TY - JOUR
T1 - Portfolio constructions in cryptocurrency market
T2 - A CVaR-based deep reinforcement learning approach
AU - Cui, Tianxiang
AU - Ding, Shusheng
AU - Jin, Huan
AU - Zhang, Yongmin
N1 - Publisher Copyright:
© 2022
PY - 2023/2
Y1 - 2023/2
N2 - Cryptocurrency markets have much larger tail risk than traditional financial markets, and constructing portfolios with such large tail risk assets would be challenging. Therefore, cryptocurrency funds demand new superior risk management models and Conditional Value at Risk (CVaR) is a prevailing risk measure for constructing portfolios in stock markets with large tail risk. Consequently, our paper contributes to the literature by developing a new cryptocurrency portfolio model framework based on the CVaR risk measure and a deep reinforcement learning optimization framework. We use the data from cryptocurrency market starting 2015 to 2021, unfolding that CVaR measure with deep learning outperforms the traditional portfolio construction technique. Compared with traditional economic parameter-based portfolio models, our model free based approach can capture the nonlinear compounding effect of multiple risk shocks by deep reinforcement learning on the risk distribution with economic structural breakdown. It can guide investments in financial markets with high tail risks.
AB - Cryptocurrency markets have much larger tail risk than traditional financial markets, and constructing portfolios with such large tail risk assets would be challenging. Therefore, cryptocurrency funds demand new superior risk management models and Conditional Value at Risk (CVaR) is a prevailing risk measure for constructing portfolios in stock markets with large tail risk. Consequently, our paper contributes to the literature by developing a new cryptocurrency portfolio model framework based on the CVaR risk measure and a deep reinforcement learning optimization framework. We use the data from cryptocurrency market starting 2015 to 2021, unfolding that CVaR measure with deep learning outperforms the traditional portfolio construction technique. Compared with traditional economic parameter-based portfolio models, our model free based approach can capture the nonlinear compounding effect of multiple risk shocks by deep reinforcement learning on the risk distribution with economic structural breakdown. It can guide investments in financial markets with high tail risks.
KW - Cryptocurrency market
KW - Efficient frontier
KW - Portfolio optimization
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85142355960&partnerID=8YFLogxK
U2 - 10.1016/j.econmod.2022.106078
DO - 10.1016/j.econmod.2022.106078
M3 - Article
AN - SCOPUS:85142355960
SN - 0264-9993
VL - 119
JO - Economic Modelling
JF - Economic Modelling
M1 - 106078
ER -