Constructing Risk Measurement Models by Quantile Regression Method

In order to use the states of price trends or historical volatility to interpret value-at-risk without distributional assumptions, quantile regression method is used to solve the problem. We present the risk measurement model using five lag returns as explanatory variables. To describe the relationship between risk and status we introduce the explanatory variables of price trend states into the model. To research the relationship between risk and volatility, we introduce the explanatory variable of historical volatility into quantile regression model. The results estimated by both the model and IGARCH model are compared. We find out that the states of price trends interpret effectively the relationship between value-at-risk and states. By using the historical volatility of 30 days as explanatory variable, the risk measurement model is more effective than IGARCH model.