Is Categorical Crossentropy Loss always bounded in the [0, 1] interval?
You can actually get values from $[0, \infty[$. Consider the cross entropy loss be following formula:

$loss (y_{pred}, y_{true}) = - \log{y_{pred, class_i}}$ 

, whereas the index $class_i$ states that you only use the output of the $i$th class. 

For example think that your true class is $dog$, which is represented by the $3$rd output neuron. On the $3$rd output neuron your network outputs a probability of $10\%$. Your loss is then $-\log{10\%}= 2.3$. Further if your probability is only $0.1\%$ your loss will equal $6.9$ and so on. 

Remember that:

$\lim\limits_{x \rightarrow 0}{\log{x}} = -\infty$