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

金融市场的投资方法和应用：使用半参数方法的基于进化核的子集时间序列

• 批准号: LP0562008
• 负责人: Prof Terence O'Neill
• 金额: $ 32.57万
• 依托单位: The Australian National University
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2006
• 资助国家: 澳大利亚
• 起止时间: 2006-11-30 至 2011-12-01
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP0562008
• 关键词: Investment; Approaches; Applications; Financial; Markets

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.

该项目将根据子集时间序列模型开发新的投资评估。创新的进化核平滑算法使用半参数的方法将被引入。该项目将在金融市场中对这一模型进行三个重要应用：a）对通货膨胀指数债券进行基准测试和评估; B）评估全球多样化投资基金的业绩; c）预测出现破坏稳定的通货紧缩或通货膨胀时提供预警。这三个应用程序将改进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。