Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach
A Value-at-Risk (VaR) forecast may be calculated for the case of a random loss alone and/or of a random loss that depends on another random loss. In both cases, the VaR forecast is obtained by employing its (conditional) probability distribution of loss data, specifically the quantile of loss distri...
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| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2020/8276019 |
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| author | Khreshna Syuhada Risti Nur’aini Mahfudhotin |
| author_facet | Khreshna Syuhada Risti Nur’aini Mahfudhotin |
| author_sort | Khreshna Syuhada |
| collection | DOAJ |
| description | A Value-at-Risk (VaR) forecast may be calculated for the case of a random loss alone and/or of a random loss that depends on another random loss. In both cases, the VaR forecast is obtained by employing its (conditional) probability distribution of loss data, specifically the quantile of loss distribution. In practice, we have an estimative VaR forecast in which the distribution parameter vector is replaced by its estimator. In this paper, the quantile-based estimative VaR forecast for dependent random losses is explored through a simulation approach. It is found that the estimative VaR forecast is more accurate when a copula is employed. Furthermore, the stronger the dependence of a random loss to the target loss, in linear correlation, the larger/smaller the conditional mean/variance. In any dependence measure, generally, stronger and negative dependence gives a higher forecast. When there is a tail dependence, the use of upper and lower tail dependence provides a better forecast instead of the single correlation coefficient. |
| format | Article |
| id | doaj-art-b8d4eac34f9541e1a2b52bfa8a4d0c22 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-b8d4eac34f9541e1a2b52bfa8a4d0c222025-02-03T01:24:57ZengWileyJournal of Applied Mathematics1110-757X1687-00422020-01-01202010.1155/2020/82760198276019Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation ApproachKhreshna Syuhada0Risti Nur’aini1Mahfudhotin2Statistics Research Division, Institut Teknologi Bandung, Jalan Ganesa 10, Bandung 40132, IndonesiaStatistics Research Division, Institut Teknologi Bandung, Jalan Ganesa 10, Bandung 40132, IndonesiaStatistics Research Division, Institut Teknologi Bandung, Jalan Ganesa 10, Bandung 40132, IndonesiaA Value-at-Risk (VaR) forecast may be calculated for the case of a random loss alone and/or of a random loss that depends on another random loss. In both cases, the VaR forecast is obtained by employing its (conditional) probability distribution of loss data, specifically the quantile of loss distribution. In practice, we have an estimative VaR forecast in which the distribution parameter vector is replaced by its estimator. In this paper, the quantile-based estimative VaR forecast for dependent random losses is explored through a simulation approach. It is found that the estimative VaR forecast is more accurate when a copula is employed. Furthermore, the stronger the dependence of a random loss to the target loss, in linear correlation, the larger/smaller the conditional mean/variance. In any dependence measure, generally, stronger and negative dependence gives a higher forecast. When there is a tail dependence, the use of upper and lower tail dependence provides a better forecast instead of the single correlation coefficient.http://dx.doi.org/10.1155/2020/8276019 |
| spellingShingle | Khreshna Syuhada Risti Nur’aini Mahfudhotin Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach Journal of Applied Mathematics |
| title | Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach |
| title_full | Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach |
| title_fullStr | Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach |
| title_full_unstemmed | Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach |
| title_short | Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach |
| title_sort | quantile based estimative var forecast and dependence measure a simulation approach |
| url | http://dx.doi.org/10.1155/2020/8276019 |
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