An empirical study of volatility and trading volume of heterogeneity of traders using high-frequency data

被引:1
|
作者
Lu, Wen-Cheng [1 ]
Wu, Chung-Han [1 ]
机构
[1] Ming Chuan Univ, Dept Econ, Taipei, Taiwan
来源
关键词
Trading volume; Volatility; Sequential information arrival hypothesis; Mixture of distribution hypothesis;
D O I
10.1080/09720510.2011.10701577
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
This paper examines the dynamic relationship between volatility and trading volume using a vector autoregressive methodology. In this study the trading volume is classified into foreign investors, investment trust, and domestic dealer. By doing this the information used by three different groups of traders can be separated. This study found bidirectional causal relations between trading volume and volatility, which is in accordance with a sequential information arrival hypothesis, which suggests that lagged values of trading volume provide the predictability component of current volatility. Findings also reveal that foreign investors explain a large part of the variance in investment trust and dealers from the viewpoint of variance decomposition analysis. Meanwhile, the trading volume of investment trust accounts for a large portion of the variance in dealers. The foreign investors play an important role in explaining other types of traders' behavior and directions.
引用
收藏
页码:659 / 673
页数:15
相关论文
共 50 条
  • [31] An empirical study of the relation between stock return volatility and trading volume in the BRVM
    Leon, N'dri Konan
    AFRICAN JOURNAL OF BUSINESS MANAGEMENT, 2007, 1 (07): : 176 - 184
  • [32] Estimation of the integrated volatility using noisy high-frequency data with jumps and endogeneity
    Li, Cuixia
    Guo, Erlin
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2018, 47 (03) : 521 - 531
  • [33] Analysis of Wind Farm Output: Estimation of Volatility Using High-Frequency Data
    Manju R. Agrawal
    John Boland
    Barbara Ridley
    Environmental Modeling & Assessment, 2013, 18 : 481 - 492
  • [34] Interactions among High-Frequency Traders
    Benos, Evangelos
    Brugler, James
    Hjalmarsson, Erik
    Zikes, Filip
    JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS, 2017, 52 (04) : 1375 - 1402
  • [35] Forecasting exchange rate volatility using high-frequency data: Is the euro different?
    Chortareas, Georgios
    Jiang, Ying
    Nankervis, John. C.
    INTERNATIONAL JOURNAL OF FORECASTING, 2011, 27 (04) : 1089 - 1107
  • [36] Modeling Jumps and Volatility of the Indian Stock Market Using High-Frequency Data
    Sen R.
    Mehrotra P.
    Journal of Quantitative Economics, 2016, 14 (1) : 137 - 150
  • [37] EQUITY RISK: MEASURING RETURN VOLATILITY USING HISTORICAL HIGH-FREQUENCY DATA
    Chow, Alan
    Lahtinen, Kyre
    STUDIES IN BUSINESS AND ECONOMICS, 2019, 14 (03) : 60 - 71
  • [38] Vast Volatility Matrix Estimation Using High-Frequency Data for Portfolio Selection
    Fan, Jianqing
    Li, Yingying
    Yu, Ke
    JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2012, 107 (497) : 412 - 428
  • [39] Forecasting oil price volatility using high-frequency data: New evidence
    Chen, Wang
    Ma, Feng
    Wei, Yu
    Liu, Jing
    INTERNATIONAL REVIEW OF ECONOMICS & FINANCE, 2020, 66 : 1 - 12
  • [40] Analysis of Wind Farm Output: Estimation of Volatility Using High-Frequency Data
    Agrawal, Manju R.
    Boland, John
    Ridley, Barbara
    ENVIRONMENTAL MODELING & ASSESSMENT, 2013, 18 (04) : 481 - 492