面向互联网金融服务平台的结构性理财产品风险测度与集成

从交易环境变化和行为偏差共同作用的视角，探索基于中国金融市场“情境”与投资者心理特点的结构性理财产品市场参与者行为特征及其演化规律，并将这种特征与规律融入结构性理财产品风险测度，研究面向互联网金融服务平台的结构性理财产品风险测度与集成、最优投资策略以及对金融系统性风险、行业创新模式的影响；分析获取结构性理财产品的一些重要情绪参数、市场情绪初值以及阈值，最终构建面向互联网金融服务平台的结构性理财产品风险测度与集成新理论、新方法和应用框架体系，丰富和深化人们对于结构性产品的认识，推进基于大数据的金融产品创新、市场监管以及系统性风险管理研究。这个分析过程也将有助于人们针对互联网金融监管以及科学地量化与管理金融风险问题总结出一套符合中国市场特色的评价标准和方法，为检验交易环境变化对金融市场的冲击、相关经济制度安排的模拟和仿真，以及经济政策作用效果判断提供可以参照的对象和标准。

In view of the circumstances that characterize China’s financial market, the origin of these circumstances, and the psychological characteristics of investors, this project explores the behavioral traits of participants in China’s internet financing service platform’s structured product market and looks for evident evolutionary principles while considering the impact of behavioral biases and changes in the trade environment. This project then takes these characteristics and principles and integrates them into structured product risk measurements to construct a new theory, methodology, model, and applied framework based on the risk measurements of financial products offered by internet service platforms. By creating a deeper understanding of structured products, we aim to advance the creation of innovative financial products based on big data, improve market supervision, and improve management of systemic risk. .This analysis also useful for creating a set of evaluation methods and standards to address problems in the trade environment while taking into account the “Chinese characteristics” of China’s financial market. It provides a standard reference point to examine the turmoil caused in the financial system by changes in the trade environment, to simulate relevant economic plans, and to evaluate the effectiveness of economic policies..Our analysis employs several different tools from a variety different fields—VaR and CVaR theories (financial engineering), mixed-Copula and model averaging theory, capital asset pricing theory (continuous-time finance), and time series analysis (econometrics)—and by integrates computational finance experimentation with behavioral finance.