Many financial time series show heavy tail behavior. Such tail characteristic is important for risk management. In this research, we focus on the calculation of Value-at-Risk (VaR) for portfolios of financial assets. We consider nonlinear autoregressive models with heavy tail innovations to model the return. Predictive distribution of the return are used to compute the VaR of the portfolios of financial assets. Examples are also given to compare the VaR computed by our approach with those by other methods.