Return and volatility spillovers between FTSE All-Share Index and S&P 500 Index
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Received November 7, 2021;Accepted April 16, 2022;Published April 26, 2022
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Author(s)Link to ORCID Index: https://orcid.org/0000-0002-8527-2392
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DOIhttp://dx.doi.org/10.21511/imfi.19(2).2022.09
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Article InfoVolume 19 2022, Issue #2, pp. 107-118
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Cited by1 articlesJournal title: Investment Management and Financial InnovationsArticle title: Determinants of UK companies’ dividend policyDOI: 10.21511/imfi.21(1).2024.29Volume: 21 / Issue: 1 / First page: 386 / Year: 2024Contributors: Munther Momany, Khaled Bataineh, Omar Al-Bataineh
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This paper explores the effect of the return and volatility spillover between the Standard and Poor’s 500 index and FTSE All-Share index using the AG-DCC_ Dynamic Conditional Correlation model over the sample period from April 1995 to April 2019. It demonstrates that the Standard and Poor’s 500 return and volatility are crucial in forecasting the market’s future dynamics of the FTSE All Shares where it finds a significant spillover effect for both return and volatility from the Standard and Poor’s 500 to FTSE All Shares, while weak evidence has been found in the opposite direction, that is, an insignificant spillover effect for both return and volatility from FTSE All Shares to the Standard and Poor’s 500. In addition, the paper also finds high Dynamic Conditional Correlation (DCC) between both the Standard and Poor’s 500 and FTSE All Shares. Therefore, it finds asymmetric correlation and transmission mechanisms between the Standard and Poor’s 500 and FTSE All Shares, which means there is an asymmetric interconnectedness between two markets, so allocating assets between two markets will not benefit investor portfolios as investing in high-yielding shares do.
- Keywords
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JEL Classification (Paper profile tab)G10, G11, G15
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References64
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Tables7
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Figures2
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- Figure 1. S&P 500 Index
- Figure 2. FTSE All-Share Index
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- Table 1. Descriptive statistics of the log difference of the Standard and Poor’s 500 index and FTSE All-Share index
- Table 2. Dickey-Fuller test for unit root
- Table 3. Skewness and kurtoses
- Table 4. Jarque-Bera test for normality
- Table 5. Ten lag return series – serial correlation’s Ljung–Box Q examination
- Table 6. Appropriate number of lags
- Table 7. AG model estimation
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Conceptualization
Khaled Bataineh
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Data curation
Khaled Bataineh
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Formal Analysis
Khaled Bataineh
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Visualization
Khaled Bataineh
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Writing – original draft
Khaled Bataineh
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Writing – review & editing
Khaled Bataineh
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Conceptualization
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Test of capital market integration using Fama-French three-factor model: empirical evidence from India
Neeraj Sehrawat , Amit Kumar , Narander Kumar Nigam , Kirtivardhan Singh , Khushi Goyal doi: http://dx.doi.org/10.21511/imfi.17(2).2020.10Investment Management and Financial Innovations Volume 17, 2020 Issue #2 pp. 113-127 Views: 1944 Downloads: 816 TO CITE АНОТАЦІЯIntegration or segmentation of markets determines whether substantial advantages in risk reduction can be attained through portfolio diversification in foreign securities. In an integrated market, investors face risk from country-specific factors and factors, which are common to all countries, but price only the later, as country-specific risk is diversifiable. The aim of this study is two-fold, firstly, investigating the superiority of the Fama-French three-factor model over Capital Asset Pricing Model (CAPM) and later using the superior model to test for integration of Indian and US equity markets (a proxy for global markets). Based on a sample of Bombay Stock Exchange 500 non-financial companies for the period 2003–2019, the data suggest the superiority of Fama-French three-factor model over CAPM. Using the Non-Linear Seemingly Unrelated Regression technique, the first half of the sample period (2003–2010) shows evidence of market segmentation; however, the second sub-period (2011–2019) shows weak signs of market integration, which is supported by the Johansen test of cointegration, suggesting that Indian market is gradually getting integrated with global markets.
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Retraction: Asset allocation in equity, fixed-income and cryptocurrency on the base of individual risk sentiment
Alexey Mikhaylov , Natalia Sokolinskaya , Evgeniy Lopatin doi: http://dx.doi.org/10.21511/imfi.16(2).2019.15Investment Management and Financial Innovations Volume 16, 2019 Issue #2 pp. 171-181 Views: 1689 Downloads: 189 TO CITE АНОТАЦІЯRetracted on August 17, 2020 by the Journal’s owner and Publisher. Type of retraction – plagiarism.
There wasn’t a request for this retraction, but the reason for investigation of plagiarism fact was the Russian Academy of Sciences Committee’s report “Predatory Journals at Scopus and WoS: Translation Plagiarism from Russian Sources”: https://kpfran.ru/wp-content/uploads/plagiarism-by-translation-2.pdf” dated August 12, 2020. The publishing house has familiarized itself with the report. The article by Alexey Mikhaylov, Natalia Sokolinskaya and Evgeniy Lopatin (2019). Asset allocation in equity, fixed-income and cryptocurrency on the base of individual risk sentiment. Investment Management and Financial Innovations, 16(2), 171-181. doi:10.21511/imfi.16(2).2019.15 was mentioned in this report. It is noted that translation plagiarism was detected in this article - http://wiki.dissernet.org/wsave/IMFI_2019_2_1publ.html.
Due to this the publishing house carried out an investigation on possible cases of plagiarism of all articles of these authors (Alexey Mikhaylov, Natalia Sokolinskaya and Evgeniy Lopatin) published in “Business Perspectives” journals.
When the manuscript "Asset allocation in equity, fixed-income and cryptocurrency on the base of individual risk sentiment" was submitted to the Journal for consideration, the authors signed the Cover letter and attested to the fact that their manuscript is an original research and has not been published before. Then, the manuscript was accepted for consideration by the Managing Editor and was tested for plagiarism using the iThenticate and Unicheck programs. Plagiarism was not detected. On August 12, 2020 the Russian Academy of Sciences Committee’s presented the report. Editorial staff decided to re-test all articles of mentioned authors for plagiarism using the iThenticate and Unicheck programs – the programs didn’t show the plagiarism, then the articles were tested for translation plagiarism by the experts of “Business Perspectives” and plagiarism was detected (plagiarism and paraphrases from Russian-language sources).According to the results of the investigation, the Publisher and owner of the journal decided to retract this article because of plagiarism on August 17, 2020.
The authors were notified of such a decision. -
Bank stability in South Africa: what matters?
Banks and Bank Systems Volume 14, 2019 Issue #1 pp. 122-136 Views: 1619 Downloads: 220 TO CITE АНОТАЦІЯThe study examined the determinants of bank stability within the South African banking sector. By controlling for individual bank characteristics and market characteristics, the study determined possible determinants of solvency, a proxy for bank stability, measured by z-score within the South African financial sector. The South African financial sector is highly concentrated but with a significantly large number of banks, the greater portion being foreign owned banks. The business models of some of the financial intermediaries differ from the big four and therefore the influence of the type of business model is of great interest in this study, as it highlights a unique feature of the South African financial sector. The study’s investigation used panel data estimation techniques and found that among the specific bank characteristics, lending activity and capitalization do significantly affect solvency of banks and at sector level concentration was significant. The crisis dummy also revealed that the presence of a financial crisis heightened insolvency. The results have implications for financial institutions and therefore are of interest to regulators, bank management and researchers. Policy prescription in the form of Prompt Corrective Action framework is made to ensure proactive reaction to trends likely to cause instability.