scholarly journals Stock price dynamics and volatility : a high frequency data perspective

2004 ◽  
Author(s):  
B.P.M. Frijns
Keyword(s):  
High Frequency ◽  
Stock Price ◽  
High Frequency Data ◽  
Price Dynamics ◽  
Frequency Data

Stock Price Jumps and News Sentiment: A Case of Investor Overreaction

2018 ◽  
Vol 11 (2) ◽  
pp. 20-37
Author(s):  
Vinay Kumar Apparaju ◽  
Ashwani Kumar ◽  
Ritu Yadav
Keyword(s):  
High Frequency ◽  
Stock Price ◽  
Strong Form ◽  
High Frequency Data ◽  
Options Market ◽  
Frequency Data ◽  
Innovative Method ◽  
Investor Behaviour ◽  
News Sentiment ◽  
The Impact

The research paper develops an understanding on how news based sentiment capture investor behaviour reflected in price jumps in stock markets. It compares the impact on two models of stock price jumps; the non-parametric model proposed by BNS and the wavelet based method. The study is also a perspective on the semi strong form of market efficiencyUsing the high frequency data from the stock and options market along with the actual high frequency news data from Bloomberg, the two alternative methodologies of jumps have been tested. In addition, options trades have been simulated to see whether profits can be earned from the news sentiment captured by jumps.Methodologically, jumps based on wavelets were found to be better related  with the news sentiment compared to the BNS method. Also,   the news sentiment based jumps were found to present opportunities in the simulated trades that could be exploited for earning profits suggesting that investors overreact.The paper uses an innovative method for computation of the news based sentiment. To the best of our knowledge, the paper is the first to evaluate jumps and news sentiment using the actual news data. A perspective on the semi strong form of market efficiency is presented, that too by departing from the event study based models. 

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.

scholarly journals Bayesian Analysis of Intraday Stochastic Volatility Models of High-Frequency Stock Returns with Skew Heavy-Tailed Errors

2021 ◽  
Vol 14 (4) ◽  
pp. 145
Author(s):  
Makoto Nakakita ◽  
Teruo Nakatsuma
Keyword(s):  
Stock Returns ◽  
Stochastic Volatility ◽  
High Frequency ◽  
Stock Price ◽  
Information Criterion ◽  
High Frequency Data ◽  
Return Volatility ◽  
Frequency Data ◽  
Volatility Clustering ◽  
Variance Gamma

Intraday high-frequency data of stock returns exhibit not only typical characteristics (e.g., volatility clustering and the leverage effect) but also a cyclical pattern of return volatility that is known as intraday seasonality. In this paper, we extend the stochastic volatility (SV) model for application with such intraday high-frequency data and develop an efficient Markov chain Monte Carlo (MCMC) sampling algorithm for Bayesian inference of the proposed model. Our modeling strategy is two-fold. First, we model the intraday seasonality of return volatility as a Bernstein polynomial and estimate it along with the stochastic volatility simultaneously. Second, we incorporate skewness and excess kurtosis of stock returns into the SV model by assuming that the error term follows a family of generalized hyperbolic distributions, including variance-gamma and Student’s t distributions. To improve efficiency of MCMC implementation, we apply an ancillarity-sufficiency interweaving strategy (ASIS) and generalized Gibbs sampling. As a demonstration of our new method, we estimate intraday SV models with 1 min return data of a stock price index (TOPIX) and conduct model selection among various specifications with the widely applicable information criterion (WAIC). The result shows that the SV model with the skew variance-gamma error is the best among the candidates.

MODELING INTERNATIONAL STOCK PRICE COMOVEMENTS WITH HIGH-FREQUENCY DATA

2017 ◽  
Vol 22 (7) ◽  
pp. 1875-1903 ◽  
Author(s):  
Hachmi Ben Ameur ◽  
Fredj Jawadi ◽  
Wael Louhichi ◽  
Abdoulkarim Idi Cheffou
Keyword(s):  
Systemic Risk ◽  
High Frequency ◽  
Stock Price ◽  
Developed Countries ◽  
The United States ◽  
High Frequency Data ◽  
Frequency Data ◽  
Mes Test ◽  
Marginal Expected Shortfall ◽  
Non Gaussian

This paper studies stock price comovements in two key regions [the United States and Europe, which is represented by three major European developed countries (France, Germany, and the United Kingdom)]. Our paper uses recent high-frequency data (HFD) and investigates price comovements in the context of “normal times” and crisis periods. To this end, we applied a non-Gaussian Asymmetrical Dynamic Conditional Correlation (ADCC)-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model and the Marginal Expected Shortfall (MES) approach. This choice has three advantages: (i) With the development of high-frequency trading (HFT), it is more appropriate to use HFD to test price linkages for overlapping and nonoverlapping data. (ii) The ADCC-GARCH model captures further asymmetry in price comovements. (iii) The use of the MES enables to measure systemic risk contributions around the distribution tails. Accordingly, we offer two interesting findings. First, while the hypothesis of asymmetrical and time-varying stock return linkages is not rejected, the MES approach indicates that both European and US indices make a considerable contribution to each other's systemic risk, with significant input from Frankfurt to the French and US markets, especially following the collapse of Lehman Brothers. Second, we show that the propagation of systemic risk is higher during the crisis period and overlapping trading hours than during nonoverlapping hours. Thus, the MES test is recommended as an indicator to help monitor market exposure to systemic risk and to gauge expected losses for other markets.

Forecasting Exchange Rate Realized Volatility Through Decomposition Using High-Frequency Data

2017 ◽  
Author(s):  
Rim mname Lamouchi ◽  
Russell mname Davidson ◽  
Ibrahim mname Fatnassi ◽  
Abderazak Ben mname Maatoug
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Frequency Data
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