Quantile estimation in ultra-high frequency financial data: a comparison between parametric and semiparametric approach

被引：0
|
作者：
Paola Zuccolotto
机构：
[1] Universitá degli Studi di Brescia,Dipartimento Metodi Quantitativi
来源：
Statistical Methods and Applications | 2003年 / 12卷 / 2期
关键词：
Ultra-high frequency data; ACD models; quantiles estimation; distribution free sample random variables;
D O I：
10.1007/s10260-003-0058-y
中图分类号：
学科分类号：
摘要：
In the context of ACD models for ultra-high frequency data different specifications are available to estimate the conditional mean of intertrade durations, while quantiles estimation has been completely neglected by literature, even if to trading extent it can be more informative. The main problem arising with quantiles estimation is the correct specification of durations’ probability law: the usual assumption of Exponentially distributed residuals, is very robust for the estimation of parameters of the conditional mean, but dramatically fails the distributional fit. In this paper a semiparametric approach is formalized, and compared with the parametric one, deriving from Exponential assumption. Empirical evidence for a stock of Italian financial market strongly supports the former approach.
引用
收藏
页码：243 / 257
页数：14
相关论文
共 50 条
  • [21] Study of comparison between Ultra-high Frequency (UHF) method and ultrasonic method on PD detection for GIS
    Li, Yanran
    Chen, Duo
    Li, Li
    Zhang, Jiwei
    Li, Guang
    Liu, Hongxia
    [J]. 2017 3RD INTERNATIONAL CONFERENCE ON ENERGY, ENVIRONMENT AND MATERIALS SCIENCE (EEMS 2017), 2017, 94
  • [22] Parametric vs. semiparametric long memory: Comments on "Prediction from ARFIMA models: Comparison between MLE and semiparametric estimation"
    Arteche, Josu
    [J]. INTERNATIONAL JOURNAL OF FORECASTING, 2012, 28 (01) : 54 - 56
  • [23] Non-parametric kernel estimation for symmetric Hawkes processes. Application to high frequency financial data
    E. Bacry
    K. Dayri
    J. F. Muzy
    [J]. The European Physical Journal B, 2012, 85
  • [24] Non-parametric kernel estimation for symmetric Hawkes processes. Application to high frequency financial data
    Bacry, E.
    Dayri, K.
    Muzy, J. F.
    [J]. EUROPEAN PHYSICAL JOURNAL B, 2012, 85 (05):
  • [25] Complex correlation approach for high frequency financial data
    Wilinski, Mateusz
    Ikeda, Yuichi
    Aoyama, Hideaki
    [J]. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2018,
  • [26] Daily Semiparametric GARCH Model Estimation Using Intraday High-Frequency Data
    Chai, Fangrou
    Li, Yuan
    Zhang, Xingfa
    Chen, Zhongxiu
    [J]. SYMMETRY-BASEL, 2023, 15 (04):
  • [27] Modeling CAC40 volatility using ultra-high frequency data
    Degiannakis, Stavros
    Floros, Christos
    [J]. RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE, 2013, 28 : 68 - 81
  • [28] Estimation and filtering by reversible jump MCMC for a doubly stochastic Poisson model for ultra-high-frequency financial data
    Centanni, S.
    Minozzo, M.
    [J]. STATISTICAL MODELLING, 2006, 6 (02) : 97 - 118
  • [29] Robust covariance estimation with noisy high-frequency financial data
    Wang, Jiandong
    Bai, Manying
    [J]. JOURNAL OF NONPARAMETRIC STATISTICS, 2022, 34 (04) : 804 - 830
  • [30] VAST VOLATILITY MATRIX ESTIMATION FOR HIGH-FREQUENCY FINANCIAL DATA
    Wang, Yazhen
    Zou, Jian
    [J]. ANNALS OF STATISTICS, 2010, 38 (02): : 943 - 978
12345