| Περίληψη: | In this dissertation we forecast Bitcoin’s realized volatility using Blockchain information variables and machine learning methods. Based on the literature of Bitcoin, we used Blockchain information and the empirical results correspond
with the literature that Blockchain information can explain the variability of
Bitcoin’s price. We are trying to examine which variables are more important
and driving Bitcoin’s price which is consider highly volatile. Also for a better
prediction results we use machine learning that is considered by the literature
that results in a higher accuracy. In our analysis we use Random Forest machine learning algorithm for the prediction of realized volatility. From the empirical results we conclude that the trade_volume variable is the most important
variable included in the model. Also medium to high important variables are
transaction_fees_usd, cost_per_transaction_percent, miners_revenue and output_volume. In the other hand the less important variables are n_transaction
and n_transaction_per_block.
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