Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders
This work addresses volatility prediction for Bitcoin traders, but it appears incremental as it applies existing methods to new data without claiming major breakthroughs.
The paper tackled the problem of short-term Bitcoin price volatility forecasting using realized volatility and order data from 2016-2017, evaluating various statistical and machine learning methods without reporting specific numerical results.
In this paper, we study the ability to make the short-term prediction of the exchange price fluctuations towards the United States dollar for the Bitcoin market. We use the data of realized volatility collected from one of the largest Bitcoin digital trading offices in 2016 and 2017 as well as order information. Experiments are performed to evaluate a variety of statistical and machine learning approaches.