Estimation and Inference Methods for Continuous-Time Models

连续时间模型的估计和推理方法

• 批准号: 1227448
• 负责人: Jia Li
• 金额: $ 5万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1227448&HistoricalAwards=false
• 关键词: Estimation; Inference; Methods; Continuous; Time

This research project involves developing new estimation and inference tools for continuous-time semimartingale models sampled at high frequency. The semimartingale model is the most general model for asset prices that precludes arbitrage opportunities and, as a result, has been the workhorse model in modern asset pricing.Semimartingales have different components: a stochastic drift capturing the smooth movement of the asset price, a continuous martingale part modeling diffusive volatility and a jump part capturing abrupt movements of the asset price. While each component plays a distinct role in applications, it is statistically nontrivial to disentangle one component from the others. The objective of this project is to develop a novel statistical estimation and inference procedure for the jump component of the semimartingale. Compared with the existing methods, the new procedure will be more robust, especially when price jumps are difficult to identify from the data.While the motivating examples in the proposed activity are those of financial models, the methods developed in this project are valid for generic semimartingales. Semimartingales play a central role in the general theory of stochastic processes and stochastic calculus. Besides economics and finance, semimartingales have also been used in biological, chemical, and electrical applications. The econometric and statistical methods developed here may find applications in these fields provided that observations are available at high frequencies.The project integrates research and education by working closely with graduate students in the form of research assistantships. The proposed methodologies involve new implementation procedures whose code will be made publicly available. The results will be disseminated broadly through publications and presentations at seminars, conferences and professional association meetings.

该研究项目涉及开发新的估计和推理工具，用于以高频采样的连续时间半明星模型。 The semimartingale model is the most general model for asset prices that precludes arbitrage opportunities and, as a result, has been the workhorse model in modern asset pricing.Semimartingales have different components: a stochastic drift capturing the smooth movement of the asset price, a continuous martingale part modeling diffusive volatility and a jump part capturing abrupt movements of the asset price.尽管每个组件在应用程序中都起着独特的作用，但将一个组件与其他组件删除在统计学上是不平衡的。该项目的目的是为Semimartingale的跳跃成分开发新的统计估计和推理程序。与现有方法相比，新的过程将更加健壮，尤其是当价格上涨很难从数据中识别出来时。虽然拟议的活动中的激励示例是财务模型的示例，但本项目中开发的方法对于通用的半明星有效。半明星在随机过程和随机演算的一般理论中起着核心作用。除了经济学和金融外，半明天还用于生物，化学和电气应用中。这里开发的计量经济学和统计方法可能会在这些领域的应用中找到应用，只要观察值在高频上可用。拟议的方法涉及新的实施程序，其代码将公开可用。结果将通过研讨会，会议和专业协会会议上的出版物和演讲大致传播。