The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

Stock market predictability

2014 ◽  
Vol 31 (4) ◽  
pp. 354-370 ◽  
Author(s):  
Silvio John Camilleri ◽  
Christopher J. Green
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Serial Correlation ◽  
Design Methodology ◽  
High Frequency Data ◽  
Frequency Data ◽  
Content Type ◽  
Lag Effects ◽  
Daily Data ◽  
Indian Stock Market

Purpose – The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices. Design/methodology/approach – The authors test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran–Timmermann tests and Granger-Causality. Then, a simple test on overnight returns is proposed to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency. Findings – The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian Stock Market. Research limitations/implications – The indication that non-synchronous trading effects become more pronounced in high-frequency data suggests that prior studies using daily data may underestimate the impacts of non-synchronicity. Originality/value – The originality of the paper rests on various important contributions: overnight returns is looked at to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency; the impacts of non-synchronicity are investigated in terms of lead-lag effects rather than serial correlation; and high-frequency data is used which gauges the impacts of non-synchronicity during less active parts of the trading day.

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.

Block trading, information asymmetry, and the informativeness of trading

2015 ◽  
Vol 5 (3) ◽  
pp. 215-235 ◽  
Author(s):  
Ningning Pan ◽  
Hongquan Zhu
Keyword(s):  
Information Asymmetry ◽  
High Frequency ◽  
Stock Return ◽  
High Frequency Data ◽  
Individual Investors ◽  
Specific Information ◽  
Frequency Data ◽  
Information Effect ◽  
Content Type ◽  
Two Measures

Purpose – The purpose of this paper is to investigate how block trading and asymmetric information contribute to the firm-specific information measured by the stock return synchronicity. Based on China stock market which is dominated by individual investors, this study focus on whether traders of block trading, which are usually institutional investors, are “information trader.” Design/methodology/approach – Based on the high frequency data, the paper constructs two measures of information asymmetry, intraday measure and inter-day measure. Then the paper constructs a multiple regression model and examine how block trading and information asymmetry contribute to the firm-specific information measured by the stock return synchronicity. Findings – The results show that: on the one hand, block trading transmits more firm-specific information, and can reduce the synchronicity; on the other hand, when the degree of information asymmetry is higher, block trading contains more firm-specific information and has a stronger effect on synchronicity. The effect of information asymmetry specifically displays as: block trading during the first half-hour of the trading day has a stronger effect on synchronicity; and block trading occurred in the days with publicly announced trading information has greater impact on synchronicity. Practical implications – The conclusions have important practical implications: for market regulators, monitoring for block trading can improve the recognition and prevention of insider trading; for individual investors, especially the risk aversion investors, recognition of intraday and inter-day information asymmetry is beneficial for them to avoid the risk of asymmetric information. Originality/value – First, the domestic and foreign research mostly concentrated impact of block trading on stock prices. However, reasons of stock price changes include the information effect and non-information effect, this paper selects stock return synchronicity as firm-specific information measure, and mainly focus on the information effect of block trading. Second, based on the high frequency data, the paper constructs two measures of information asymmetry, intraday measure and inter-day measure. Compared with general measure of information asymmetry, such as firm size, earnings quality, the two measures based on high frequency data are more precisely.

scholarly journals When Moving‐Average Models Meet High‐Frequency Data: Uniform Inference on Volatility

Econometrica ◽  
2021 ◽  
Vol 89 (6) ◽  
pp. 2787-2825 ◽  
Author(s):  
Rui Da ◽  
Dacheng Xiu
Keyword(s):  
High Frequency ◽  
Moving Average ◽  
Information Criterion ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Finite Samples ◽  
Moving Average Models ◽  
Moving Average Model ◽  
Itô Semimartingale

We conduct inference on volatility with noisy high‐frequency data. We assume the observed transaction price follows a continuous‐time Itô‐semimartingale, contaminated by a discrete‐time moving‐average noise process associated with the arrival of trades. We estimate volatility, defined as the quadratic variation of the semimartingale, by maximizing the likelihood of a misspecified moving‐average model, with its order selected based on an information criterion. Our inference is uniformly valid over a large class of noise processes whose magnitude and dependence structure vary with sample size. We show that the convergence rate of our estimator dominates n 1/4 as noise vanishes, and is determined by the selected order of noise dependence when noise is sufficiently small. Our implementation guarantees positive estimates in finite samples.

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