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The risk premium of a policy is the sum of the pure premium and the risk loading. In the classification ratemaking process, generalized linear models are usually used to calculate pure premiums, and various premium principles are applied to derive the risk loadings. No matter which premium principle is used, some risk loading parameters should be given in advance subjectively. To overcome this subjective problem and calculate the risk premium more reasonably and objectively, we propose a top-down method to calculate these risk loading parameters. First, we implement the bootstrap method to calculate the total risk premium of the portfolio. Then, under the constraint that the portfolio's total risk premium should equal the sum of the risk premiums of each policy, the risk loading parameters are determined. During this process, besides using generalized linear models, three kinds of quantile regression models are also applied, namely, traditional quantile regression model, fully parametric quantile regression model, and quantile regression model with coefficient functions. The empirical result shows that the risk premiums calculated by the method proposed in this study can reasonably differentiate the heterogeneity of different risk classes.