Return and volatility spillovers between FTSE All-Share Index and S&P 500 Index
-
Received November 7, 2021;Accepted April 16, 2022;Published April 26, 2022
-
Author(s)Link to ORCID Index: https://orcid.org/0000-0002-8527-2392
-
DOIhttp://dx.doi.org/10.21511/imfi.19(2).2022.09
-
Article InfoVolume 19 2022, Issue #2, pp. 107-118
- TO CITE АНОТАЦІЯ
-
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
- 593 Views
-
268 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
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
-
JEL Classification (Paper profile tab)G10, G11, G15
-
References64
-
Tables7
-
Figures2
-
- Figure 1. S&P 500 Index
- Figure 2. FTSE All-Share Index
-
- 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
-
- Abbas, G., Bashir, U., Wang, S., Zebende, G. F., & Ishfaq, M. (2019). The return and volatility nexus among stock market and macroeconomic fundamentals for China. Physica A: Statistical Mechanics and Its Applications, 526, 121025.
- Alqahtani, A., & Chevallier, J. (2020). Dynamic spillovers between Gulf Cooperation Council’s stocks, VIX, oil and gold volatility indices. Journal of Risk and Financial Management, 13(4), 69.
- Antonakakis, N. (2012). Exchange return co-movements and volatility spillovers before and after the introduction of euro. Journal of International Financial Markets, Institutions and Money, 22(5), 1091-1109.
- Arouri, M. E. H., Jouini, J., & Nguyen, D. K. (2011). Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management. Journal of International Money and Finance, 30(7), 1387-1405.
- Asteriou, D., & Bashmakova, Y. (2013). Assessing the impact of oil returns on emerging stock markets: A panel data approach for ten Central and Eastern European Countries. Energy Economics, 38, 204-211.
- Atenga, E. M. E., & Mougoué, M. (2021). Return and volatility spillovers to African equity markets and their determinants. Empirical Economics, 61(2), 883-918.
- Awartani, B., & Maghyereh, A. I. (2013). Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries. Energy Economics, 36, 28-42.
- Awartani, B., Maghyereh, A. I., & Al Shiab, M. (2013). Directional spillovers from the US and the Saudi market to equities in the Gulf Cooperation Council countries. Journal of International Financial Markets, Institutions and Money, 27, 224-242.
- Belcaid, K., & El Ghini, A. (2019). Spillover effects among European, the US and Moroccan stock markets before and after the global financial crisis. Journal of African Business, 20(4), 525-548.
- Berisha, E., Meszaros, J., & Olson, E., 2018. Income inequality, equities, household debt, and interest rates: Evidence from a century of data. Journal of International Money and Finance, 80, 1-14.
- Bono, R., Arnau, J., Alarcón, R., & Blanca, M. J. (2019). Bias, precision, and accuracy of skewness and kurtosis estimators for frequently used continuous distributions. Symmetry, 12(1), 19.
- Cardona, L., Gutiérrez, M., & Agudelo, D. A. (2017). Volatility transmission between US and Latin American stock markets: Testing the decoupling hypothesis. Research in International Business and Finance, 39, 115-127.
- Chen, Yi-Ting. (2002). On the Robustness of Ljung − Box and McLeod − Li Q Tests: A Simulation Study. Economics Bulletin, 3(17), 1-10.
- Chuliá, H., Furió, D., & Uribe, J. M. (2019). Volatility Spillovers in Energy Markets. Energy Journal, 40(3).
- Corsetti, G., Pericoli, M., & Sbracia, M. (2005). ‘Some contagion, some interdependence’: More pitfalls in tests of financial contagion. Journal of International Money and Finance, 24(8), 1177-1199.
- Darrat, A. F., Elkhal, K., & Hakim, S. R. (2000). On the integration of emerging stock markets in the Middle East. Journal of Economic Development, 25(2), 119-130.
- Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158-171.
- Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66.
- Eun, C. S., & Shim, S. (1989). International transmission of stock market movements. Journal of Financial and Quantitative Analysis, 24(2), 241-256.
- Fayyad, A., & Daly, K. (2011). The impact of oil price shocks on stock market returns: Comparing GCC countries with the UK and USA. Emerging Markets Review, 12(1), 61-78.
- Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261.
- Fowowe, B. (2017). Return and volatility spillovers between oil and stock markets in South Africa and Nigeria. African Journal of Economic and Management Studies.
- Gamba-Santamaria, S., Gomez-Gonzalez, J. E., Hurtado-Guarin, J. L., & Melo-Velandia, L. F. (2017). Stock market volatility spillovers: Evidence for Latin America. Finance Research Letters, 20, 207-216.
- Geng, J. B., Du, Y. J., Ji, Q., & Zhang, D. (2021). Modeling return and volatility spillover networks of global new energy companies. Renewable and Sustainable Energy Reviews, 135, 110214.
- Giovannetti, G., & Velucchi, M. (2013). A spillover analysis of shocks from US, UK and China on African financial markets. Review of Development Finance, 3(4), 169-179.
- Goncalves-Pinto, L., Grundy, B. D., Hameed, A., van der Heijden, T., & Zhu, Y. (2020). Why do option prices predict stock returns? The role of price pressure in the stock market. Management Science, 66(9), 3903-3926.
- Graham, M., Kiviaho, J., Nikkinen, J., & Omran, M. (2013). Global and regional co-movement of the MENA stock markets. Journal of Economics and Business, 65, 86-100.
- Hamao, Y., Masulis, R. W., & Ng, V. (1990). Correlations in price changes and volatility across international stock markets. The Review of Financial Studies, 3(2), 281-307.
- Humavindu, M. N., & Floros, C. (2006). Integration and volatility spillovers in African equity markets: evidence from Namibia and South Africa. African Finance Journal, 8(2), 31-50.
- Jin, X., & An, X. (2016). Global financial crisis and emerging stock market contagion: A volatility impulse response function approach. Research in International Business and Finance, 36, 179-195.
- Kang, S. H., Maitra, D., Dash, S. R., & Brooks, R. (2019). Dynamic spillovers and connectedness between stock, commodities, bonds, and VIX markets. Pacific-Basin Finance Journal, 58(C).
- Kang, S. H., & Lee, J. W. (2019). The network connectedness of volatility spillovers across global futures markets. Physica A: Statistical Mechanics and its Applications.
- Kinkyo, T. (2021). Region-wide connectedness of Asian equity and currency markets. The North American Journal of Economics and Finance, 58, 101515.
- Kishor, N., & Singh, R. P. (2014). Stock return volatility effect: Study of BRICS. Transnational Corporations Review, 6(4), 406-418.
- Kumar, D., & Maheswaran, S. (2013). Correlation transmission between crude oil and Indian markets. South Asian Journal of Global Business Research.
- Kumar, M. (2011). Return and volatility spillovers: evidence from Indian exchange rates. International Journal of Economics and Business Research, 3(4), 371-387.
- Kuttu, S. (2014). Return and volatility dynamics among four African equity markets: A multivariate VAR-EGARCH analysis. Global Finance Journal, 25(1), 56-69.
- Lee, J. S. (2009). Volatility spillover effects among six Asian countries. Applied Economics Letters, 16, 501-508.
- Lin, W. L., Engle, R. F., & Ito, T. (1994). Do bulls and bears move across borders? International transmission of stock returns and volatility. Review of Financial Studies, 7(3), 507-538.
- Maghyereh, A., & Awartani, B. (2012). Return and volatility spillovers between Dubai financial market and Abu Dhabi stock exchange in the UAE. Applied Financial Economics, 22(10), 837-848.
- Maghyereh, A. I., Awartani, B., & Bouri, E. (2016). The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes. Energy Economics, 57, 78-93.
- Maghyereh, A., & Al-Kandari, A. (2007). Oil prices and stock markets in GCC countries: new evidence from nonlinear cointegration analysis. Managerial Finance.
- Maghyereh, A., & Al-Zuobi, H. (2005). Free trade agreements and equity market integration: the case of the US and Jordan. Applied Financial Economics, 15(14), 995-1005.
- Malik, F. (2021). Volatility spillover between exchange rate and stock returns under volatility shifts. The Quarterly Review of Economics and Finance, 80, 605-613.
- Markowski, Ł., & Keller, J. (2020). Fear Anatomy – an Attempt to Assess the Impact of Selected Macroeconomic Variables on the Variability of the VIX S&P 500 Index. Annales Universitatis Mariae Curie-Skłodowska, sectio H–Oeconomia, 54(2).
- Mensi, W., Beljid, M., Boubaker, A., & Managi, S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Economic Modelling, 32, 15-22.
- Mohamed El Hédi Arouri, Amine Lahiani, & Duc Khuong Nguyen. (2015). Cross-market dynamics and optimal portfolio strategies in Latin American equity markets. European Business Review.
- Naifar, N., & Al Dohaiman, M. S. (2013). Nonlinear analysis among crude oil prices, stock markets’ return and macroeconomic variables. International Review of Economics & Finance, 27, 416-431.
- Sahu, T. N. (2016). Macroeconomic variables and security prices in India during the liberalized period. Springer.
- Salameh, H. M., & Alzubi, B. (2018). An investigation of stock market volatility: evidence from Dubai financial market. Journal of Economic and Administrative Sciences.
- Singh, A. (2021). Investigating the dynamic relationship between litigation funding, gold, bitcoin and the stock market: The case of Australia. Economic Modelling, 97, 45-57.
- Śmiech, S., Papież, M., Fijorek, K., & Dąbrowski, M. A. (2019). What drives food price volatility? Evidence based on a generalized VAR approach applied to the food, financial and energy markets. Economics, 13(1).
- Sugimoto, K., Matsuki, T., & Yoshida, Y. (2014). The global financial crisis: An analysis of the spillover effects on African stock markets. Emerging Markets Review, 21, 201-233.
- Sun, X., Wang, J., Yao, Y., Li, J., & Li, J. (2019). Spillovers among sovereign CDS, stock and commodity markets: A correlation network perspective. International Review of Financial Analysis.
- Thadewald, T., & Büning, H. (2007). Jarque–Bera test and its competitors for testing normality–a power comparison. Journal of Applied Statistics, 34(1), 87-105.
- Umar, Z., Jareño, F., & Escribano, A. (2021). Oil price shocks and the return and volatility spillover between industrial and precious metals. Energy Economics, 99, 105291.
- Vortelinos, D., Gkillas, K. (Gillas), Syriopoulos, C., & Svingou, A. (2018). Asymmetric and nonlinear inter-relations of US stock indices. International Journal of Managerial Finance, 14(1), 78-129.
- Wen, D., Wang, G. J., Ma, C., & Wang, Y. (2019). Risk spillovers between oil and stock markets: A VAR for VaR analysis. Energy Economics, 80, 524-535.
- Yang, J. Y., Samitas, A., & Kampouris, I. (2020). Investor behavior, stock returns and CDS spreads: evidence from foreign and domestic investors in Korea. International Journal of Managerial Finance.
- Yaya, O. S., Ogbonna, A. E., Mudida, R., & Abu, N. (2021). Market efficiency and volatility persistence of cryptocurrency during pre-and post-crash periods of Bitcoin: Evidence based on fractional integration. International Journal of Finance & Economics, 26(1), 1318-1335.
- Yilmaz, K. (2010). Return and volatility spillovers among the East Asian equity markets. Journal of Asian Economics, 21(3), 304-313.
- Yoon, S. M., Al Mamun, M., Uddin, G. S., & Kang, S. H. (2019). Network connectedness and net spillover between financial and commodity markets. The North American Journal of Economics and Finance, 48, 801-818.
- Yunus, N. (2015). Trends and convergence in global housing markets. Journal of International Financial Markets, Institutions and Money, 36, 100-112.
- Zhou, X., Zhang, W., & Zhang, J. (2012). Volatility spillovers between the Chinese and world equity markets. Pacific-Basin Finance Journal, 20(2), 247-270.
-
-
Conceptualization
Khaled Bataineh
-
Data curation
Khaled Bataineh
-
Formal Analysis
Khaled Bataineh
-
Visualization
Khaled Bataineh
-
Writing – original draft
Khaled Bataineh
-
Writing – review & editing
Khaled Bataineh
-
Conceptualization
-
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: 1922 Downloads: 810 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.
-
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: 1682 Downloads: 187 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: 1610 Downloads: 219 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.