Realised quantile-based estimation of the integrated variance

By Kim Christensen, Roel C. A. Oomen, Mark Podolskij. 2010

130 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/36ec50c3189f5632872842a4859d0090c2d6de42

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Citations in the corpus

Optimal combinations of realised volatility estimators (2009, background): “…and Shephard (2008, in press), Barndorff-Nielsen and Shephard (2002, 2004, 2006), Christensen and Podolskij (2007), Bandi and Russell (2006, 2008), Christensen, Oomen, and Podolskij (2008), Hansen and Lunde (2006a), Large (2005), Oomen (2006) and Zhang, Mykland, and Aı̈t-Sahalia (2005)…”
Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes (2015, unknown): “7 (6)Christensen et al. (2010) refers to this formulation of the QRV as “subsampled QRV”, as opposed to “block QRV”, which has adjacent non-overlapping blocks.”
Outlyingness weighted covariation (2011, methodology): “This value is of the same order as the noise ratios reported in Table 1 of Christensen et al. (2010) for S&P 500 stocks in 2006.”
Fact or friction: jumps at ultra high frequency (2014, unknown): “3 Alternative ways of estimating the integrated variance in the presence of jumps include the threshold estimators of A¨ıt-Sahalia and Jacod (2009b,a) and Mancini (2004, 2009), and the quantile-based estimators of Christensen, Oomen, and Podolskij (2010) and Andersen, Dobrev, and Schaumburg (2012).”
Preaveraging-Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence (2013, methodology): “An alternative way of jump robust measuring in diffusion models with microstructure noise, which we will not follow in this article, is the quantile estimation method introduced by Christensen, Oomen, and Podolskij (2010).”
HAR Modeling for Realized Volatility Forecasting (2012, unknown): “Recent research focusing on jump detection and volatility measuring in presence of jumps includes [12], [62], [59], [56], [2], [1], [29], [63] and [24].”
Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting (2010, background): “Their estimators also admit a central limit theorem in the presence of jumps, but with a larger asymptotic variance than TBPV. Related work can be found in Christensen et al. (2008) and Boudt et al. (2008).”
Pre-Averaging Estimators of the Ex-Post Covariance Matrix in Noisy Diffusion Models with Non-Synchronous Data (2010, unknown): “This feature of the data, the so-called Epps effect, was highlighted by Epps (1979). Intuitively, as the sampling frequency is increased, there are more and more zero-returns in the presence of non-synchronous trading, and this will dominate realised covariance and related statistics (e.g. realised ”