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Risk measurement in Bitcoin market by fusing LSTM with the joint-regression-combined forecasting model
Kybernetes ; 52(4):1487-1502, 2023.
Article in English | ProQuest Central | ID: covidwho-2269829
ABSTRACT
PurposeThe 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/approachThe 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.FindingsEmpirical 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 implicationsThe 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/valueA 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.
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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Type of study: Prognostic study Language: English Journal: Kybernetes Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Type of study: Prognostic study Language: English Journal: Kybernetes Year: 2023 Document Type: Article