|High Frequency Econometrics|
|Instructor：||Per Mykland, Lan Zhang|
|Place：||Lecture Hall, floor 3, Jin Chun Yuan West Building (近春园西楼三层报告厅)|
This is a course on modeling and estimation for high frequency financial data, i.e. data observed more frequently than daily and down to the individual transaction. It is designed for an audience that includes people interested in finance, econometrics, statistics, probability and financial engineering.
In recent years there has been a vast increase in the amount of high frequency data available in finance. Their analysis may require methods different from the common ones for time series of regularly spaced data, and there has been an explosion in the literature on the subject. In this course, we start from scratch, introducing a probabilistic model for such data, and then turn to the estimation question in this model, with main emphasis on estimating volatility. Similar techniques to those we present can be applied to estimating leverage effects, realized regressions, semi-variances, doing analyses of variance, detecting jumps, measuring liquidity by measuring the size of the microstructure noise, and many other objects of interest. The applications are mainly in finance, ranging from risk management to options hedging, execution of transactions, portfolio optimization and forecasting. Methodologies based on high frequency data can also be found in neural science and climatology.
Basic mathematical sophistication, for example, courses in probability theory and stochastic process, in statistical inference, and finally a course in real analysis, or measure theory, or stochastic calculus.
The econometrics of high frequency data. Mykland, P.A. and Zhang, L., Statistical Methods for Stochastic Differential Equations, M. Kessler, A. Lindner, and M. Sorensen, eds. Chapman & Hall/CRC Press, 109- 190, June 2012.
Other relevant articles will be distributed.