scholarly journals Stock Market Interactions and the Impact of Macroeconomic News - Evidence from High Frequency Data of European Futures Markets

2006 ◽  
Author(s):  
Roman Kräussl ◽  
Bea Canto
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Futures Markets ◽  
High Frequency Data ◽  
Frequency Data ◽  
Macroeconomic News ◽  
Market Interactions ◽  
The Impact

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.

Forecasting downside risk in China’s stock market based on high-frequency data

2019 ◽  
Vol 517 ◽  
pp. 530-541
Author(s):  
Nan Xie ◽  
Zongrun Wang ◽  
Sicen Chen ◽  
Xu Gong
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Downside Risk ◽  
High Frequency Data ◽  
Frequency Data

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.

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.

Modeling Jumps and Volatility of the Indian Stock Market Using High-Frequency Data

2016 ◽  
Vol 14 (1) ◽  
pp. 137-150
Author(s):  
Rituparna Sen ◽  
Pulkit Mehrotra
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Indian Stock Market

Identifying herding effect in Chinese stock market by high-frequency data

2017 ◽  
Author(s):  
Yunfei Hou ◽  
Jianbo Gao ◽  
Fangli Fan ◽  
Feiyan Liu ◽  
Changqing Song
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
High Frequency Data ◽  
Chinese Stock Market ◽  
Frequency Data
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