Short-Selling, Uptick Rule, and Market Quality: Evidence from High-Frequency Data on Hong Kong Stock Exchange

2011 ◽  
Author(s):  
Pengjie Gao ◽  
Jia Hao ◽  
Ivalina Kalcheva ◽  
Tongshu Ma
Keyword(s):  
Hong Kong ◽  
High Frequency ◽  
Stock Exchange ◽  
Short Selling ◽  
High Frequency Data ◽  
Market Quality ◽  
Frequency Data ◽  
Quality Evidence ◽  
Hong Kong Stock Exchange

Order-based manipulation: evidence from Hong Kong stock market

2013 ◽  
Vol 21 (1) ◽  
pp. 111-118 ◽  
Author(s):  
Chun-Hin Chan ◽  
Alfred Ka Chun Ma
Keyword(s):  
Hong Kong ◽  
Stock Market ◽  
High Frequency ◽  
Design Methodology ◽  
High Frequency Data ◽  
Frequency Data ◽  
Content Type ◽  
Level Ii ◽  
Trading Activities ◽  
The Relationship

Purpose – The paper aims to investigate order-based manipulation that consists of order-placing strategies. Design/methodology/approach – Using the bid and ask record provided by Hong Kong Exchanges and Clearing Limited, a Level II dataset, the paper develops a methodology to obtain cancelled orders during regular trading hours. The paper examines the cancelled orders and potential order-based manipulation activities, as well as the corresponding behavior of different groups of stocks. Findings – Empirical results show that the relationship between order cancellation and order-based manipulation is strong and deserves more attention. Originality/value – The methodology can also be used by regulators and authorities to monitor suspicious activities in the market. This paper also suggests that analysis on high-frequency data does improve the understanding of trading activities in the stock market.

scholarly journals Pairs trading with the persistence-based decomposition model

2020 ◽  
Vol 20 (2) ◽  
pp. 151
Author(s):  
Jonas Rende
Keyword(s):  
High Frequency ◽  
Scientific Community ◽  
Large Scale ◽  
Model Fitting ◽  
Short Selling ◽  
High Frequency Data ◽  
Frequency Data ◽  
Pairs Trading ◽  
Risk Return ◽  
The Impact

Recently, the persistence-based decomposition (PBD) model has been introduced to the scientific community by Rende et al. (2019). It decomposes a spread time series between two securities into three components capturing infinite, finite, and no shock persistence. The authors provide empirical evidence that the model adopts well to noisy high-frequency data in terms of model fitting and prediction. We put the PBD model to test on a large-scale high-frequency pairs trading application, using SP 500 minute-by-minute data from 1998 to 2016. After accounting for execution limitations (waiting rule, volume constraints, and short-selling fees) the PBD model yields statistically significant and economically meaningful annual returns after transaction costs of 9.16 percent. These returns can only partially be explained by the exposure to common risk. In addition, the model is superior in terms of risk-return metrics. The model performs very well in bear markets. We quantify the impact of execution limitations on risk and return measures by relaxing backtesting restrictions step-by-step. If no restrictions are imposed, we find annual returns after costs of 138.6 percent.

scholarly journals Daily LGARCH model estimation using high frequency data

2021 ◽  
Vol 1 (2) ◽  
pp. 165-179
Author(s):  
Xiaoling Chen ◽  
◽  
Xingfa Zhang ◽  
Yuan Li ◽  
Qiang Xiong
Keyword(s):  
High Frequency ◽  
Stock Exchange ◽  
Likelihood Estimation ◽  
High Frequency Data ◽  
Simulation Studies ◽  
Frequency Data ◽  
Asymptotic Results ◽  
Estimation Precision ◽  
Quasi Maximum Likelihood ◽  
Autoregressive Conditional Heteroscedasticity

<abstract> <p>In this paper, we introduce the intraday high frequency data to estimate the daily linear generalized autoregressive conditional heteroscedasticity (LGARCH) model. Based on the volatility proxies constructed from the intraday high frequency data, the quasi maximum likelihood estimation (QMLE) of the daily LGARCH model and its asymptotic distribution are studied under some regular assumptions. One criterion is also given to choose the optimal volatility proxy according to the asymptotic results. Simulation studies show that the QMLE of the parameters performs well. It is also found that introducing the intraday high frequency data can significantly improve the estimation precision. The proposed method is applied to analyze the SSE 50 Index, which consists of the 50 largest and most liquid A-share stocks listed on Shanghai Stock Exchange. Empirical results show the method is of potential application value.</p> </abstract>

scholarly journals Liquidity of Tunisian Stock Market: A Panel Var Estimation

2021 ◽  
Vol 10 (3) ◽  
pp. 93
Author(s):  
Wafa Chabeb ◽  
Adel Boubaker
Keyword(s):  
Response Function ◽  
High Frequency ◽  
Stock Exchange ◽  
Impulse Response Function ◽  
High Frequency Data ◽  
Var Model ◽  
Panel Var ◽  
Frequency Data ◽  
Dynamic Interactions ◽  
Dynamic Relationships

The purpose of this paper is to estimate the functions impulsions-response of liquidity on the Tunisian Stock Exchange (TSE). We will use the methodology proposed by Abrigo and Love (2016). Our study is done on an order-driven market. The data is composed of high frequency data of orders listed on the TSE for the period April 2014 to June 2014. Inspired of the study of Jarnecic and Snape (2014), we apply a panel VAR model to stocks traded in continuous in order to examine the dynamic interactions between spread, volatility, size and frequency of transactions. Then we study the liquidity of the TSE through the impulse response function of the Panel VAR model. Our findings show dynamic relationships between spread, volatility, size and frequency of trading. Some differences exist in the dynamics of liquidity when we take into account the trading intensity of the stock. Furthermore, we note that shocks are absorbed after three gaps of 45minutes.

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