Kim Christensen, Mark Podolskij
arXiv 28 Jan 2026 · Econometrics · publishedJournal of Econometrics (2006) · 249 citations (OpenAlex)
arXiv:2601.20463 · PDF · DOI · OpenAlex · Extracted main text
We provide a set of probabilistic laws for estimating the quadratic variation of continuous semimartingales with realized range-based variance -- a statistic that replaces every squared return of realized variance with a normalized squared range. If the entire sample path of the process is available, and under a set of weak conditions, our statistic is consistent and has a mixed Gaussian limit, whose precision is five times greater than that of realized variance. In practice, of course, inference is drawn from discrete data and true ranges are unobserved, leading to downward bias. We solve this problem to get a consistent, mixed normal estimator, irrespective of non-trading effects. This estimator has varying degrees of efficiency over realized variance, depending on how many observations that are used to construct the high-low. The methodology is applied to TAQ data and compared with realized variance. Our findings suggest that the empirical path of quadratic variation is also estimated better with the realized range-based variance.
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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 | P. R. Hansen and A. Lunde (2006) Consistent ranking of volatility models | 0.874 | 5 | 2 | 100% |
| 2 | S. Alizadeh and M. W. Brandt and F. X. Diebold (2002) Range-based estimation of stochastic volatility models | 0.843 | 3 | 3 | 100% |
| 3 | 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 | 0.843 | 3 | 3 | 100% |
| 4 | M. B. Garman and M. J. Klass (1980) On the estimation of security price volatilities from historical data | 0.737 | 3 | 2 | 100% |
| 5 | L. C. G. Rogers and S. E. Satchell (1991) Estimating variances from high, low, and closing prices | 0.737 | 3 | 2 | 100% |
| 6 | F. M. Bandi and J. R. Russell (2005) Realized covariation, realized beta and microstructure noise | 0.644 | 2 | 2 | 100% |
| 7 | 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 | 0.644 | 2 | 2 | 100% |
| 8 | K. Christensen and M. Podolskij (2012) Asymptotic theory of range-based multipower variation self | 0.644 | 2 | 2 | 100% |
| 9 | W. Feller (1951) The asymptotic distribution of the range of sums of independent random variables | 0.644 | 2 | 2 | 100% |
| 10 | A. R. Gallant and C.-T. Hsu and G. E. Tauchen (1999) Using daily range data to calibrate volatility diffusions and extract the forward integrated variance | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 42 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Asymptotic theory of range-based multipower variation | 0.874 | 9 | 5 |
| 2 | 2502.02695 | 0.737 | 3 | 2 |
| 3 | Stochastic volatility model with range-based correction and leverage | 0.511 | 2 | 1 |
| 4 | Autoencoder Enhanced Realised GARCH on Volatility Forecasting | 0.405 | 1 | 1 |
| 5 | Estimation of large approximate dynamic matrix factor models based on the EM algorithm and Kalman filtering | 0.405 | 1 | 1 |
| 6 | Spot Regressions with Candlesticks | 0.405 | 1 | 1 |