文件夹
投资组合优化
计量经济学
夏普比率
经济
库存(枪支)
交易成本
股票市场
黑色-垃圾模型
金融经济学
现代投资组合理论
复制投资组合
微观经济学
工程类
机械工程
古生物学
马
生物
作者
Yunlin Wu,Lei Huang,Hui Jiang
出处
期刊:Energy
[Elsevier]
日期:2023-12-01
卷期号:285: 129456-129456
被引量:1
标识
DOI:10.1016/j.energy.2023.129456
摘要
Investment in the fast-growing new-energy of China has gradually attracted great attention. At present, more than 100 new-energy stocks are listed on China’s stock markets. How to effectively manage the investment risk of these stocks and achieve better returns is of great interest to investors and policymakers. In finance, mean–variance portfolio (MVP) is often applied to solve portfolio allocation problems. However, when investing in numerous stocks, the classical MVP is no longer applicable. For large portfolio allocation, this study constructs a comprehensive MVP model, called an unconstrained regression model with latent factors (URELAF). It applies a factor structure with latent factors to modify the covariance matrix estimation in the classical MVP, using an unconstrained penalized regression to estimate the allocation. In addition, to more realistically simulate the investment of stock markets, transaction costs are considered. The empirical results show that URELAF model can control the risk with Sharpe ratio reaching 6 times as many as MVP model, and even improve about 26% compared with the sub-optimal portfolio model. URELAF also outperforms other portfolios with transaction costs. Finally, hypothesis tests further verify that URELAF can significantly improve portfolio performance compared with other portfolio methods for China’s new-energy market.
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