Kim Christensen, Roel Oomen, Mark Podolskij
arXiv 19 Jan 2026 · Econometrics
arXiv:2601.13006 · PDF · Extracted main text
In this paper, we propose a new jump robust quantile-based realised variance measure of ex-post return variation that can be computed using potentially noisy data. The estimator is consistent for the integrated variance and we present feasible central limit theorems which show that it converges at the best attainable rate and has excellent efficiency. Asymptotically, the quantile-based realised variance is immune to finite activity jumps and outliers in the price series, while in modified form the estimator is applicable with market microstructure noise and therefore operational on high-frequency data. Simulations show that it has superior robustness properties in finite sample, while an empirical application illustrates its use on equity data.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | O. E. Barndorff-Nielsen and P. R. Hansen and A. Lunde and N. Shephard (2008) Designing realized kernels to measure the ex post variation of equity prices in the presence of noise | 0.928 | 5 | 4 | 80% |
| 2 | Y. Aït-Sahalia and J. Jacod (2009) Estimating the degree of activity of jumps in high frequency data | 0.928 | 4 | 3 | 100% |
| 3 | T. G. Andersen and T. Bollerslev and F. X. Diebold (2007) Roughing it up: Including jump components in the measurement, modeling and forecasting of return volatility | 0.928 | 4 | 3 | 100% |
| 4 | X. Huang and G. Tauchen (2005) The relative contribution of jumps to total price variance | 0.928 | 4 | 3 | 100% |
| 5 | M. Podolskij and M. Vetter (2009) Bipower-type estimation in a noisy diffusion setting self | 0.874 | 5 | 2 | 100% |
| 6 | J. Jacod and Y. Li and P. A. Mykland and M. Podolskij and M. Vetter (2009) Microstructure noise in the continuous case: The pre-averaging approach self | 0.843 | 4 | 4 | 75% |
| 7 | T. G. Andersen and D. Dobrev and E. Schaumburg (2008) Duration-based volatility estimation | 0.843 | 4 | 3 | 75% |
| 8 | L. Zhang (2006) Efficient estimation of stochastic volatility using noisy observations: A multi-scale approach | 0.843 | 3 | 3 | 100% |
| 9 | R. C. A. Oomen (2006) Comment on 2005 JBES invited address “Realized variance and market microstructure noise” by Peter R. Hansen and Asger Lunde self | 0.737 | 5 | 2 | 60% |
| 10 | O. E. Barndorff-Nielsen and N. Shephard (2004) Power and bipower variation with stochastic volatility and jumps | 0.737 | 3 | 2 | 100% |
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