Would averaging two vectors in word embeddings make sense?
Averaging embeddings vectors could make sense if your aim is to represent a sentence or document with a unique vector. For words out of vocabulary it make more sense to just use a random initialisation and allow training of the embedding parameters during the training of the model. In this way the model will learn the representation for the out-of-vocabulary words by itself. 

Alternatively, you could use external resources like WordNet [1] to extract a set of synonyms and other words closely related to a specific term, and then leverage the vectors of those close words (averaging them might have sense but it's always a matter of testing and see what happens, as far as I know there are no grounded rules established yet). 

[1] https://wordnet.princeton.edu