Fact or friction: Jumps at ultra high frequency

Abstract

This paper shows that jumps in financial asset prices are often erroneously identified and are, in fact, rare events accounting for a very small proportion of the total price variation. We apply new econometric techniques to a comprehensive set of ultra high-frequency equity and foreign exchange tick data recorded at millisecond precision, allowing us to examine the price evolution at the individual order level. We show that in both theory and practice, traditional measures of jump variation based on lower-frequency data tend to spuriously assign a burst of volatility to the jump component. As a result, the true price variation coming from jumps is overstated. Our estimates based on tick data suggest that the jump variation is an order of magnitude smaller than typical estimates found in the existing literature.

References in corpus (3)

• Testing for jumps in a discretely observed process
• Estimation of volatility functionals in the simultaneous presence of microstructure noise and jumps
• Estimating the degree of activity of jumps in high frequency data

Cited by in corpus (8)

• Pathwise large deviations for the Rough Bergomi model
• Impact and Recovery Process of Mini Flash Crashes: An Empirical Study
• Hierarchical adaptive sparse grids and quasi Monte Carlo for option pricing under the rough Bergomi model
• Optimal covariance matrix estimation for high-dimensional noise in high-frequency data
• Semiparametric inference on the fractal index of Gaussian and conditionally Gaussian time series data
• The Local Fractional Bootstrap
• Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book
• Estimation of integrated covariances in the simultaneous presence of nonsynchronicity, microstructure noise and jumps