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.

scholarly journals Improved algorithm for cleaning high frequency data: An analysis of foreign currency

2015 ◽  
Vol 12 (3) ◽  
pp. 125-132
Author(s):  
Nirodha I. Jayawardena ◽  
Jason West ◽  
Neda Todorova ◽  
Bin Li
Keyword(s):  
High Frequency ◽  
Foreign Currency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Volatility Clustering ◽  
Time Series Modelling ◽  
Non Gaussian ◽  
Irregular Spacing ◽  
The Empirical Analysis

High-frequency data are notorious for their noise and asynchrony, which may bias or contaminate the empirical analysis of prices and returns. In this study, we develop a novel data filtering approach that simultaneously addresses volatility clustering and irregular spacing, which are inherent characteristics of high-frequency data. Using high frequency currency data collected at five-minute intervals, we find the presence of vast microstructure noise coupled with random volatility clusters, and observe an extremely non-Gaussian distribution of returns. To process non-Gaussian high-frequency data for time series modelling, we propose two efficient and robust standardisation methods that cater for volatility clusters, which clean the data and achieve near-normal distributions. We show that the filtering process efficiently cleans high-frequency data for use in empirical settings while retaining the underlying distributional properties

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.

Costa Rica’s economy looks set for a strong recovery

2021 ◽  
Keyword(s):  
United States ◽  
Exchange Rate ◽  
High Frequency ◽  
The United States ◽  
Economic Recovery ◽  
High Frequency Data ◽  
Frequency Data ◽  
Short Term ◽  
Content Type ◽  
Credit Quality

Significance GDP posted growth of 9.4% year-on-year in the second quarter, the highest rate in 23 years. According to high-frequency data, economic recovery appears to have continued between July and September albeit at a slightly slower pace. Impacts Low inflation will allow the Central Bank to maintain an accommodative stance in the short term; any rate hikes next year will be gradual. Banks’ profitability and credit quality may deteriorate in 2022 as loan restructuring measures expire and lagged pandemic effects kick in. The exchange rate may further depreciate amid uncertainty over the country’s fiscal prospects and the outcome of the 2022 elections. While tourism appears to be on a strong trajectory, the spread of Omicron in Europe and the United States could reverse its recovery.

scholarly journals High-Frequency Data and a Weekly Economic Index during the Pandemic

2021 ◽  
Vol 111 ◽  
pp. 326-330
Author(s):  
Daniel J. Lewis ◽  
Karel Mertens ◽  
James H. Stock ◽  
Mihir Trivedi
Keyword(s):  
High Frequency ◽  
Credit Card ◽  
The United States ◽  
Economic Index ◽  
High Frequency Data ◽  
The Novel ◽  
Frequency Data ◽  
Similarities And Differences ◽  
Economic Developments ◽  
Novel Coronavirus

This paper describes a weekly economic index (WEI) developed to track the rapid economic developments associated with the onset of and policy response to the novel coronavirus in the United States. The WEI, with its ten component series, tracks the overall economy. Comparing the contributions of the WEI's components in the 2008 and 2020 recessions reveals differences in how the two events played out at a high frequency. During the 2020 collapse and recovery, it provides a benchmark to interpret similarities and differences of novel indicators with shorter samples and/or nonstationary coverage, such as mobility indexes or credit card spending.

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