For any continuous distribution we can define $$VaR_{\alpha}=-F^{-1}(1-\alpha)$$ where $F^{-1}$ is the inverse of the CDF. Now suppose that you have a distribution which comes from a location-scale family with location parameter $\mu$ and scale parameter $\sigma$ then $$F^{-1}(1-\alpha)=\mu+\sigma \phi^{-1}(1-\alpha)$$ where $\phi^{-1}$ is the inverse CDF of the distribution with $\mu=0$ and $\sigma=1$. Thus we have $$VaR_{\alpha}=-(\mu+\sigma \phi^{-1}(1-\alpha))$$ for any location-scale distribution. In particular for the student-t $$VaR_{\alpha}=-(\mu+\sigma \;t_v^{-1}(1-\alpha))$$ where t_v^{-1} is the inverse CDF of the standard student-t with $v$ degrees of freedom.

For any continuous distribution we can define $$VaR_{\alpha}=-F^{-1}(1-\alpha)$$ where $F^{-1}$ is the inverse of the CDF. Now suppose that you have a distribution which comes from a location-scale family with location parameter $\mu$ and scale parameter $\sigma$ then $$F^{-1}(1-\alpha)=\mu+\sigma \phi^{-1}(1-\alpha)$$ where $\phi^{-1}$ is the inverse CDF of the distribution with $\mu=0$ and $\sigma=1$. Thus we have $$VaR_{\alpha}=-(\mu+\sigma \phi^{-1}(1-\alpha))$$ for any location-scale distribution. In particular for the student-t $$VaR_{\alpha}=-(\mu+\sigma \;t_v^{-1}(1-\alpha))$$ where $t_v^{-1}$ is the inverse CDF of the standard student-t with $v$ degrees of freedom.