This paper extends the univariate two-sided Weibull distribution to a multivariate case for portfolio-value-at-risk estimation. This method allows to capture the stylized facts of the time series of cryptocurrencies, such as extreme volatility, volatility clustering, very heavy tails, and skewness. This new portfolio risk model is applied to a cryptocurrency portfolio consisting of four major coins: Bitcoin, Litecoin, Ripple, and Dash. The predictive performance of the proposed model is compared with several widely used models. We find that the portfolio value-at-risk with two-sided Weibull distribution outperforms the other models.