Timo Dimitriadis, Roxana Halbleib, Jeannine Polivka, Jasper Rennspies, Sina Streicher, Axel Friedrich Wolter
arXiv 22 Dec 2022 · Econometrics · publishedJournal of Econometrics (2025) · 1 citations (OpenAlex)
arXiv:2212.11833 · PDF · DOI · OpenAlex · Extracted main text
This paper analyzes the benefits of sampling intraday returns in intrinsic time for the realized variance (RV) estimator. We theoretically show in finite samples that depending on the permitted sampling information, the RV estimator is most efficient under either hitting time sampling that samples whenever the price changes by a pre-determined threshold, or under the new concept of realized business time that samples according to a combination of observed trades and estimated tick variance. The analysis builds on the assumption that asset prices follow a diffusion that is time-changed with a jump process that separately models the transaction times. This provides a flexible model that allows for leverage specifications and Hawkes-type jump processes and separately captures the empirically varying trading intensity and tick variance processes, which are particularly relevant for disentangling the driving forces of the sampling schemes. Extensive simulations confirm our theoretical results and show that for low levels of noise, hitting time sampling remains superior while for increasing noise levels, realized business time becomes the empirically most efficient sampling scheme. An application to stock data provides empirical evidence for the benefits of using these intrinsic sampling schemes to construct more efficient RV estimators as well as for an improved forecast performance.
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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 | Dahlhaus, R. and Tunyavetchakit, S (2016) Volatility decomposition and estimation in time-changed price models | 1.000 | 8 | 4 | 100% |
| 2 | Jacod, J., Li, Y., and Zheng, X (2017) Statistical properties of microstructure noise | 0.928 | 10 | 5 | 80% |
| 3 | Fukasawa, M (2010) Realized volatility with stochastic sampling | 0.928 | 5 | 3 | 80% |
| 4 | Li, Z. M. and Linton, O (2022) A remedi for microstructure noise | 0.909 | 8 | 4 | 75% |
| 5 | Patton, A. J (2011) Data-based ranking of realised volatility estimators | 0.874 | 9 | 3 | 67% |
| 6 | Liu, L. Y., Patton, A. J., and Sheppard, K (2015) Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes | 0.874 | 7 | 2 | 100% |
| 7 | Da, R. and Xiu, D (2021) When moving-average models meet high-frequency data: Uniform inference on volatility | 0.843 | 4 | 3 | 75% |
| 8 | Barndorff-Nielsen, O. E., Hansen, P. R., Lunde, A., and Shephard, N (2011) Subsampling realised kernels | 0.811 | 4 | 2 | 100% |
| 9 | Oomen, R. C. A (2005) Properties of bias-corrected realized variance under alternative sampling schemes | 0.811 | 4 | 2 | 100% |
| 10 | Oomen, R. C. A (2006) Properties of realized variance under alternative sampling schemes | 0.783 | 25 | 4 | 48% |
Showing the top 10 of 81 scored citations.