Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach
Joint Authors
Syuhada, Khreshna
Nur’aini, Risti
Mahfudhotin, Risti
Source
Journal of Applied Mathematics
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-23
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Syuhada, Khreshna& Nur’aini, Risti& Mahfudhotin, Risti. 2020. Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach. Journal of Applied Mathematics،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1174586
Modern Language Association (MLA)
Syuhada, Khreshna…[et al.]. Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach. Journal of Applied Mathematics No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1174586
American Medical Association (AMA)
Syuhada, Khreshna& Nur’aini, Risti& Mahfudhotin, Risti. Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach. Journal of Applied Mathematics. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1174586
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1174586