摘 要
金融工程作为现代金融学与数学、统计学及计算机科学的交叉学科，在投资组合优化领域展现出重要价值。本文以提升投资组合绩效和风险管理能力为目标，结合经典均值-方差模型与前沿金融工程技术，提出了一种基于动态风险调整的投资组合优化框架。研究通过引入条件风险价值（CVaR）和机器学习算法，构建了更加灵活且适应性强的优化模型，并利用历史市场数据对模型进行回测验证。结果表明，该方法能够有效平衡收益与风险，在不同市场环境下显著提高投资组合的稳健性与收益水平。本文的主要创新点在于将非线性优化技术与实时市场信息整合，实现了对传统静态模型的改进，同时为投资者提供了更具操作性的决策支持工具。研究表明，金融工程在复杂市场环境下的应用潜力巨大，其精准的风险度量与优化能力为现代投资管理提供了新的思路和方法论支持，对未来相关领域的理论发展与实践探索具有重要意义。
关键词：投资组合优化 金融工程 条件风险价值
Abstract
Financial engineering, as an interdisciplinary field combining modern finance, mathematics, statistics, and computer science, demonstrates significant value in the domain of portfolio optimization. This study aims to enhance portfolio performance and risk management capabilities by integrating the classical mean-variance model with advanced financial engineering techniques, proposing a dynamic risk-adjusted portfolio optimization fr amework. By incorporating Conditional Value-at-Risk (CVaR) and machine learning algorithms, a more flexible and adaptive optimization model is constructed, which is validated through backtesting using historical market data. The results indicate that this approach effectively balances return and risk, significantly improving the robustness and return levels of investment portfolios across various market conditions. The primary innovation of this study lies in the integration of nonlinear optimization techniques with real-time market information, thereby refining traditional static models and providing investors with more operational decision-support tools. The research highlights the substantial application potential of financial engineering in complex market environments, where its precise risk measurement and optimization capabilities offer new perspectives and methodological support for modern investment management, contributing significantly to the theoretical development and practical exploration of related fields in the future.
Keyword:Portfolio Optimization Financial Engineering Conditional Value At Risk
目 录
1绪论 1
1.1金融工程与投资组合优化的背景 1
1.2国内外研究现状分析 1
1.3研究方法与技术路线 1
2投资组合优化理论基础 2
2.1经典均值 2
2.2风险测度在优化中的作用 2
2.3现代投资组合理论扩展 3
3金融工程工具的应用实践 4
3.1衍生品在风险对冲中的应用 4
3.2蒙特卡洛模拟在优化中的实现 4
3.3机器学习算法的引入与改进 5
4实证分析与案例研究 5
4.1数据选取与实验设计 5
4.2模型构建与结果分析 6
4.3应用效果评估与讨论 6
结论 7
参考文献 8
致谢 9
金融工程作为现代金融学与数学、统计学及计算机科学的交叉学科，在投资组合优化领域展现出重要价值。本文以提升投资组合绩效和风险管理能力为目标，结合经典均值-方差模型与前沿金融工程技术，提出了一种基于动态风险调整的投资组合优化框架。研究通过引入条件风险价值（CVaR）和机器学习算法，构建了更加灵活且适应性强的优化模型，并利用历史市场数据对模型进行回测验证。结果表明，该方法能够有效平衡收益与风险，在不同市场环境下显著提高投资组合的稳健性与收益水平。本文的主要创新点在于将非线性优化技术与实时市场信息整合，实现了对传统静态模型的改进，同时为投资者提供了更具操作性的决策支持工具。研究表明，金融工程在复杂市场环境下的应用潜力巨大，其精准的风险度量与优化能力为现代投资管理提供了新的思路和方法论支持，对未来相关领域的理论发展与实践探索具有重要意义。
关键词：投资组合优化 金融工程 条件风险价值
Abstract
Financial engineering, as an interdisciplinary field combining modern finance, mathematics, statistics, and computer science, demonstrates significant value in the domain of portfolio optimization. This study aims to enhance portfolio performance and risk management capabilities by integrating the classical mean-variance model with advanced financial engineering techniques, proposing a dynamic risk-adjusted portfolio optimization fr amework. By incorporating Conditional Value-at-Risk (CVaR) and machine learning algorithms, a more flexible and adaptive optimization model is constructed, which is validated through backtesting using historical market data. The results indicate that this approach effectively balances return and risk, significantly improving the robustness and return levels of investment portfolios across various market conditions. The primary innovation of this study lies in the integration of nonlinear optimization techniques with real-time market information, thereby refining traditional static models and providing investors with more operational decision-support tools. The research highlights the substantial application potential of financial engineering in complex market environments, where its precise risk measurement and optimization capabilities offer new perspectives and methodological support for modern investment management, contributing significantly to the theoretical development and practical exploration of related fields in the future.
Keyword:Portfolio Optimization Financial Engineering Conditional Value At Risk
目 录
1绪论 1
1.1金融工程与投资组合优化的背景 1
1.2国内外研究现状分析 1
1.3研究方法与技术路线 1
2投资组合优化理论基础 2
2.1经典均值 2
2.2风险测度在优化中的作用 2
2.3现代投资组合理论扩展 3
3金融工程工具的应用实践 4
3.1衍生品在风险对冲中的应用 4
3.2蒙特卡洛模拟在优化中的实现 4
3.3机器学习算法的引入与改进 5
4实证分析与案例研究 5
4.1数据选取与实验设计 5
4.2模型构建与结果分析 6
4.3应用效果评估与讨论 6
结论 7
参考文献 8
致谢 9
