High-Frequency Trading and Price Volatility in the Paris Euronext Stock Market

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F16%3A00091076" target="_blank" >RIV/00216224:14560/16:00091076 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

High-Frequency Trading and Price Volatility in the Paris Euronext Stock Market

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

High-Frequency Trading and Price Volatility in the Paris Euronext Stock Market

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

European Financial Systems 2016. Proceedings of the 13th International Scientific Conference

ISBN

9788021083080

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

249-255

Název nakladatele

Masaryk University

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

1. 1. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000385692200032