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

• Main Authors: Lensky, Artem, Hao, Mingyu
• Format: Journal Article
• Language: English
• Published: 04-04-2023
• Subjects: Computer Science - Learning; Quantitative Finance - Risk Management; Quantitative Finance - Trading and Microstructure
• DOI: 10.48550/arxiv.2304.02472

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 images are then used to train both a simple 3-layer
Convolutional Neural Network (CNN) and more advanced ResNet-18 and ConvMixer,
with additionally supplementing them with hand-crafted features. The models are
evaluated against classical GARCH, Multilayer Perceptron trained on raw data,
and a naive guess method that considers current volatility as a prediction.
Results: The experiments are conducted using price data from January 2021 and
evaluate model performance in terms of root mean square error (RMSPE). The
results show that our order flow representation with a CNN as a predictive
model achieves the best performance, with an RMSPE of 0.85+/-1.1 for the model
with aggregated features and 1.0+/-1.4 for the model without feature
supplementation. ConvMixer with feature supplementation follows closely. In
comparison, the RMSPE for the naive guess method was 1.4+/-3.0.