Fact or Friction: Jumps at Ultra High Frequency

2014-01-31

AI Summary

This study addresses the well-documented bias in conventional jump detection methods, which, when applied to low-frequency data, systematically overestimate price jumps by misattributing high-frequency market microstructure noise to genuine discontinuities. Leveraging millisecond-level tick-by-tick transaction data, the authors propose a novel framework that integrates nonparametric jump detection with tick-level volatility decomposition to identify true price jumps at the order-book level. This approach effectively disentangles market microstructure noise from authentic jump signals. The findings reveal that the contribution of genuine jumps to price variation is an order of magnitude smaller than previously reported in the literature, suggesting that jumps are far rarer events than commonly assumed and thereby revising the prevailing understanding of jump dynamics in financial markets.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

In this paper, we demonstrate that jumps in financial asset prices are not nearly as common as generally thought, and that they account for only a very small proportion of total return variation. We base our investigation on an extensive set of ultra high-frequency equity and foreign exchange rate data recorded at milli-second precision, allowing us to view the price evolution at a microscopic level. We show that both in theory and practice, traditional measures of jump variation based on low-frequency tick data tend to spuriously attribute a burst of volatility to the jump component thereby severely overstating the true variation coming from jumps. Indeed, our estimates based on tick data suggest that the jump variation is an order of magnitude smaller. This finding has a number of important implications for asset pricing and risk management and we illustrate this with a delta hedging example of an option trader that is short gamma. Our econometric analysis is build around a pre-averaging theory that allows us to work at the highest available frequency, where the data are polluted bymicrostructure noise. We extend the theory in a number of directions important for jump estimation and testing. This also reveals that pre-averaging has a built-in robustness property to outliers in high-frequency data, and allows us to show that some of the few remaining jumps at tick frequency are in fact induced by data-cleaning routines aimed at removing the outliers.

Problem

• jumps
• ultra high-frequency data
• price variation
• volatility
• jump detection

Innovation

• ultra high-frequency data
• jump detection
• tick data
• volatility decomposition
• econometric methodology