Econometrics of co-jumps in high-frequency data with noise

The Econometrics of High Frequency Data

There has in recent years been a vast increase in the amount of high frequency data available. There has also been an explosion in the literature on the subject. In this course, we start from scratch, introducing the probabilistic model for such data, and then turn to the estimation question in this model. We shall be focused on the (for this area) emblematic problem of estimating volatility. S...

Detecting spurious jumps in high frequency data

We propose a technique to avoid spurious detection of jumps in high frequency data via an explicit thresholding on available test statistics. We prove that it eliminates asymptotically all spurious jumps. Monte Carlo results show that it performs also well in finite samples. Our empirical investigation of Dow Jones stocks reveals that the spurious detections represent up to 50% of the jumps det...

High-Frequency Financial Econometrics

This book is fundamentally about the estimation of risk. At an intuitive level, risk is easy to understand: given an asset with a current price of say $100, what is the likelihood that at some future time—the risk horizon—its price will be less than $90? Or more than $120? And how does the probability of observing a price below $90 change as a function of the risk horizon? Such heuristic notion...

The Econometrics of High Frequency Data 2 1 . 2 High Frequency Data

Testing for jumps in noisy high frequency data

This paper proposes a robustification of the test statistic of Aït-Sahalia and Jacod (2009b) for the presence of market microstructure noise in high frequency data, based on the pre-averaging method of Jacod et al. (2010). We show that the robustified statistic restores the test’s discriminating power between jumps and no jumps despite the presence of market microstructure noise in the data. © ...