As a newly emerged asset class, cryptocurrency is evidently more volatile
compared to the traditional equity markets. Due to its mostly unregulated
nature, and often low liquidity, the price of crypto assets can sustain a
significant change within minutes that in turn might result in considerable
losses. In this paper, we employ an approach for encoding market information
into images and making predictions of short-term realized volatility by
employing Convolutional Neural Networks. We then compare the performance of the
proposed encoding and corresponding model with other benchmark models. The
experimental results demonstrate that this representation of market data with a
Convolutional Neural Network as a predictive model has the potential to better
capture the market dynamics and a better volatility prediction.Comment: Third International Workshop on Modelling Uncertainty in the
Financial World (MUFin'23