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.

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.

scholarly journals Evidence of Information Asymmetry and Herding Behaviour – The Swiss Franc Unpegging Event in Perspective

2020 ◽  
Vol 19 (3) ◽  
pp. 59-77
Author(s):  
Shruti Garg
Keyword(s):  
Information Asymmetry ◽  
Short Duration ◽  
High Frequency ◽  
Swiss Franc ◽  
High Frequency Data ◽  
Frequency Data ◽  
Indian Market ◽  
Causality Test ◽  
Financial Events ◽  
The Impact

The paper aims to find the impact of financial events that occurred in one country on another. Taking the case of the Swiss Franc Unpegging of 2015 in Switzerland, the paper observes its impact on the Indian economy. This is done by studying the information asymmetry and herding behaviour in Indian market on the day of the event. The study uses two sets of data, (i) high frequency data and (ii) 3 years index data of both countries. The Ganger Causality test has been conducted to study the cause and effect relationship between the economies, which helps determine the impact on any of the countries. The study found that herding behaviour and information asymmetry in Indian market are now linked to each other in such a way that the country is affected even if the event has not occurred in the economy itself, however, only for a short duration of time. There also seems to be a huge gap between information available amongst all investors.

The nonlinear characteristics of Chinese stock index futures yield volatility

2019 ◽  
Vol 10 (2) ◽  
pp. 175-196 ◽  
Author(s):  
Xuebiao Wang ◽  
Xi Wang ◽  
Bo Li ◽  
Zhiqi Bai
Keyword(s):  
High Frequency ◽  
Futures Market ◽  
Stock Index ◽  
High Frequency Data ◽  
Stock Index Futures ◽  
Frequency Data ◽  
Short Term ◽  
Content Type ◽  
Index Futures ◽  
Basic Characteristics

Purpose The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory. Design/methodology/approach This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry. Findings This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased. Research limitations/implications This paper has research limitations in variable measurement and data selection. Practical implications This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration. Originality/value The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

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.

Implied volatility smirk and future stock returns: evidence from the German market

2015 ◽  
Vol 41 (12) ◽  
pp. 1357-1379
Author(s):  
Di Mo ◽  
Neda Todorova ◽  
Rakesh Gupta
Keyword(s):  
High Frequency ◽  
Implied Volatility ◽  
Stock Index ◽  
High Frequency Data ◽  
Frequency Data ◽  
European Options ◽  
German Market ◽  
Content Type ◽  
The Relationship ◽  
Option Market

Purpose – The purpose of this paper is to investigate the relationship between option’s implied volatility smirk (IVS) and excess returns in the Germany’s leading stock index Deutscher-Aktien Index (DAX) 30. Design/methodology/approach – The study defines the IVS as the difference in implied volatility derived from out-of-the-money put options and at-the-money call options. This study employs the ordinary least square regression with Newey-West correction to analyse the relationship between IVS and excess DAX 30 index returns in Germany. Findings – The authors find that the German market adjusts information in an efficient way. Consequently, there is no information linkage between option volatility smirk and market index returns over the nine years sample period after considering the control variables, global financial crisis dummies, and the subsample test. Research limitations/implications – This study finds that the option market and the DAX 30 index are informationally efficient. Implications of the findings are that the investors cannot profit from the information contained in the IVS since the information is simultaneously incorporated into option prices and the stock index prices. The findings of this study are applicable to other markets with European options and for market participants who seek to exploit short-term market divergence from efficiency. Originality/value – The relationship between IVS and stock price changes has not been investigated sufficiently in academic literature. This study looks at this relationship in the context of European options using high-frequency transactions data. Prior studies look at this relationship for only American options using daily data. Pricing efficiency of the European option market using high-frequency data have not been studied in the prior literature. The authors find different results for the German market based on this high-frequency data set.

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.

The economic value of using CAW-type models to forecast covariance matrix

2019 ◽  
Vol 9 (3) ◽  
pp. 338-359
Author(s):  
Shuran Zhao ◽  
Jinchen Li ◽  
Yaping Jiang ◽  
Peimin Ren
Keyword(s):  
Covariance Matrix ◽  
High Frequency ◽  
High Frequency Data ◽  
Leverage Effect ◽  
Frequency Data ◽  
Economic Values ◽  
Content Type ◽  
New Model ◽  
Return Rates ◽  
The Matrix

Purpose The purpose of this paper is twofold: to improve the traditional conditional autoregressive Wishart (CAW) and heterogeneous autoregressive (HAR)-CAW model to account for heterogeneous leverage effect and to adjust the high-frequency volatility. The other is to confirm whether CAW-type models that have statistical advantages have economic advantages. Design/methodology/approach Based on the high-frequency data, this study proposed a new model to describe the volatility process according to the heterogeneous market hypothesis. Thus, the authors acquire needed and credible high-frequency data. Findings By designing two mean-variance frameworks and considering several economic performance measures, the authors find that compared with five other models based on daily data, CAW-type models, especially LHAR-CAW and HAR-CAW, indeed generate the substantial economic values, and matrix adjustment method significantly improves the three CAW-type performances. Research limitations/implications The findings in this study suggest that from the aspect of economics, LHAR-CAW model can more accurately built the dynamic process of return rates and covariance matrix, respectively, and the matrix adjustment can reduce bias of realized volatility as covariance matrix estimator of return rates, and greatly improves the performance of unadjusted CAW-type models. Practical implications Compared with traditional low-frequency models, investors should allocate assets according to the LHAR-CAW model so as to get more economic values. Originality/value This study proposes LHAR-CAW model with the matrix adjustment, to account for heterogeneous leverage effect and empirically show their economic advantage. The new model and the new bias adjustment approach are pioneering and promote the evolution of financial econometrics based on high-frequency data.

The intraday effect of nature disaster and production safety accident announcement based on high-frequency data from China’s stock markets

2015 ◽  
Vol 5 (3) ◽  
pp. 277-302 ◽  
Author(s):  
Ping Li ◽  
Huailin Tang ◽  
Jingchi Liao
Keyword(s):  
Stock Markets ◽  
High Frequency ◽  
Stock Price ◽  
Abnormal Returns ◽  
High Frequency Data ◽  
Frequency Data ◽  
Content Type ◽  
Production Safety ◽  
Before And After ◽  
Safety Accident

Purpose – The purpose of this paper is to investigate the intraday effect of nature disaster (external inevitable factor) and production safety accident (PSA) (internal factor regarding management level) announcement on stock price in China’s stock markets. Design/methodology/approach – Using high-frequency data, this study adopts event study method to examine the intraday abnormal returns as well as the volatility of stock price before and after the announcement of nature disaster and PSA. Findings – First, both nature disaster announcement and PSA announcement produce negative effects on stock returns. However, there are some differences in effects between the different types of announcement. Second, it is just within the event day (announcement day if trading day, otherwise the first trading day after announcement) that the volatility of stock price is distinctly increased by the two kinds of announcement. Third, there are some differences in the impacts of nature disaster announcement on firms in different industries. Finally, there are also some differences observed between the impacts of PSA announcement on chemical firms and other firms. Originality/value – It is the first time that using high-frequency data to analyze the intraday impact of nature disaster and PSA announcement on stock short price behavior. The results can help us to understand the role of market microstructure playing in the process of stock price formation, especially the stock price movements before and after disaster and accident announcement and the sensitivity to the announcement. The empirical results have important implications for investors when making trading decisions, and for market regulators when setting trading rules.

Test for volatility spillover effects in Japan’s oil futures markets by a realized variance approach

2019 ◽  
Vol 36 (2) ◽  
pp. 224-239 ◽  
Author(s):  
Tadahiro Nakajima
Keyword(s):  
Crude Oil ◽  
Granger Causality ◽  
High Frequency ◽  
High Frequency Data ◽  
Volatility Spillover ◽  
Frequency Data ◽  
Oil Futures ◽  
Content Type ◽  
Daily Data ◽  
Egarch Model

Purpose The purpose of this paper is twofold. First, the paper examines the risk transmission between crude oil and petroleum product prices of Japan’s oil futures market. Second, it compares the performance of two tests for Granger causality using realized variance (RV) and the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. Design/methodology/approach The author measures the daily RV of crude oil, kerosene and gasoline futures listed on the Tokyo Commodity Exchange using high-frequency data, and he examines the Granger causality in variance between these variables using the vector autoregression model. Further, the author estimates the EGARCH model based on daily data and test for Granger causality in variance between commodity futures using Hong’s (2001) approach. Findings The results of the RV approach reveal that the hypothesis on the existence of a mutual volatility spillover between crude oil and petroleum product markets is accepted. However, the results of the conventional approach indicate that all the hypotheses on Granger causalities in variance are rejected. The methodology based on intraday high-frequency data exhibits higher power than the conventional approach based on daily data. Originality/value This is the first paper to investigate Japan’s oil market using RV. The authors conclude that the approach based on RV is universally adoptable when testing for Granger causality in variance.

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