Bayesian portfolio selection using VaR and CVaR
作者：
Highlights：
• Optimal portfolio allocation problem is considered from a Bayesian perspective.
• Value at risk (VaR) and conditional value at risk (CVaR) are used as risk measures.
• VaR and CVaR are computed by applying the posterior predictive distribution.
• Optimal portfolio weights are expressed in terms of observed data only.
• Bayesian approach outperforms the conventional one at predicting the out-of-sample VaR.
摘要
•Optimal portfolio allocation problem is considered from a Bayesian perspective.•Value at risk (VaR) and conditional value at risk (CVaR) are used as risk measures.•VaR and CVaR are computed by applying the posterior predictive distribution.•Optimal portfolio weights are expressed in terms of observed data only.•Bayesian approach outperforms the conventional one at predicting the out-of-sample VaR.
论文关键词：Bayesian inference,Posterior predictive distribution,Optimal portfolio,VaR,CVaR
论文评审过程：Received 22 October 2021, Revised 26 January 2022, Accepted 22 March 2022, Available online 19 April 2022, Version of Record 19 April 2022.
论文官网地址：https://doi.org/10.1016/j.amc.2022.127120
