Uncovering Long-Run Economic Relationships in High-Frequency Financial Data

小行星9730440 该项目提高了研究人员从高频金融数据中提取有关重要经济现象的有用信息的能力。 日内金融数据的增加刺激了跨金融工具和市场的横截面和跨时间依赖性的实证研究的巨大增长。 然而，直到最近才变得清楚，这种高频数据的分析需要引入新的建模范式相对于在日常和较低的频率采用的标准方法。 对传统时间序列技术的依赖总是导致日内特定特征主导所观察到的高频数据的推断统计特性，而任何关于长期依赖性的证据往往被扭曲或完全消失。 这些事实有效地分离了高频数据的实证研究，从其余的文献，和一个近二分法之间出现了所谓的微观结构研究和更主流的实证文献在资产定价金融。 该项目通过开发新的计量经济学时间序列模型和工具来弥合这一差距，并使用这些模型和工具来提高我们对引起剧烈价格反应的信息类型的理解，以及塑造价格波动强度及其与基础市场微观结构关系的力量。 实证研究是基于一个较长的时间跨度的日内汇率和利率。 它提供了一个直接的窗口，以了解市场预期和不同政策制度下宏观经济公告的重要性。 该项目还根据高频数据制定了新的和更准确的风险计量方法，这些方法应能对金融市场风险的管理和监测产生直接的实际影响。 该项目的总体结果应该与应用宏观经济学家、时间序列经济计量学家、金融研究人员、监管机构和从业人员等相关。 ??

9730440 Bollerslev This project enhances the ability of researchers to extract useful information about important economic phenomena from high-frequency financial data. The increased availability of intraday financial data has spurred a tremendous growth in empirical research on the cross-sectional and intertemporal dependencies across financial instruments and markets. However, only recently has it become clear that the analysis of such high-frequency data necessitates the introduction of novel modeling paradigms relative to the standard methodologies employed at the daily and lower frequencies. The reliance on conventional time series techniques invariably results in the intraday-specific features dominating the inferred statistical properties of the observed high-frequency data, whereas any evidence concerning longer-run dependencies tend to be distorted or disappear altogether. These facts have effectively divorced the empirical studies of high-frequency data from the remainder the literature, and a near dichotomy has arisen between so-called microstructure studies and the more mainstream empirical literature in asset pricing finance. This project bridges this gap by developing new econometric time series models and tools and using these to improve our understanding of the type of information that induces dramatic price responses, and the forces shaping the intensity of price movements and their relation to underlying market microstructure. The empirical research is based on a long time span of intradaily exchange and interest rates. It provides a direct window to the market expectations and the importance of macroeconomic announcements across different policy regimes. The project also develops new and more accurate risk measurements based on high-frequency data and these should have immediate practical implications for the management and monitoring of financial market risks. The general results of the project should be of relevant to applied macroeconomists, time series econo metricians, financial researchers, regulators, and practitioners alike. ??