New Statistical Procedures for Analysing Dependence in Non-Gaussian Time Series Data

用于分析非高斯时间序列数据依赖性的新统计程序

• 批准号: DP0664121
• 负责人: Prof Gael Martin
• 金额: $ 15.25万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2006
• 资助国家: 澳大利亚
• 起止时间: 2006-01-03 至 2009-06-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0664121
• 关键词: New; Statistical; Procedures; Analysing; Dependence

In the economic, finance and business spheres, statistical data is often discrete, binary, strictly positive, or characterized by an uneven distribution of values above and below the average. Prominent examples are the high frequency financial data that have become accessible with the computerization of financial markets, including the number of trades in successive time intervals, the direction of price changes, the time between trades and the return on a financial asset over short periods. This project develops a range of new statistical tools that will enable both researchers and practitioners to analyze the dynamic behaviour in such data and thereby validate and implement a range of financial models.

在经济、金融和商业领域，统计数据往往是离散的、二进制的、严格为正的，或者以平均值上下的不均匀分布为特征。突出的例子是随着金融市场电脑化而变得可获得的高频金融数据，包括连续时间间隔内的交易数量、价格变化方向、交易之间的时间以及金融资产在短期内的回报。该项目开发了一系列新的统计工具，使研究人员和从业人员能够分析这些数据中的动态行为，从而验证和实施一系列财务模型。