Risk measurement in Bitcoin market by fusing LSTM with the joint-regression-combined forecasting model
ISSN: 0368-492X
Article publication date: 24 December 2021
Issue publication date: 24 March 2023
Abstract
Purpose
The purpose of the paper is to better measure the risks and volatility of the Bitcoin market by using the proposed novel risk measurement model.
Design/methodology/approach
The joint regression analysis of value at risk (VaR) and expected shortfall (ES) can effectively overcome the non-elicitability problem of ES to better measure the risks and volatility of financial markets. And because of the incomparable advantages of the long- and short-term memory (LSTM) model in processing non-linear time series, the paper embeds LSTM into the joint regression combined forecasting framework of VaR and ES, constructs a joint regression combined forecasting model based on LSTM for jointly measuring VaR and ES, i.e. the LSTM-joint-combined (LSTM-J-C) model, and uses it to investigate the risks of the Bitcoin market.
Findings
Empirical results show that the proposed LSTM-J-C model can improve forecasting performance of VaR and ES in the Bitcoin market more effectively compared with the historical simulation, the GARCH model and the joint regression combined forecasting model.
Social implications
The proposed LSTM-J-C model can provide theoretical support and practical guidance to cryptocurrency market investors, policy makers and regulatory agencies for measuring and controlling cryptocurrency market risks.
Originality/value
A novel risk measurement model, namely LSTM-J-C model, is proposed to jointly estimate VaR and ES of Bitcoin. On the other hand, the proposed LSTM-J-C model provides risk managers more accurate forecasts of volatility in the Bitcoin market.
Keywords
Acknowledgements
The authors would like to thank the editor and two anonymous reviewers for the constructive comments, which helped to improve the manuscript. The authors are also very grateful for the financial support of the National Natural Science Foundation of China (No. 71701104), the MOE Project of Humanities and Social Sciences (No. 17YJC790102) and the Social Science Fund of Jiangsu Province (No. 20GLB008).
Citation
Lu, X., Liu, C., Lai, K.K. and Cui, H. (2023), "Risk measurement in Bitcoin market by fusing LSTM with the joint-regression-combined forecasting model", Kybernetes, Vol. 52 No. 4, pp. 1487-1502. https://doi.org/10.1108/K-07-2021-0620
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited