RT - Journal Article T1 - Estimating Value-at-Risk and Average Value-at-Risk Measures Using Composite quantile Regression JF - JSS YR - 2017 JO - JSS VO - 10 IS - 2 UR - http://jss.irstat.ir/article-1-373-en.html SP - 185 EP - 202 K1 - Value-at-risk K1 - Average value-at-risk K1 - Composite quantile regression K1 - Statistical inference AB - 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. LA eng UL http://jss.irstat.ir/article-1-373-en.html M3 10.18869/acadpub.jss.10.2.185 ER -