Kim Christensen, Mark Podolskij
arXiv 22 Feb 2026 · Econometrics · publishedJournal of Financial Econometrics (2012) · 40 citations (OpenAlex)
arXiv:2602.19287 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we present a realized range-based multipower variation theory, which can be used to estimate return variation and draw jump-robust inference about the diffusive volatility component, when a high-frequency record of asset prices is available. The standard range-statistic -- routinely used in financial economics to estimate the variance of securities prices -- is shown to be biased when the price process contains jumps. We outline how the new theory can be applied to remove this bias by constructing a hybrid range-based estimator. Our asymptotic theory also reveals that when high-frequency data are sparsely sampled, as is often done in practice due to the presence of microstructure noise, the range-based multipower variations can produce significant efficiency gains over comparable subsampled return-based estimators. The analysis is supported by a simulation study and we illustrate the practical use of our framework on some recent TAQ 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 | K. Christensen and R. C. A. Oomen and M. Podolskij (2010) Realised quantile-based estimation of the integrated variance self | 1.000 | 5 | 3 | 100% |
| 2 | K. Christensen and M. Podolskij and M. Vetter (2009) Bias-correcting the realized range-based variance in the presence of market microstructure noise self | 0.928 | 4 | 3 | 100% |
| 3 | F. Corsi and D. E. Pirino and R. Renò (2010) Threshold bipower variation and the impact of jumps on volatility forecasting | 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. Martens and D. van Dijk (2007) Measuring volatility with the realized range | 0.928 | 4 | 3 | 100% |
| 6 | K. Christensen and M. Podolskij (2007) Realized range-based estimation of integrated variance self | 0.874 | 9 | 5 | 67% |
| 7 | Y. Aït-Sahalia and J. Jacod (2009) Estimating the degree of activity of jumps in high frequency data | 0.874 | 5 | 2 | 100% |
| 8 | Y. Aït-Sahalia and J. Jacod (2009) Testing for jumps in a discretely observed process | 0.874 | 5 | 2 | 100% |
| 9 | O. E. Barndorff-Nielsen and N. Shephard and M. Winkel (2006) Limit theorems for multipower variation in the presence of jumps | 0.874 | 5 | 2 | 100% |
| 10 | L. Zhang and P. A. Mykland and Y. Aït-Sahalia (2005) A tale of two time scales: determining integrated volatility with noisy high-frequency data | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 79 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Realized range-based estimation of integrated variance | 0.644 | 2 | 2 |
| 2 | Spot Regressions with Candlesticks | 0.405 | 1 | 1 |
| 3 | The fine structure of electricity price volatility | 0.405 | 1 | 1 |