AbstractCompared with current risks, future risks are more important for investment decisions and risk management. This paper modifies the Square Root of Time Rule with the boosting additive quantile regression model to forecast multi‐period horizon market risk. In J. P. Morgan's RiskMetrics Model, the k‐period horizon value at risk (VaR) equals to . Since its assumptions are too strict, expected capacity of Risk Metrics is not so well. Taylor relaxed assumptions of this model, used the GARCH model to replace the IGARCH model, and obtained multi‐period horizon VaR, which is a nonlinear function of the one‐step‐ahead volatility forecast .The conditional mean μt is zero in Taylor's model, but Tsay pointed out that this assumption (μt = 0) does not always hold. Therefore, we relax this assumption about the conditional mean, and obtain the VaR which is mixed function consisting of two parts, one is a linear function of conditional mean, and the other is a nonlinear function of , given the holding horizon k. For our mixed VaR function, we chose a more appropriate method, the boosting additive quantile regression model, to forecast multi‐period horizon VaR. Taking log‐returns of the Hang Seng Index from January 1, 2007 to November 1, 2016 as the sample, weforecast the 5‐, 10‐, 15‐, and 20‐day horizon VaRs, and compare the prediction accuracy of Morgan's model withour quantile regression model through likelihood ratio tests. Results show that VaR based on the quantile regression model is not only more accurate, but also sensitive to volatility, and is conducive to maintaining a reasonable risk reserve level for financial institutions, enabling them to pay less for regulation andachieve incentive compatibility.