Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock Risk
Background: Stock investment has been gaining momentum in the past years due to the development of technology. During the pandemic lockdown, people have invested more. One the one hand, stock investment has high potential profitability, but on the other, it is equally risky. Therefore, a value at ri...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universitas Airlangga
2021-04-01
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| Series: | Journal of Information Systems Engineering and Business Intelligence |
| Online Access: | https://e-journal.unair.ac.id/JISEBI/article/view/22762 |
| _version_ | 1811159682074542080 |
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| author | Brina Miftahurrohmah Catur Wulandari Yogantara Setya Dharmawan |
| author_facet | Brina Miftahurrohmah Catur Wulandari Yogantara Setya Dharmawan |
| author_sort | Brina Miftahurrohmah |
| collection | DOAJ |
| description | Background: Stock investment has been gaining momentum in the past years due to the development of technology. During the pandemic lockdown, people have invested more. One the one hand, stock investment has high potential profitability, but on the other, it is equally risky. Therefore, a value at risk (VaR) analysis is needed. One approach to calculate VaR is by using the Bayesian mixture model, which has been proven to be able to overcome heavy-tailed cases. Then, the VaR’s accuracy needs to be tested, and one of the ways is by using backtesting, such as the Kupiec test.
Objective: This study aims to determine the VaR model of PT NFC Indonesia Tbk (NFCX) return data using Bayesian mixture modelling and backtesting. On a practical level, this study can provide information about the potential risks of investing that is grounded in empirical evidence.
Methods: The data used was NFCX data retrieved from Yahoo Finance, which was then modelled with a mixture model based on the normal and Laplace distributions. After that, the VaR accuracy was calculated and then tested by using backtesting.
Results: The test results showed that the VaR with the mixture Laplace autoregressive (MLAR) approach (2;[2],[4]) was accurate at 5% and 1% quantiles while mixture normal autoregressive MNAR (2;[2],[2,4]) was only accurate at 5% quantiles.
Conclusion: The better performing NFCX VaR model for this study based on backtesting using Kupiec test is MLAR(2;[2],[4]). |
| first_indexed | 2024-04-10T05:45:17Z |
| format | Article |
| id | doaj.art-62d997d7757e4d1fbda0554e5af5ba6b |
| institution | Directory Open Access Journal |
| issn | 2598-6333 2443-2555 |
| language | English |
| last_indexed | 2024-04-10T05:45:17Z |
| publishDate | 2021-04-01 |
| publisher | Universitas Airlangga |
| record_format | Article |
| series | Journal of Information Systems Engineering and Business Intelligence |
| spelling | doaj.art-62d997d7757e4d1fbda0554e5af5ba6b2023-03-06T02:56:32ZengUniversitas AirlanggaJournal of Information Systems Engineering and Business Intelligence2598-63332443-25552021-04-0171112110.20473/jisebi.7.1.11-2118620Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock RiskBrina Miftahurrohmah0Catur Wulandari1Yogantara Setya Dharmawan2Universitas Internasional Semen IndonesiaUniversitas Internasional Semen IndonesiaUniversitas Internasional Semen IndonesiaBackground: Stock investment has been gaining momentum in the past years due to the development of technology. During the pandemic lockdown, people have invested more. One the one hand, stock investment has high potential profitability, but on the other, it is equally risky. Therefore, a value at risk (VaR) analysis is needed. One approach to calculate VaR is by using the Bayesian mixture model, which has been proven to be able to overcome heavy-tailed cases. Then, the VaR’s accuracy needs to be tested, and one of the ways is by using backtesting, such as the Kupiec test. Objective: This study aims to determine the VaR model of PT NFC Indonesia Tbk (NFCX) return data using Bayesian mixture modelling and backtesting. On a practical level, this study can provide information about the potential risks of investing that is grounded in empirical evidence. Methods: The data used was NFCX data retrieved from Yahoo Finance, which was then modelled with a mixture model based on the normal and Laplace distributions. After that, the VaR accuracy was calculated and then tested by using backtesting. Results: The test results showed that the VaR with the mixture Laplace autoregressive (MLAR) approach (2;[2],[4]) was accurate at 5% and 1% quantiles while mixture normal autoregressive MNAR (2;[2],[2,4]) was only accurate at 5% quantiles. Conclusion: The better performing NFCX VaR model for this study based on backtesting using Kupiec test is MLAR(2;[2],[4]).https://e-journal.unair.ac.id/JISEBI/article/view/22762 |
| spellingShingle | Brina Miftahurrohmah Catur Wulandari Yogantara Setya Dharmawan Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock Risk Journal of Information Systems Engineering and Business Intelligence |
| title | Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock Risk |
| title_full | Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock Risk |
| title_fullStr | Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock Risk |
| title_full_unstemmed | Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock Risk |
| title_short | Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock Risk |
| title_sort | investment modelling using value at risk bayesian mixture modelling approach and backtesting to assess stock risk |
| url | https://e-journal.unair.ac.id/JISEBI/article/view/22762 |
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