the financial position of the firm. Accordingly, this paper presents the structured way of calculating the iterated market value of asset and its volatility of a firm based on Merton’s approach. The iterated market value of asset and its volatility are used to predict default risk of a firm using Merton-KMV model. The iteration procedure is done until the values are found converged up to 10−3. A sample of data of PN17 Company is used to run the procedures. As a result, we found that the market value of assets and its volatility mostly reached their convergence at the third iterations with the value of volatility of 114.30%. Meanwhile, the probability of default of the PN17 Company is found to be converged at the second iteration with the value of 0.0276%. In this case, low default risk is predicted.
Skip Nav Destination
Article navigation
2 October 2018
PROCEEDING OF THE INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2018 (ICoMEIA 2018)
24–26 July 2018
Kuala Lumpur, Malaysia
Research Article|
October 02 2018
An iterated Merton-KMV based approach of default risk prediction
N. M. Yusof;
N. M. Yusof
a)
1
Mathematics Department, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA
, Cawangan Negeri Sembilan, Kampus Seremban 3, Persiaran Seremban Tiga/1, Sremban 3, 70300 Seremban, Negeri Sembilan, Malaysia
a)Corresponding author: [email protected]
Search for other works by this author on:
S. N. H. Alias;
S. N. H. Alias
1
Mathematics Department, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA
, Cawangan Negeri Sembilan, Kampus Seremban 3, Persiaran Seremban Tiga/1, Sremban 3, 70300 Seremban, Negeri Sembilan, Malaysia
Search for other works by this author on:
N. A. Rosli;
N. A. Rosli
1
Mathematics Department, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA
, Cawangan Negeri Sembilan, Kampus Seremban 3, Persiaran Seremban Tiga/1, Sremban 3, 70300 Seremban, Negeri Sembilan, Malaysia
Search for other works by this author on:
W. N. A. Wan Rosna;
W. N. A. Wan Rosna
1
Mathematics Department, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA
, Cawangan Negeri Sembilan, Kampus Seremban 3, Persiaran Seremban Tiga/1, Sremban 3, 70300 Seremban, Negeri Sembilan, Malaysia
Search for other works by this author on:
M. L. Sapini
M. L. Sapini
b)
1
Mathematics Department, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA
, Cawangan Negeri Sembilan, Kampus Seremban 3, Persiaran Seremban Tiga/1, Sremban 3, 70300 Seremban, Negeri Sembilan, Malaysia
Search for other works by this author on:
a)Corresponding author: [email protected]
AIP Conf. Proc. 2013, 020021 (2018)
Citation
N. M. Yusof, S. N. H. Alias, N. A. Rosli, W. N. A. Wan Rosna, M. L. Sapini; An iterated Merton-KMV based approach of default risk prediction. AIP Conf. Proc. 2 October 2018; 2013 (1): 020021. https://doi.org/10.1063/1.5054220
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
Inkjet- and flextrail-printing of silicon polymer-based inks for local passivating contacts
Zohreh Kiaee, Andreas Lösel, et al.
Effect of coupling agent type on the self-cleaning and anti-reflective behaviour of advance nanocoating for PV panels application
Taha Tareq Mohammed, Hadia Kadhim Judran, et al.
Design of a 100 MW solar power plant on wetland in Bangladesh
Apu Kowsar, Sumon Chandra Debnath, et al.
Related Content
Developing a java android application of KMV-Merton default rate model
AIP Conference Proceedings (November 2017)
A proposed java algorithm for default-recovery rate model
AIP Conf. Proc. (August 2019)
Consumer default risk assessment in a banking institution
AIP Conference Proceedings (December 2016)
Comparing the performance of random forest with decision tree and logistic regression algorithm in loan default prediction
AIP Conf. Proc. (May 2024)
Issues in the Determination of Default POD for Hard‐Alpha Inclusions in Titanium Rotating Components for Aircraft Engines
AIP Conference Proceedings (February 2004)