Title: High probability error bounds of SGD in unbounded domain. Abstract: This paper studies the high probability convergence behaviour of the stochastic gradient descent (SGD) method applied to convex problems. The existing tail-bound analysis of SGD relies crucially on assuming the domain of the problem to be bounded. In this work, we show that the bounded domain assumption can be removed for free. That is, we prove SGD in an unbounded domain enjoys the same high probability error bound as the bound established in the bounded domain; SGD converges with rate $O(\log(1/\delta)/\epsilon^2)$ no matter the problem domain is bounded or not. As a by-product, we also prove that the trajectory of SGD is guaranteed to stay in a neighbourhood of the initialization with almost bounded diameter. As simple extensions of our analysis, we further establish the high probability error bounds of the last iterate of SGD and SGD with momentum, respectively.