Oil Prices and the Stock Markets: Evidence from High Frequency Data

2019 ◽  
Author(s):  
Sajjadur Rahman ◽  
Apostolos Serletis
Keyword(s):  
Stock Markets ◽  
High Frequency ◽  
Oil Prices ◽  
High Frequency Data ◽  
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.

Nonlinear dynamic correlation between geopolitical risk and oil prices: A study based on high-frequency data

2021 ◽  
Vol 56 ◽  
pp. 101370
Author(s):  
Jianbai Huang ◽  
Qian Ding ◽  
Hongwei Zhang ◽  
Yaoqi Guo ◽  
Muhammad Tahir Suleman
Keyword(s):  
High Frequency ◽  
Nonlinear Dynamic ◽  
Oil Prices ◽  
High Frequency Data ◽  
Frequency Data ◽  
Dynamic Correlation

Using Mutual Information to Analyse Serial Dependence

2021 ◽  
pp. 411-427
Author(s):  
Andreia Dionísio ◽  
Paulo Ferreira
Keyword(s):  
Mutual Information ◽  
Stock Markets ◽  
High Frequency ◽  
Nonlinear Dependence ◽  
High Frequency Data ◽  
Frequency Data ◽  
Serial Dependence ◽  
Very High Frequency ◽  
Global Correlation ◽  
Before And After

The main objective of this research is to analyse the serial dependence of high frequency data for G7 stock indices. The authors use two different periodicities, and with linear and nonlinear approaches, they evaluate the stock markets' behaviour and conclude about the higher or lower dependence levels of the stock markets in the periods before and after the COVID-19 pandemic declaration. They use mutual information and the global correlation coefficient based on that measure, comparing results with the linear coefficient. The results are clear, showing that nonlinear dependence exists and could be an important factor in terms of historical information, especially for very high frequency data. Results are mixed in regard to the effect of the pandemic declaration in the dependence of stock markets.

scholarly journals Modeling Financial Intraday Jump Tail Contagion with High Frequency Data Using Mutually Exciting Hawkes Process

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chao Yu ◽  
Jianxin Bi ◽  
Xujie Zhao
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
High Frequency ◽  
Asset Price ◽  
High Frequency Data ◽  
Background Intensity ◽  
Chinese Stock Market ◽  
Hawkes Process ◽  
Frequency Data ◽  
The Stability

Financial extreme jumps in asset price may propagate across stock markets and lead to the market-wide crashes, which severely threatens the stability of the financial system. In order to analyzing the contagion features of jump tail risk, this paper proposes a mutually exciting contagion model based on Hawkes process with intraday high frequency data. We use a simple two-stage method that first extracts the jump component nonparametrically from the high frequency data and then models the intraday jump tail using mutually exciting Hawkes process. Moreover, we take both the occurrence time and magnitude of jump into account in modeling the conditional intensity of Hawkes process. The proposed method is applied to the five-minute high frequency data of the Chinese stock market. The empirical results show that, for the two main Chinese stock markets, only background intensity is significant in the Shanghai stock market, while mutually exciting effect is significant in the Shenzhen stock market. Both the location and size of jump in the Shanghai stock market have significant stimulation to the next occurrences of jump in the Shenzhen stock market. Furthermore, the proposed model performs very well in predicting the future jump tail events.

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