Data-driven jump detection thresholds for application in jump regressions

Published

Journal Article

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This paper develops a method to select the threshold in threshold-based jump detection methods. The method is motivated by an analysis of threshold-based jump detection methods in the context of jump-diffusion models. We show that over the range of sampling frequencies a researcher is most likely to encounter that the usual in-fill asymptotics provide a poor guide for selecting the jump threshold. Because of this we develop a sample-based method. Our method estimates the number of jumps over a grid of thresholds and selects the optimal threshold at what we term the ‘take-off’ point in the estimated number of jumps. We show that this method consistently estimates the jumps and their indices as the sampling interval goes to zero. In several Monte Carlo studies we evaluate the performance of our method based on its ability to accurately locate jumps and its ability to distinguish between true jumps and large diffusive moves. In one of these Monte Carlo studies we evaluate the performance of our method in a jump regression context. Finally, we apply our method in two empirical studies. In one we estimate the number of jumps and report the jump threshold our method selects for three commonly used market indices. In the other empirical application we perform a series of jump regressions using our method to select the jump threshold.

Full Text

Duke Authors

Cited Authors

  • Davies, R; Tauchen, G

Published Date

  • June 1, 2018

Published In

Volume / Issue

  • 6 / 2

Electronic International Standard Serial Number (EISSN)

  • 2225-1146

Digital Object Identifier (DOI)

  • 10.3390/econometrics6020016

Citation Source

  • Scopus