Learning to Predict Short-Term Volatility with Order Flow Image Representation

arXiv:2304.02472v2 Announce Type: replace-cross
Abstract: Introduction: The paper addresses the challenging problem of predicting the short-term realized volatility of the Bitcoin price using order flow information. The inherent stochastic nature and anti-persistence of price pose difficulties in accurate prediction.
Methods: To address this, we propose a method that transforms order flow data over a fixed time interval (snapshots) into images. The order flow includes trade sizes, trade directions, and limit order book, and is mapped into image colour channels. These …

abstract arxiv bitcoin cs.lg flow image information introduction nature paper persistence prediction price q-fin.rm q-fin.tr representation stochastic type