Stylized Facts of High-frequency Financial Time Series Data

Shakeel, Moonis; Srivastava, Bhavana

doi:10.1177/0972150918811701

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…He went on to demonstrate how these statistical qualities invalidate a large number of the statistical methodologies commonly used to study financial data sets and discussed some of the statistical issues that arise in each situation. [15] investigated the features and stylized facts presented by the S&P CNX Nifty futures index's high-frequency financial time series data. They found that the majority of stylized facts were connected to observable empirical behaviors, distributional features, autocorrelation functions, and the seasonality of high-frequency data.…”

Section: Related Researchmentioning

confidence: 99%

Impacts of COVID-19 on Stock Markets: Evidence from Stylized Facts of Technology Companies

Gao¹

2023

Proceedings of the 4th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2022, December

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The outbreak of the COVID-19 pandemic in 2019 has significantly impacted the stock market. The aim of the present study is to investigate whether the COVID-19 pandemic changed the statistical properties of stock return series and whether some stylized facts, which referred to the consistent characteristics across various instruments, markets, and periods, still holds true based on stock data of 34 US technology companies. Some of the common statistical properties of stock returns data include the absence of autocorrelations, volatility clustering, negative skewness and existence of crosscorrelations of absolute returns, amongst others. The analysis in this study is based on the stock data of 34 US companies that have large market capitalizations. The pre-Covid data shows an absence of autocorrelation. Contrastingly, this study shows that autocorrelation exists under the circumstances of COVID-19. This study also demonstrates that volatility clustering exists both before and during the pandemic. Moreover, the stock market during the pandemic is more volatile than before. The study also contradicts the stylized fact of negative skewness of return series and about 50% of the companies' return series also show positive skewness. Furthermore, the data disputes the existence of crosscorrelations between absolute returns across multivariate series.

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Section: Related Researchmentioning

confidence: 99%

Impacts of COVID-19 on Stock Markets: Evidence from Stylized Facts of Technology Companies

Gao¹

2023

Proceedings of the 4th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2022, December

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“…Before estimation of the model, we must ensure that the data series is stationary (Shakeel & Srivastava, 2019). The ADF test (Dickey & Fuller, 1979) and the KPSS test (Kwiatkowski et al, 1992) is employed on the return data of the indices for the purpose.…”

Section: Stationary Variablesmentioning

confidence: 99%

Time-Varying Risks in ESG Equity Investments during the COVID-19 Pandemic

Bhattacharjee

2022

Global Business Review

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We analyse the time-varying risks associated with ESG equity investments in developed, emerging and BRIC equity markets in the wake of the COVID-19 pandemic which has once again underscored the vulnerability of the financial space to shocks. For this purpose, the nonlinear Markov regime switching model is used to analyse the time-varying beta and idiosyncratic volatility of the World ESG Leaders, Emerging Markets ESG Leaders and BRIC ESG Leaders equity portfolios provided by Morgan Stanley Capital International (MSCI). To further complement the evidence, we also refer to the global ACWI ESG Leaders index which represents both developed markets and emerging markets. The evidence suggests that the risk dynamics of the ESG equity portfolios representing the developed markets, emerging markets, BRIC markets and the global markets are distinct during the crisis and calm period. ESG equity investments have higher systematic risk exposure in emerging markets and BRIC markets during the crisis period as well as calm periods. On the other hand, ESG equity investments have higher systematic risk exposure during the crisis period only in case of developed markets. The results of the study provide insights on the time -varying risk dynamics of ESG investing and thus, facilitate informed investment decisions.

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“…Time series analysis is a statistical analysis method based on time series data for forecasting technology trends [85]. Time series data consist of an independent variable representing a time point and a dependent variable representing the frequency that can predict the future trend [86,87]. Among the models for analyzing time series data, the ARMA model is used as a representative [88,89].…”

Section: Time Series Analysismentioning

confidence: 99%

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

et al. 2022

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The railway main transformer is considered one of the most important electrical equipment for trains. Companies and research institutes around the world are striving to develop high-performance railway main transformers. In order to be the first mover for railway main transformer technology, companies and research institutes should predict vacant technology based on the analysis of promising detailed technology areas. Therefore, in this study, a patent analysis to predict vacant technologies based on identified promising IPC technology areas is provided. In order to identify promising detailed IPC technology areas, the technology mapping analysis, the time series analysis, and the social network analysis are conducted based on the patent-IPC matrix, extracted from the data information of 707 patents from the patent database of Korea, China, Japan, United States, Canada, and Europe. Then, through the GTM analysis based on promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node are obtained to predict vacant technologies. From the analysis, we predict the following three groups of vacant technologies: (1) blowerless technology, (2) oil-free technology, and (3) solid-state technology. This study provides insights on the technology trend in railway main transformers, as well as the analysis framework for the development of R&D strategies based on the patent data.

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Stylized Facts of High-frequency Financial Time Series Data

Cited by 10 publications

References 24 publications

Impacts of COVID-19 on Stock Markets: Evidence from Stylized Facts of Technology Companies

Impacts of COVID-19 on Stock Markets: Evidence from Stylized Facts of Technology Companies

Time-Varying Risks in ESG Equity Investments during the COVID-19 Pandemic

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

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