In the field of options trading, there is little research being done in option markets for cryptocurrencies such as Bitcoin. This is especially important when trading options given the volatile and non-mature nature of these cryptocurrency markets. In these environments, traditional option pricing methods such as the Black-Scholes model can lead to unreliable results. However, there is some research into using neural networks where data on European style options written in Bitcoin are collected and classical pricing models including the trinomial tree, Monte Carlo simulation and explicit finite difference method are used as input layers. Results show that this is a more precise approach for option pricing. My research attempts to further improve prediction precision by altering the model specifications. I achieve this by studying all the previously mentioned traditional option pricing techniques and introduce stochastic volatility models as input layers in the neural network. I additionally consider how environmental/societal variables can change the options market such that mathematical option pricing models could lose their effectiveness as a whole.