One of the approaches to determining and quantifying the credit risk of a loan portfolio is by obtaining the distribution of losses of the portfolio and determining the risk quantities from such distributions. In this paper, we describe the challenges to using this approach and illustrate a practical solution where simulation methods are used to obtain loss distribution for a two obligor portfolio. This is then extended to ten and hundred obligor portfolios. Existing probability distributions with specified parameters are then used to approximate the loss distributions obtained. Using such parameters of the existing probability distributions, we obtain the risk quantities associated with the loan portfolio including Expected and Unexpected losses. We realized that depending on the confidence interval for which we measure the Unexpected Loss, Stress Losses are needed to account for the total loss of the portfolio
| Published in | Science Journal of Applied Mathematics and Statistics (Volume 2, Issue 2) |
| DOI | 10.11648/j.sjams.20140202.11 |
| Page(s) | 42-52 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
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Copyright © The Author(s), 2024. Published by Science Publishing Group |
Economic Capital, Expected Loss, Unexpected Loss, Obligor, Loss Given Default, Exposure at Default, Stress Loss
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APA Style
Osei Antwi. (2014). Measuring Portfolio Loss Using Approximation Methods. Science Journal of Applied Mathematics and Statistics, 2(2), 42-52. https://doi.org/10.11648/j.sjams.20140202.11
ACS Style
Osei Antwi. Measuring Portfolio Loss Using Approximation Methods. Sci. J. Appl. Math. Stat. 2014, 2(2), 42-52. doi: 10.11648/j.sjams.20140202.11
AMA Style
Osei Antwi. Measuring Portfolio Loss Using Approximation Methods. Sci J Appl Math Stat. 2014;2(2):42-52. doi: 10.11648/j.sjams.20140202.11
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Download@article{10.11648/j.sjams.20140202.11,
author = {Osei Antwi},
title = {Measuring Portfolio Loss Using Approximation Methods},
journal = {Science Journal of Applied Mathematics and Statistics},
volume = {2},
number = {2},
pages = {42-52},
doi = {10.11648/j.sjams.20140202.11},
url = {https://doi.org/10.11648/j.sjams.20140202.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20140202.11},
abstract = {One of the approaches to determining and quantifying the credit risk of a loan portfolio is by obtaining the distribution of losses of the portfolio and determining the risk quantities from such distributions. In this paper, we describe the challenges to using this approach and illustrate a practical solution where simulation methods are used to obtain loss distribution for a two obligor portfolio. This is then extended to ten and hundred obligor portfolios. Existing probability distributions with specified parameters are then used to approximate the loss distributions obtained. Using such parameters of the existing probability distributions, we obtain the risk quantities associated with the loan portfolio including Expected and Unexpected losses. We realized that depending on the confidence interval for which we measure the Unexpected Loss, Stress Losses are needed to account for the total loss of the portfolio},
year = {2014}
}
TY - JOUR T1 - Measuring Portfolio Loss Using Approximation Methods AU - Osei Antwi Y1 - 2014/04/20 PY - 2014 N1 - https://doi.org/10.11648/j.sjams.20140202.11 DO - 10.11648/j.sjams.20140202.11 T2 - Science Journal of Applied Mathematics and Statistics JF - Science Journal of Applied Mathematics and Statistics JO - Science Journal of Applied Mathematics and Statistics SP - 42 EP - 52 PB - Science Publishing Group SN - 2376-9513 UR - https://doi.org/10.11648/j.sjams.20140202.11 AB - One of the approaches to determining and quantifying the credit risk of a loan portfolio is by obtaining the distribution of losses of the portfolio and determining the risk quantities from such distributions. In this paper, we describe the challenges to using this approach and illustrate a practical solution where simulation methods are used to obtain loss distribution for a two obligor portfolio. This is then extended to ten and hundred obligor portfolios. Existing probability distributions with specified parameters are then used to approximate the loss distributions obtained. Using such parameters of the existing probability distributions, we obtain the risk quantities associated with the loan portfolio including Expected and Unexpected losses. We realized that depending on the confidence interval for which we measure the Unexpected Loss, Stress Losses are needed to account for the total loss of the portfolio VL - 2 IS - 2 ER -
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