一个新的不确定性增强的指数跟踪模型,下调的下降势头具有更高阶的时刻
Tingting Yang1,2, Xiaoxia Huang2, Kwon Ryong Hong2,3
1Wujinglian School of Economics, Changzhou University, Changzhou, 213164 China.
Soft computing
|June 26, 2023
概括
本研究引入了使用不确定性理论进行增强指数跟踪 (EIT) 的新模型,将股票回报视为不确定性. 它提出了一种元启发式算法,以尽量减少跟踪错误,为投资者提供了一种新的方法.
科学领域:
- 量化金融 量化金融
- 金融工程是金融工程.
- 决策科学 决策科学 决策科学
背景情况:
- 增强指数跟踪 (EIT) 问题旨在在尽量减少跟踪错误的同时优于基准指数.
- 传统的EIT模型通常假定随机的股票回报,这可能无法完全捕捉市场的不确定性.
- 投资者对风险的看法往往侧重于下行偏差,需要符合这一观点的风险措施.
研究的目的:
- 为了使用不确定性理论来解决EIT的问题,我们将库存回报模型作为不确定变量.
- 提出一种新的不确定的EIT模型,将高阶下行时刻作为追踪错误指标.
- 引入现实的约束,并分析拟议的不确定的EIT模型的特性.
主要方法:
- 基于不确定性理论开发一种新的不确定性增强指数跟踪模型.
- 利用高阶下行时刻来量化跟踪错误,提高模型适用性和风险调整.
- 制定模型作为非线性整数编程问题.
- 为解决复杂的优化问题提出了一种元启发式算法.
主要成果:
- 提出的不确定性EIT模型有效地纳入了跟踪错误测量的高阶下行时刻.
- 介绍了一个元启发式算法,并证明它在解决非线性整数编程问题上是有效的.
- 数字实验验证算法的效率和不确定的EIT模型的实际应用.
结论:
- 不确定性理论为解决增强的指数跟踪问题提供了一个强大的框架.
- 拟议的模型和算法为指数跟踪的投资组合选择提供了更现实的和适用的方法.
- 这些发现对金融工程和寻求在不确定性下优化投资组合业绩的投资策略有重大影响.
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