NSCPHD1081 Statistical Methods for High-Frequency Data

In recent years there has been a vast increase in the amount of high-frequency data available, particularly 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 this subject. In this course we start from scratch, introducing a probabilistic model for such data, and then turn to estimation in this model, with main emphasis on estimating volatility. Similar techniques to those we present can be applied to estimate 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.