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Econometrics 2018, 6(2), 16; https://doi.org/10.3390/econometrics6020016

Data-Driven Jump Detection Thresholds for Application in Jump Regressions

1
Amazon.com, 399 Fairview Ave N, Seattle, WA 98109, USA
2
Department of Economics, Duke University, Durham, NC 27708, USA
*
Author to whom correspondence should be addressed.
Received: 8 January 2018 / Revised: 24 February 2018 / Accepted: 24 February 2018 / Published: 26 March 2018
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Abstract

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. View Full-Text
Keywords: efficient estimation; high-frequency data; jumps; semimartingale; specification test; stochastic volatility efficient estimation; high-frequency data; jumps; semimartingale; specification test; stochastic volatility
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Davies, R.; Tauchen, G. Data-Driven Jump Detection Thresholds for Application in Jump Regressions. Econometrics 2018, 6, 16.

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