Multifrequency-based Non-linear Approach to Analyzing Implied Volatility Transmission Across Global Financial Markets

• Autorzy: Ebenezer Boateng , Emmanuel Asafo-Adjei , John Gartchie Gatsi , Ştefan Cristian Gherghina , Liliana Nicoleta Simionescu
• Języki publikacji: EN

Abstrakty

EN

Research background: The contagious impact of the COVID-19 pandemic has heightened financial market's volatility, nonlinearity, asymmetric and nonstationary dynamics. Hence, the existing relationship among financial assets may have been altered. Moreover, the level of investor risk aversion and market opportunities could also alter in the pandemic. Predictably, investors in the heat of the moment are concerned about minimizing losses. In order to determine the level of hedge risks between implied volatilities in the COVID-19 pandemic through information flow, it is required to take into account the increased vagueness of economic projections as well as the increased uncertainty in asset values as a result of the pandemic.
Purpose of the article: The study aims to examine the transmission of information between the VIX-implied volatility index for S&P 500 and fifteen other implied volatility indices in the COVID-19 pandemic.
Methods: We relied on daily changes in the VIX and fifteen other implied volatility indices from commodities, currencies, and stocks. The study employed the improved complete ensemble empirical mode decomposition with adaptive noise which is in line with the heterogeneous expectations of market participants to denoise the data and extract intrinsic mode functions (IMFs). Subsequently, we clustered the IMFs based on common features into high, low, and medium frequencies. The analysis was carried out using Rényi transfer entropy (RTE), which allowed for the evaluation of both linear and non-linear, as well as varied distributions of the market dynamics.
Findings & value added: Findings from the RTE revealed a bi-directional flow of negative information amid the VIX and each of the volatility indices, particularly in the long term. We found this behavior of the markets to be consistent at varying levels of investors' risk aversion. The findings help investors with their portfolio strategies in the time of the pandemic, which has resulted in fluctuating levels of risk aversion. Our findings characterize global financial markets to be "non-linear heterogeneous evolutionary systems". The results also lend support to the emerging delayed volatility of market competitiveness and external shocks hypothesis. (original abstract)

Słowa kluczowe

PL

Transmisja zjawisk szokowych; Przepływ informacji; Entropia; Rynki finansowe

EN

Transmission of shocks; Data flow; Entropy; Financial markets

• Czasopismo: Oeconomia Copernicana
• Rocznik: 2022
• Tom: 13
• Numer: nr 3
• Strony: 699-743

Twórcy

autor

Ebenezer Boateng

• University of Cape Coast, Ghana

autor

Emmanuel Asafo-Adjei

• University of Cape Coast, Ghana

autor

John Gartchie Gatsi

• University of Cape Coast, Ghana

autor

Ştefan Cristian Gherghina

• Bucharest University of Economic Studies, Bucharest, Romania

autor

Liliana Nicoleta Simionescu

• Bucharest University of Economic Studies, Romania

Bibliografia

• Adam, A. M. (2020). Susceptibility of stock market returns to international economic policy: evidence from effective transfer entropy of Africa with the implication for open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 6(3), 71. doi: 10.3390/joitmc6030071.
• Adam, A. M., Kyei, K., Moyo, S., Gill, R., & Gyamfi, E. N. (2022). Similarities in Southern African Development Community (SADC) exchange rate markets structure: evidence from the ensemble empirical mode decomposition. Journal of African Business, 23(2), 516-530.
• Agyei, S. K., Adam, A. M., Bossman, A., Asiamah, O., Owusu Junior, P., Asafo-Adjei, R., & Asafo-Adjei, E. (2022). Does volatility in cryptocurrencies drive the interconnectedness between the cryptocurrencies market? Insights from wavelets. Cogent Economics & Finance, 10(1), 2061682. doi: 10.1080/23322039.2022.2061682.
• Archer, C., Owusu Junior, P., Adam, A. M., Asafo-Adjei, E., & Baffoe, S. (2022). Asymmetric dependence between exchange rate and commodity prices in Ghana. Annals of Financial Economics, 17(2) 2250012. doi: 10.1142/S2010495222500129.
• Asafo-Adjei, E., Adam, A. M., & Darkwa, P. (2021a). Can crude oil price returns drive stock returns of oil producing countries in Africa? Evidence from bivariate and multiple wavelet. Macroeconomics and Finance in Emerging Market Economies. Advance online publication. doi: 10.1080/17520843.2021.1953864.
• Asafo-Adjei, E., Adam, A. M., Adu-Asare Idun, A., & Ametepi, P. Y. (2022a). Dynamic interdependence of systematic risks in emerging markets economies: a recursive-based frequency-domain approach. Discrete Dynamics in Nature and Society, 2022, 1139869. doi: 10.1155/2022/1139869.
• Asafo-Adjei, E., Adam, A. M., Owusu Junior, P., Akorsu, P. K., & Arthur, C. L. (2022b). A CEEMDAN-based entropy approach measuring multiscale information flow between macroeconomic conditions and stock returns of BRICS. Complexity, 2022, 1-24. doi: 10.1155/2022/7871109.
• Asafo-Adjei, E., Boateng, E., Isshaq, Z., Idun, A. A. A., Owusu Junior, P., & Adam, A. M. (2021b). Financial sector and economic growth amid external uncertainty shocks: insights into emerging economies. Plos one, 16(11), e0259303. doi: 10.1371/journal.pone.0259303.
• Asafo-Adjei, E., Frimpong, S., Owusu Junior, P., Adam, A. M., Boateng, E., & Ofori Abosompim, R. (2022). Multi-frequency information flows between global commodities and uncertainties: evidence from COVID-19 pandemic. Complexity, 2022, 6499876. doi: 10.1155/2022/6499876.
• Asafo-Adjei, E., Owusu Junior, P., & Adam, A. M. (2021c). Information flow between global equities and cryptocurrencies: a VMD-based entropy evaluating shocks from COVID-19 pandemic. Complexity, 2021, 4753753. doi: 10.1155/2021/4753753.
• Badshah, I. U. (2018). Volatility spillover from the fear index to developed and emerging markets. Emerging Markets Finance and Trade, 54(1), 27-40. doi: 10.1080/1540496X.2016.1220294.
• Badshah, I., Bekiros, S., Lucey, B. M., & Uddin, G. S. (2018). Asymmetric linkages among the fear index and emerging market volatility indices. Emerging Markets Review, 37, 17-31. doi: 10.1016/j.ememar.2018.03.002.
• Balcilar, M., & Demirer, R. (2015). Effect of global shocks and volatility on herd behavior in an emerging market: evidence from Borsa Istanbul. Emerging Markets Finance and Trade, 51(1), 140-159. doi: 10.1080/1540496X.2015.1011520.
• Barson, Z., Junior, P. O., Adam, A. M., & Asafo-Adjei, E. (2022). Connectedness between gold and cryptocurrencies in COVID-19 pandemic: a frequency dependent asymmetric and causality analysis. Complexity, 2022, 7648085. doi: 10.1155/2022/7648085.
• Beck, C, & Schögl, F. (1995). Thermodynamics of chaotic systems: an introduction. Cambridge: Cambridge University Press.
• Behrendt, S., Dimpfl, T., Peter, F. J., & Zimmermann, D. J. (2019). RTransfer Entropy-quantifying information flow between different time series using effective transfer entropy. SoftwareX, 10, 100265. doi: 10.1016/j.softx.2019.100265.
• Boateng, E., Adam, A. M., & Owusu Junior, P. (2021). Modelling the heterogeneous relationship between the crude oil implied volatility index and African stocks in the coronavirus pandemic. Resources policy, 74, 102389. doi: 10.1016/j.resourpol.2021.102389.
• Boateng, E., Asafo-Adjei, E., Addison, A., Quaicoe, S., Yusuf, M. A., & Adam, A. M. (2022a). Interconnectedness among commodities, the real sector of Ghana and external shocks. Resources Policy, 75, 102511. doi: 10.1016/j.resourpol.2021.102511.
• Boateng, E., Owusu Junior, P., Adam, A. M., Abeka Jr, M., Qabhobho, T., & Asafo-Adjei, E. (2022b). Quantifying information flows among developed and emerging equity markets. Mathematical Problems in Engineering, 2022, 2462077. doi: 10.1155/2022/2462077.
• Bossman, A. (2021). Information flow from COVID-19 pandemic to Islamic and conventional equities: an ICEEMDAN-induced transfer entropy analysis. Complexity, 2021, 4917051. doi: 10.1155/2021/4917051.
• Bossman, A., Agyei, S. K., Owusu Junior, P., Agyei, E. A., Akorsu, P. K., Marfo-Yiadom, E., & Amfo-Antiri, G. (2022a). Flights-to-and-from-quality with Islamic and conventional bonds in the COVID-19 pandemic era: ICEEMDAN based transfer entropy. Complexity, 2022, 1027495. doi: 10.1155/2022/1027495.
• Bossman, A., Owusu Junior, P., & Tiwari, A. K. (2022b). Dynamic connectedness and spillovers between Islamic and conventional stock markets: time-and frequency-domain approach in COVID-19 era. Heliyon, 8(4), e09215. doi: 10.1016/j.heliyon.2022.e09215.
• Bulathsinhalage, S., & Pathirawasam, C. (2017). The effect of corporate governance on firms' capital structure of listed companies in Sri Lanka. Journal of Competitiveness, 9(2), 19-33. doi: 10.7441/joc.2017.02.02.
• Bui, T. D., & Bui, H. T. M. (2020). Threshold effect of economic openness on bank risk-taking: evidence from emerging markets. Economic Modelling, 91, 790-803. doi: 10.1016/j.econmod.2019.11.013.
• Chen, J. H., & Huang, Y. F. (2014). Long memory and structural breaks in modelling the volatility dynamics of VIX-ETFs. International Journal of Business, Economics and Law, 4(1), 54-63.
• Cheuathonghua, M., Padungsaksawasdi, C., Boonchoo, P., & Tongurai, J. (2019). Extreme spillovers of VIX fear index to international equity markets. Financial Markets and Portfolio Management, 33(1), 1-38. doi: 10.1007/s11408-018-0323-6.
• Ciner, C., Gurdgiev, C., & Lucey, B. M. (2010). Hedges and safe havens: an examination of stocks, bonds, oil, gold and the dollar. International Review of Financial Analysis, 29, 202-211. doi: 10.1016/j.irfa.2012.12.001.
• Colominas, M. A., Schlotthauer, G., & Torres, M. E. (2014). Improved complete ensemble EMD: a suitable tool for biomedical signal processing. Biomedical Signal Processing and Control, 14, 19-29. doi: 10.1016/j.bspc.2014.06.009.
• Del Castillo Olivares, A. F., Kumiega, A., Sterijevski, G., & Van Vliet, B. (2018). An empirical study of volatility spillover worldwide. Wilmott, 2018(95), 48-59. doi: 10.1002/wilm.10675.
• Dimpfl, T., & Peter, F. J. (2014). The impact of the financial crisis on transatlantic information flows: an intraday analysis. Journal of International Financial Markets, Institutions and Money, 31, 1-13. doi: 10.1016/j.intfin.2014.03.004.
• Dutta, A., Nikkinen, J., & Rothovius, T. (2017). Impact of oil price uncertainty on middle east and african stock markets. Energy, 123, 189-197. doi: 10.1016/j.energy.2017.01.126.
• Espinosa-Méndez, C., & Arias, J. (2021). COVID-19 effect on herding behaviour in European capital markets. Finance Research Letters, 38, 101787. doi: 10.1016/j.frl.2020.101787.
• Flandrin, P., Goncalves, P., & Rilling, G. (2004). Detrending and denoising with empirical mode decompositions. In 2004 12th European signal processing conference. Vienna: IEEE, 1581-1584.
• Gallegati, M. (2012). A wavelet-based approach to test for financial market contagion. Computational Statistics & Data Analysis, 56(11), 3491-3497. doi: 10.1016/j.csda.2010.11.003.
• Gunay, S. (2020). A new form of financial contagion: Covid-19 and stock market responses. SSRN, 3584243. doi: 10.2139/ssrn.3584243.
• Hartley, R. V. (1928). Transmission of information 1. Bell System Technical Journal, 7(3), 535-563. doi: 10.1002/j.1538-7305.1928.tb01236.x.
• Hommes, C. H. (2001). Financial markets as nonlinear adaptive evolutionary systems. Quantitative Finance, 1(1), 149. doi: 10.1088/1469-7688/1/1/311.
• Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N-C., Tung, C.C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995. doi: 10.1098/rspa.1998.0193.
• Jizba, P., Kleinert, H., & Shefaat, M. (2012). Rényi's information transfer between financial time series. Physica A: Statistical Mechanics and its Applications, 391(10), 2971-2989. doi: 10.1016/j.physa.2011.12.064.
• Khoury, T. A., Junkunc, M., & Mingo, S. (2015). Navigating political hazard risks and legal system quality: venture capital investments in Latin America. Journal of Management, 41(3), 808-840. doi: 10.1177/0149206312453737.
• Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.
• Lahmiri, S., & Bekiros, S. (2020). Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic. Chaos, Solitons & Fractals, 139, 110084. doi: 10.1016/j.chaos.2020.110084y.
• Law, S. H., & Habibullah, M. S. (2009). The determinants of financial development: institutions, openness and financial liberalisation. South African Journal of Economics, 77(1), 45-58. doi: 10.1111/j.1813-6982.2009.01201.x.
• Le, T. H., Kim, J., & Lee, M. (2016). Institutional quality, trade openness, and financial sector development in Asia: an empirical investigation. Emerging Markets Finance and Trade, 52(5), 1047-1059. doi: 10.1080/1540496X.2015.1103138.
• Lekhal, M., & El Oubani, A. (2020). Does the adaptive market hypothesis explain the evolution of emerging markets efficiency? Evidence from the Moroccan financial market. Heliyon, 6(7), e04429. doi: 10.1016/j.heliyon.2020.e04429
• Li, T., Qian, Z., & He, T. (2020). Short-term load forecasting with improved CEEMDAN and GWO-based multiple kernel ELM. Complexity, 2020, 1209547. doi: 10.1155/2020/1209547.
• Lim, K. P., & Kim, J. H. (2011). Trade openness and the informational efficiency of emerging stock markets. Economic Modelling, 28(5), 2228-2238. doi: 10.1016/j.econmod.2011.06.004.
• Lo, A. W. (2004). The adaptive markets hypothesis. Journal of Portfolio Management, 30(5), 15-29. doi: 10.3905/jpm.2004.442611.
• Marschinski, R., & Kantz, H. (2002). Analysing the information flow between financial time series. European Physical Journal B-Condensed Matter and Complex Systems, 30(2), 275-281. doi: 10.1140/epjb/e2002-00379-2.
• Müller, U. A., Dacorogna, M. M., Davé, R. D., Pictet, O. V., Olsen, R. B., & Ward, J. R. (1993). Fractals and intrinsic time: a challenge to econometricians. Unpublished manuscript, Olsen & Associates, Zürich.
• Nyakurukwa, K. (2021). Information flow between the Zimbabwe Stock Exchange and the Johannesburg Stock Exchange: a transfer entropy approach. Organizations and Markets in Emerging Economies, 12(24), 353-376. doi: 10.15388/omee.2021.12.60.
• Owusu Junior, P., Adam, A. M., Asafo-Adjei, E., Boateng, E., Hamidu, Z., & Awotwe, E. (2021a). Time-frequency domain analysis of investor fear and expectations in stock markets of BRIC economies. Heliyon, 7(10), e08211. doi: 10.1016/j.heliyon.2021.e08211.
• Owusu Junior, P., Frimpong, S., Adam, A. M., Agyei, S. K., Gyamfi, E. N., Agyapong, D., & Tweneboah, G. (2021b). COVID-19 as information transmitter to global equity markets: evidence from CEEMDAN-based transfer entropy approach. Mathematical Problems in Engineering, 2021, 8258778. doi: 10.1155/2021/8258778.
• Peng, Y., & Ng, W. L. (2012). Analysing financial contagion and asymmetric market dependence with volatility indices via copulas. Annals of Finance, 8(1), 49-74. doi: 10.1007/s10436-011-0181-y.
• Ramsey, J. B., & Lampart, C. (1998). The decomposition of economic relationships by time scale using wavelets: expenditure and income. Studies in Nonlinear Dynamics & Econometrics, 3(1), 23-42. doi: 10.2202/1558-3708.1039.
• Rényi, A. (1970). Probability theory. Amsterdam: North-Holland Publ. Co.
• Sarwar, G. (2019). Transmission of risk between US and emerging equity markets. Emerging Markets Finance and Trade, 55(5), 1171-1183. doi: 10.1080/1540496X.2018.1468248.
• Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423. doi: 10.1002/j.1538-7305.1948.tb01338.x.
• Smales, L. A. (2022). Spreading the fear: the central role of CBOE VIX in global stock market uncertainty. Global Finance Journal, 51, 100679. doi: 10.1016/j.gfj.2021.100679.
• Tissaoui, K., & Zaghdoudi, T. (2021). Dynamic connectedness between the US financial market and Euro-Asian financial markets: testing transmission of uncertainty through spatial regressions models. Quarterly Review of Economics and Finance, 81, 481-492. doi: 10.1016/j.qref.2020.10.020.
• Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 4144-4147.
• Valaskova, K., Kliestik, T., & Gajdosikova, D. (2021). Distinctive determinants of financial indebtedness: evidence from Slovak and Czech enterprises. Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(3), 639-659. doi: 10.24136/eq.2021.023.
• Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noiseassisted data analysis method. Advances in Adaptive Data Analysis, 1(1), 1-41. doi: 10.1142/S1793536909000047.

Identyfikatory

DOI

10.24136/oc.2022.021