Optimal Asset Allocation for a Mean-Variance-CVaR Insurer under Regulatory Constraints

Abstract

In this paper, we introduce the mean-variance-CVaR criteria into the study of asset allocation for insurers. Considering that the financial market consists of one risk-free asset and multiple risky assets with regulatory constraints, an optimization problem is established for an insurer with underwriting business. Based on practical financial and insurance data, an empirical study is carried out. The results show that the mean-variance-CVaR model is able to provide more potential investment strategies for an insurer. The regulatory policy released by China Insurance Regulatory Commission plays a key role in controlling investment risk for Chinese insurers.

Portfolio Research Based on Mean-Realized Variance-CVaR and Random Matrix Theory under High-Frequency Data

“…Further, Li et al (2012) and Yu & Ma (2014) used this model to study China's foreign exchange reserves and sovereign fund investment respectively. Gao et al (2016) extended it to dynamic situations in financial market and Shi et al (2019) considered optimal investment and reinsurance problem in continuous time. However, the data in above literatures is low-frequency data, and the situation of high-frequency data is ready to be explored.…”

Stable Portfolio Selection Strategy for Mean-Variance-CVaR Model under High-Dimensional Scenarios

“…Additionally, the personal preference between variance and CVaR of the decision-maker can be considered. Gao et al [13] extend it to dynamic scenario in financial field and Shi et al [14] give the discussion in insurance investment under regulatory constraints. However, the study of the stability of optimal strategy's out-of-sample performance is ready to explore under mean-variance-CVaR criterion.…”