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@inproceedings{1355940,
author = {Hruška, Juraj and Deev, Oleg},
address = {Brno},
booktitle = {European Financial Systems 2016. Proceedings of the 13th International Scientific Conference},
keywords = {volatility; high frequency trading; general method of moments; Markov switching model; GARCH},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Brno},
isbn = {978-80-210-8308-0},
pages = {249-255},
publisher = {Masaryk University},
title = {High-Frequency Trading and Price Volatility in the Paris Euronext Stock Market},
url = {http://is.muni.cz/do/econ/sborniky/2016/EFS2016-Proceedings_september_2.pdf},
year = {2016}
}
TY - JOUR
ID - 1355940
AU - Hruška, Juraj - Deev, Oleg
PY - 2016
TI - High-Frequency Trading and Price Volatility in the Paris Euronext Stock Market
PB - Masaryk University
CY - Brno
SN - 9788021083080
KW - volatility
KW - high frequency trading
KW - general method of moments
KW - Markov switching model
KW - GARCH
UR - http://is.muni.cz/do/econ/sborniky/2016/EFS2016-Proceedings_september_2.pdf
N2 - Algorithmic trading has become the crucial part of security trading on world equity markets influencing many of its characteristics. In this paper, we consider the effects of high frequency trading on the short term volatility. The aim of the paper is to analyze the relationship between high frequency trading (HFT) and spot volatility in high frequency as well as low frequency data from the French stock market. We employ GMM, GARCH and Markov switching models to estimate the relationship between changes in stock returns and changes in the activities of high frequency traders. We propose our own methodology to proxy changes in the activity of algorithmic traders. We also address the problem of optimal sampling to avoid possible biases in our empirical findings, since high frequency data contain a disruptive volatility component (market microstructure noise), by incorporating Bundi-Russell (2008) test and test of Lagrangian multipliers. Most actively traded stocks listed on the Paris stock exchange are chosen for the empirical analysis. Sampling tests suggest that optimal frequency should be approximately 60 minutes. Results from models confirm the hypotheses of positive impact of high-frequency trading on market volatility.
ER -
HRUŠKA, Juraj and Oleg DEEV. High-Frequency Trading and Price Volatility in the Paris Euronext Stock Market. In \textit{European Financial Systems 2016. Proceedings of the 13th International Scientific Conference}. Brno: Masaryk University, 2016, p.~249-255. ISBN~978-80-210-8308-0.
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