Statistical Inference for High Frequency Data
高频数据的统计推断
基本信息
- 批准号:1124526
- 负责人:
- 金额:$ 15.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2011
- 资助国家:美国
- 起止时间:2011-09-15 至 2014-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The project is concerned with the estimation of volatility and related quantities for high frequency data using the nonparametric "latent semi-martingale model." The existence of market microstructure (statistically similar to measurement error) is crucial to this problem as it substantially affects estimators. The main questions to be asked by this project are the following: What information is there in the data? What quantities can consistently be estimated on the basis of daily data or even five minute data? A satisfactory answer to these questions will serve several purposes: (1) an understanding of what information (parameter estimates) researchers can hope to extract from data; (2) data compression, because the amounts of data are huge, and estimation of all relevant parameters will provide an (at least approximately) "sufficient" summary of the data; and (3) broader scientific and social goals as discussed below. The data to be looked at include financial transaction and quote data and, in some circumstances, order book data. High dimensional data also will be considered. A major tool will be the use of statistical contiguity which simplifies and clarifies the structure of the data.The availability of high frequency financial data has exploded in recent years. This has opened the possibility of estimating quantities like volatility on a daily basis with high precision. Such estimates are of substantial interest to investors, regulators, and policymakers. An academic literature on this topic is in development, drawing on diverse areas ranging from finance via econometrics and statistics to pure probability. The main topic of this project is to find ways of turning these data into knowledge. Instead of massive, barely structured data, the project seeks to provide estimators of (economically or otherwise) interpretable parameters. The theoretical approach is to see the high frequency data in the context of continuous-time finance models, as used in asset pricing, portfolio management, options trading, and risk management. So far, such models have often relied on hypothetical high frequency data. By bringing them together with actual high frequency data, the project aims, in the long run, for a "grand unified theory" of finance with both theoretical and empirical components. As a corollary, this has the potential to induce transformational change by integrating risk management with business and regulatory decisions and data with models. This may help avoid some of the gravest model-with-no-data mistakes of the last few years. Methods of high frequency data also have application outside finance, such as in neural science and turbulence, and other areas where streaming data are available. Environmental science and monitoring also often present forms of high frequency data. Meanwhile, likelihood theory (which is central to this project) for time dependent data is a setting that connects finance and economics to a great variety of scientific endeavors, including biological and medical science, with mutual feedback between methods in these areas.
本课题主要研究利用非参数“潜在半鞅”模型估计高频数据的波动率及其相关量。“市场微观结构的存在(统计上类似于测量误差)对这个问题至关重要,因为它会对估计者产生重大影响。 这个项目要问的主要问题如下:数据中有什么信息? 根据每天的数据甚至是五分钟的数据,可以一致地估计出哪些数量? 对这些问题的满意回答将服务于以下几个目的:(1)了解研究人员希望从数据中提取哪些信息(参数估计);(2)数据压缩,因为数据量巨大,对所有相关参数的估计将提供(至少近似)“足够”的数据摘要;以及(3)更广泛的科学和社会目标,如下所述。 要查看的数据包括财务交易和报价数据,在某些情况下还包括订单簿数据。 高维数据也将被考虑。 一个主要工具是使用统计连续性,它简化和澄清了数据的结构。 这为每天高精度地估计波动率等数量提供了可能性。 投资者、监管者和政策制定者对这些估计非常感兴趣。 关于这一主题的学术文献正在编写中,涉及从金融到计量经济学和统计学到纯概率的各个领域。 该项目的主要主题是找到将这些数据转化为知识的方法。 该项目寻求提供(经济上或其他方面)可解释参数的估计值,而不是大量的、几乎没有结构化的数据。 理论方法是在连续时间金融模型的背景下查看高频数据,如用于资产定价,投资组合管理,期权交易和风险管理。 到目前为止,这些模型通常依赖于假设的高频数据。 通过将它们与实际的高频数据结合起来,该项目的目标是,从长远来看,建立一个既有理论又有经验成分的金融“大统一理论”。 作为一个必然的结果,这有可能通过将风险管理与业务和监管决策以及数据与模型相结合来引发转型变革。 这可能有助于避免过去几年中一些最严重的无数据模型错误。 高频数据的方法在金融之外也有应用,例如神经科学和湍流,以及其他可以获得流数据的领域。 环境科学和监测也经常呈现高频数据的形式。 与此同时,时间相关数据的似然理论(这是本项目的核心)是一种将金融和经济学与各种科学努力(包括生物和医学科学)联系起来的设置,这些领域的方法之间相互反馈。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Per Mykland其他文献
Per Mykland的其他文献
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{{ truncateString('Per Mykland', 18)}}的其他基金
Collaborative Research: Statistical Inference for High Dimensional and High Frequency Data
合作研究:高维高频数据的统计推断
- 批准号:
2015544 - 财政年份:2020
- 资助金额:
$ 15.5万 - 项目类别:
Standard Grant
Collaborative Research: Statistical Inference for High-Frequency Data
合作研究:高频数据的统计推断
- 批准号:
1713129 - 财政年份:2017
- 资助金额:
$ 15.5万 - 项目类别:
Standard Grant
Collaborative Research: Better efficiency, better forecasting, better accuracy: A new light on the dependence structure in high frequency data
协作研究:更高的效率、更好的预测、更高的准确性:高频数据中依赖结构的新视角
- 批准号:
1407812 - 财政年份:2014
- 资助金额:
$ 15.5万 - 项目类别:
Standard Grant
Inference and Ill-Posedness for Financial High Frequency Data
金融高频数据的推理和不适定
- 批准号:
0631605 - 财政年份:2007
- 资助金额:
$ 15.5万 - 项目类别:
Standard Grant
Statistical Inference for High Frequency Data
高频数据的统计推断
- 批准号:
0604758 - 财政年份:2006
- 资助金额:
$ 15.5万 - 项目类别:
Continuing Grant
Is Deliberate Misspecification Desirable? Statistical Study of Financial and Other Time-Dependent Data
故意错误指定是可取的吗?
- 批准号:
0204639 - 财政年份:2002
- 资助金额:
$ 15.5万 - 项目类别:
Continuing Grant
Mathematical Sciences: Expanison and Likelihood Methods forMartingales and Martingale Inference
数学科学:鞅和鞅推理的展开和似然方法
- 批准号:
9305601 - 财政年份:1993
- 资助金额:
$ 15.5万 - 项目类别:
Standard Grant
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