基于资产价格隐含信息的最优资产配置与风险管理

有效资产配置和风险管理的关键在于对未来市场的准确预测。半个多世纪以来，主流的资产配置和风险管理理论基于历史样本的“后向信息”以预测未来，隐含“过去代表未来”的不现实假设，导致理论模型在实践中的表现不尽人意。近年来资产价格中隐含的“前向信息”为我们预测未来开启了另一扇大门，提取资产价格隐含信息以改善投资表现成为当前研究的热点和前沿。本课题拟从资产价格中提取隐含的未来分布信息，建立基于“前向信息”的最优资产配置和风险管理模型。进而，运用最优化理论、智能算法和蒙特卡洛模拟等方法得到模型的最优解和性质，并分析隐含信息对投资决策和风险管理效率的影响。最后，将所得的模型和策略运用到我国金融市场的投资实践，以检验模型和策略的表现。课题研究成果在理论上可以丰富和深化资产配置和风险管理理论体系；在实践上可以改善投资者的投资策略，为企业和政府制定前瞻性风险管理或监管政策，防范和化解金融风险提供科学建议。

The key to effective asset allocation and risk management is the accurate forecast of asset prices. Over last half centuries, most studies use “backward information” in historical samples to forecast future, which suppose assets’ past performance can predict their future performance. However, this unrealistic assumption could cause theoretical models ineffective in practice. Recently the “forward information” implied in option prices has lit a torch for us to predict the future. Applications of implied information in investment strategies has become a frontier and hot topic. This project intends to extract implicit distribution information from option prices and thereby establish optimal asset allocation and risk management models. Furthermore, we take advantage of the optimization theory, intelligent algorithm and Monte Carlo simulation to obtain the optimal solution and properties of the model, and analyze the impact of implied information on investment decision-making and risk management efficiency. Finally, the resulting models and strategies are applied to the investment decision of Chinese financial markets. The research achievement of this project can enrich and deepen the theories of asset allocation and risk management; in practice, our works can improve investors’ trade strategies and provide forward-looking risk management or regulatory policy recommendations to prevent financial market risks for enterprises and governments.