Statistical Inference for High Frequency Data

高频数据的统计推断

• 批准号: 0604758
• 负责人: Per Mykland
• 金额: $ 22万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0604758&HistoricalAwards=false
• 关键词: Statistical; Inference; Frequency; Data

The project will investigate the estimation of volatility-like objects in the context of the hidden semi-martingale model. Apart from volatility, we are concerned with co-variations, ANOVA, leverage effect, and related quantities. The project uses ideas from contiguity and unbiased estimation to find such estimators. Data are assumed to have high frequency, so that small-interval asymptotics will be used. A main part of the project is concerned with the applications of such estimators. The investigator's earlier findings on nonparametric, trading based, risk management for options will be interfaced with the estimators to find complete procedures for safely unwinding dangerous positions. The estimators will also be combined with forecasting techniques to provide high-frequency based competitors to latent volatility models like GARCH. We can here draw on the martingale type error structure in the high frequency estimation. The economic value of the estimators in terms of portfolio management will also be investigated. A main background for the project is the increasing availability of high frequency data for financial securities prices. This permits, in principle, very precise determination of volatility and similar characteristics of prices. The investigator's finding, however, that prices behave as if they have measurement error, raises a number of questions about how the statistics is carried out. This project will be concerned with both estimation, and applications to risk management, forecasting, portfolio management, and regulation. The results are of interest to investors, regulators, and policymakers.

该项目将研究在隐半鞅模型的背景下对波动性类对象的估计。除了波动性，我们还关注协变、方差分析、杠杆效应和相关量。该项目使用的思想，从邻近和无偏估计，以找到这样的估计。假设数据具有高频率，因此将使用小区间渐近。该项目的一个主要部分是与这些估计的应用。 调查员的早期发现非参数，交易为基础，风险管理的选择将接口与估计，以找到完整的程序，安全解除危险的立场。估计器还将与预测技术相结合，为潜在波动率模型（如GARCH）提供基于高频的竞争对手。在这里，我们可以利用鞅型误差结构在高频估计。还将研究估计量在投资组合管理方面的经济价值。该项目的一个主要背景是金融证券价格的高频数据越来越多。原则上，这允许非常精确地确定价格的波动性和类似特征。然而，调查人员发现，价格的表现似乎存在测量误差，这就提出了一些关于如何进行统计的问题。这个项目将涉及估计，并应用于风险管理，预测，投资组合管理和监管。投资者、监管机构和政策制定者对结果感兴趣。