Open AccessApplication of Machine Learning and CoVaR Model on Intelligent Decision Method
Author(s) -
Zheyu Yang,
Aozhi Dai
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1982/1/012043
Subject(s) - portfolio , efficient frontier , systemic risk , portfolio optimization , computer science , asset (computer security) , expected shortfall , asset allocation , econometrics , economics , finance , financial crisis , computer security , macroeconomics
The traditional asset allocation model does not take into account the spread of systemic risk in the process of portfolio optimization, which will lead to great losses in the portfolio when facing financial risks, especially extreme risks. In order to solve this problem, this paper improves the efficiency frontier of Markowitz, takes the factors that cause changes in the rate of return on individual underlying assets into the consideration of systemic risk, applies CoVaR model to measure the spread of systemic risk, and constructs a new asset allocation model based on Mean-CoVaR. The results show that when considering systemic risk shocks, the impact of systemic risk diffusion on Mean-CoVaR portfolio is significantly lower than that of traditional Mean-Variance portfolio. And Mean-CoVaR model is more efficient for portfolio allocation.