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:: Volume 10, Issue 2 (2-2017) ::
JSS 2017, 10(2): 185-202 Back to browse issues page
Estimating Value-at-Risk and Average Value-at-Risk Measures Using Composite quantile Regression
Ali Aghamohammadi *, Mahdi Sojoudi
Abstract:   (9248 Views)

Value-at-Risk and Average Value-at-Risk are tow important risk measures based on statistical methoeds that used to measure the market's risk with quantity structure. Recently, linear regression models such as least squares and quantile methods are introduced to estimate these risk measures. In this paper, these two risk measures are estimated by using omposite quantile regression. To evaluate the performance of the proposed model with the other models, a simulation study was conducted and at the end, applications to real data set from Iran's stock market are illustarted.

Keywords: Value-at-risk, Average value-at-risk, Composite quantile regression, Statistical inference
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Type of Study: Research | Subject: Applied Statistics
Received: 2015/04/26 | Accepted: 2015/12/20 | Published: 2017/02/27
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Aghamohammadi A, Sojoudi M. Estimating Value-at-Risk and Average Value-at-Risk Measures Using Composite quantile Regression. JSS 2017; 10 (2) :185-202
URL: http://jss.irstat.ir/article-1-373-en.html

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Volume 10, Issue 2 (2-2017) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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