Realised quantile-based estimation of the integrated variance

被引:65
|
作者
Christensen, Kim [2 ]
Oomen, Roel [1 ,3 ]
Podolskij, Mark [2 ,4 ]
机构
[1] Deutsch Bank AG, London EC2N 2DB, England
[2] Aarhus Univ, CREATES, Sch Econ & Management, DK-8000 Aarhus, Denmark
[3] Univ Amsterdam, Dept Quantitat Econ, NL-1012 WX Amsterdam, Netherlands
[4] ETH, Dept Math, CH-8092 Zurich, Switzerland
基金
新加坡国家研究基金会;
关键词
Finite activity jumps; Market microstructure noise; Order statistics; Outliers; Realised variance; CONTINUOUS-TIME MODELS; MICROSTRUCTURE NOISE; VOLATILITY; MARKET; JUMPS; PRICES; FUNCTIONALS; ELECTRICITY; RETURNS;
D O I
10.1016/j.jeconom.2010.04.008
中图分类号
F [经济];
学科分类号
02 ;
摘要
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. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:74 / 98
页数:25
相关论文
共 50 条
  • [31] Improved design of quantile-based control charts
    Ning, Xianghui
    Wu, Chunjie
    [J]. JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING, 2011, 28 (07) : 504 - 511
  • [32] QUANTILE-BASED POLICY OPTIMIZATION FOR REINFORCEMENT LEARNING
    Jiang, Jinyang
    Peng, Yijie
    Hu, Jiaqiao
    [J]. 2022 WINTER SIMULATION CONFERENCE (WSC), 2022, : 2712 - 2723
  • [33] Quantile-based risk sharing with heterogeneous beliefs
    Paul Embrechts
    Haiyan Liu
    Tiantian Mao
    Ruodu Wang
    [J]. Mathematical Programming, 2020, 181 : 319 - 347
  • [34] Semiparametric quantile regression using family of quantile-based asymmetric densities
    Gijbels, Irene
    Karim, Rezaul
    Verhasselt, Anneleen
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2021, 157
  • [35] A Quantile-Based Watermarking Approach for Distortion Minimization
    Gort, Maikel Lazaro Perez
    Olliaro, Martina
    Cortesi, Agostino
    [J]. FOUNDATIONS AND PRACTICE OF SECURITY, FPS 2021, 2022, 13291 : 162 - 176
  • [36] Quantile-based risk sharing with heterogeneous beliefs
    Embrechts, Paul
    Liu, Haiyan
    Mao, Tiantian
    Wang, Ruodu
    [J]. MATHEMATICAL PROGRAMMING, 2020, 181 (02) : 319 - 347
  • [37] A quantile-based study on ageing intensity function
    Sunoj, S. M.
    Rasin, R. S.
    [J]. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2018, 47 (22) : 5474 - 5484
  • [38] Quantile-Based Inference for Tempered Stable Distributions
    Hasan A. Fallahgoul
    David Veredas
    Frank J. Fabozzi
    [J]. Computational Economics, 2019, 53 : 51 - 83
  • [39] A quantile-based test of protection for sale model
    Imai, Susumu
    Katayama, Hajime
    Krishna, Kala
    [J]. JOURNAL OF INTERNATIONAL ECONOMICS, 2013, 91 (01) : 40 - 52
  • [40] QUANTILE-BASED STUDY OF (DYNAMIC) INACCURACY MEASURES
    Kayal, Suchandan
    Moharana, Rajesh
    Sunoj, S. M.
    [J]. PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES, 2020, 34 (02) : 183 - 199