Investment Approaches and Applications in Financial Markets: Evolutionary Kernel Based Subset Time-Series Using Semi-Parametric Approaches

该项目将基于子集时间序列建模开发新的投资评估。将介绍使用半参数方法的创新进化核平滑算法。该项目将在金融市场上对这种模型进行三个重要的应用：a)对通胀指数债券进行基准评估；b)评估全球多元化投资基金的表现；以及c)对破坏稳定的通缩或通胀的出现提供早期预警。这三项应用将改善风险管理做法和投资业绩。递归算法将为研究投资资产价格变动和市场波动提供新的统计方法。

The project will develop new investment assessments based on subset time-series modeling. Innovative evolutionary kernel smoothing algorithms using semi-parametric approaches will be introduced. The project will make three important applications of this modeling in financial markets: a) benchmarking and evaluation of inflation-indexed bonds; b) evaluation of the performance of global diversified investment funds; and c) prediction to provide early warning of the emergence of destabilising deflation or inflation. These three applications will lead to improved risk management practices and investment performance. Recursive algorithms will provide new statistical methods to study investment asset price movements and market volatility.