AbstractConventional VAR (Value at Risk) estimation includes historical simulation, variance/covariance, and the Monte Carlo simulation method. This study is the first to present a hybrid method of estimating VAR, combining ARMA and Neural Network. Empirical results demonstrate that the hybrid method obtained superior results to the conventional method in estimating VAR. In terms of accuracy, both the conventional and hybrid methods performed well when applied to the Chinese stock market, with the only poorly performing method being the HS method when applied to the Shanghai A share market. In terms of conservativeness, the hybrid method was superior to the conventional method, while in terms of efficiency, the hybrid method outperformed the conventional method when applied to the Shenzhen stock market. Thus, using hybrid Neural Network with the ARMA method to compare with the conventional method in estimating VAR offers certain advantages Consequently, this study suggests that investors use the hybrid method to estimate VAR.