Uncovering Long-Run Economic Relationships in High-Frequency Financial Data -- An Accomplishment Based Renewal

点击翻译按钮获取中文摘要

The advent of computerized communication networks and automated trade execution systems have resulted in the recent availability of real-time prices and information variables for a host of different financial markets and instruments. This project aims to expand on the ability to extract useful information about important economic phenomena from this new rich source of data. Specifically, the high-frequency data hold the promise of delivering: (i) a much better understanding of the type of information that induces important price movements and their relation to the underlying market microstructure; (ii) a deeper understanding concerning the functioning of markets and the effectiveness of various macroeconomic policies; (iii) the development of new and more accurate risk measurements; and (iv) important information about the longer-run interday volatility dependencies that play a crucial role in asset pricing and risk management. Meanwhile, it has become increasingly clear that the satisfactory empirical analysis of high -frequency data presents a host of unique and challenging problems, requiring the development of new modeling paradigms and specialized procedures relative to the techniques employed in traditional time series econometrics involving the analysis of daily or lower frequency macroeconomic and financial data. The previous NSF-sponsored research (award SES-9730440) has been at the forefront of these developments. Many of the ideas and methodologies put forth in our various papers have already been successfully implemented and applied by other researchers and finance practitioners in academia, government, and the private sector. Most immediately, our findings have allowed for the construction of more accurate financial market volatility forecasts and ex-post volatility measurements. Our proposed research agenda in turn holds the promise of improved procedures for risk management, monitoring, and oversight, and should also result in a deeper understanding concerning the efficiency of markets and the effectiveness of different macroeconomic policies. The general results of the proposed activity should therefore be of relevance to applied macroeconomists, time series econometricians, financial researchers, regulators, and practitioners alike.