Stock market dependence in crisis periods: Evidence from oil price shocks and the Qatar blockade

Research in International Business and Finance - Tập 54 - Trang 101285 - 2020
Noureddine Benlagha1
1Department of Finance and Economics, College of Business and Economics, Qatar University, P.O.X 2713, DOHA, Qatar

Tóm tắt

Từ khóa


Tài liệu tham khảo

Abul, 2014, Journal of Multinational Financial Dependence patterns across Gulf Arab stock markets: a copula approach, J. Multinatl. Financ. Manag., 25–26, 30

Al Janabi, 2010, ‘An empirical investigation of the informational efficiency of the GCC equity markets: evidence from bootstrap simulation’, Int. Rev. Financ. Anal., 19, 47, 10.1016/j.irfa.2009.11.002

Al Nasser, 2016, Integration of emerging stock markets with global stock markets, Res. Int. Bus. Financ., 36, 1, 10.1016/j.ribaf.2015.09.025

Al‐Maadid, 2020, Political tension and stock markets in the Arabian peninsula, Int. J. Financ. Econ., 1

Alotaibi, 2017, Time varying international financial integration for GCC stock markets, 63, 66

Aloui, 2014, ‘Co-movements of GCC emerging stock markets: new evidence from wavelet coherence analysis’, Econ. Model., 36, 421, 10.1016/j.econmod.2013.09.043

Arouri, 2011, Return and volatility transmission between world oil prices and stock markets of the GCC countries, Econ. Model., 28, 1815, 10.1016/j.econmod.2011.03.012

Baffes, 2015, The great plunge in oil prices: causes, consequences, and policy responses, Ssrn, 10.2139/ssrn.2624398

Balc, 2015, 24, 160

Barlevy, 2003, Rational panics and stock market crashes, J. Econ. Theory, 110, 234, 10.1016/S0022-0531(03)00039-5

Basher, 2014, Dependence patterns across Gulf Arab stock markets: a copula approach, J. Multinatl. Financ. Manag., 25–26, 30, 10.1016/j.mulfin.2014.06.008

Baur, 2013, The structure and degree of dependence: a quantile regression approach, J. Bank. Financ., 37, 786, 10.1016/j.jbankfin.2012.10.015

Belkhir, 2017, ‘Political risk and the cost of capital in the MENA region’, Emerg. Mark. Rev., 33, 155, 10.1016/j.ememar.2017.08.002

Benlagha, 2014, Dependence structure between nominal and index-linked bond returns: a bivariate copula and DCC-GARCH approach, Applied Economics, 46, 3849, 10.1080/00036846.2014.943886

Benlagha, 2017, Range-based and GARCH volatility estimation: Evidence from the French asset market, Global Finance Journal, 32, 149, 10.1016/j.gfj.2016.04.001

Benlagha, 2018, The Dynamic and Dependence of Takaful and Conventional Stock Return Behaviours: Evidence from the Insurance Industry in Saudi Arabia, Asia-Pac Financial Markets, 25, 285, 10.1007/s10690-018-9249-2

Bensaïda, 2015, ‘The frequency of regime switching in fi nancial market volatility’, J. Empir. Finance, 32, 63, 10.1016/j.jempfin.2015.03.005

Bouyé, 2009, Dynamic copula quantile regressions and tail area dynamic dependence in forex markets, Eur. J. Financ., 10.1080/13518470902853491

Charfeddine, 2019, Political tensions, stock market dependence and volatility spillover: Evidence from the recent intra-GCC crises, The North American Journal of Economics and Finance, 50, 10.1016/j.najef.2019.101032

Charfeddine, 2016, A time-varying copula approach for modelling dependency: New evidence from commodity and stock markets, Journal of Multinational Financial Management., 37–38, 168, 10.1016/j.mulfin.2016.10.003

Charfeddine, 2020, Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors, Economic Modelling, 85, 198, 10.1016/j.econmod.2019.05.016

Cheung, 1996, ‘A causality-in-variance test and its application to financial market prices’, J. Econom., 72, 33, 10.1016/0304-4076(94)01714-X

Claus, 2012, Research in International Business and Finance Equity market integration in the Asia Pacific region: evidence from discount factors ଝ, Elsevier B.V., 26, 137

Coelho, 2007, ‘The evolution of interdependence in world equity markets — evidence from minimum spanning trees’, Phys. A Stat. Mech. Appl., 376, 455, 10.1016/j.physa.2006.10.045

De Groot, 2012, The cross-section of stock returns in frontier emerging markets, J. Empir. Finance, 19, 796, 10.1016/j.jempfin.2012.08.007

Demarta, 2007, The t copula and related copulas, Int. Stat. Rev., 73, 111, 10.1111/j.1751-5823.2005.tb00254.x

Fantazzini, 2016, ‘The oil price crash in 2014/15: was there a (negative) financial bubble?, Energy Policy, 96, 383, 10.1016/j.enpol.2016.06.020

Fayyad, 2011, The impact of oil price shocks on stock market returns: comparing GCC countries with the UK and USA, Emerg. Mark. Rev., 12, 61, 10.1016/j.ememar.2010.12.001

Fei, 2017, Dependence in credit default swap and equity markets: Dynamic copula with Markov-switching, Int. J. Forecast., 33, 662, 10.1016/j.ijforecast.2017.01.006

Fenech, 2019, Oil price and Gulf Corporation Council stock indices: new evidence from time-varying copula models, Econ. Model., 77, 81, 10.1016/j.econmod.2018.09.009

Frijns, 2012, Political crises and the stock market integration of emerging markets, J. Bank. Financ., 36, 644, 10.1016/j.jbankfin.2011.05.007

Genest, 2009, Goodness-of-fit tests for copulas: a review and a power study, Insur. Math. Econ., 44, 199, 10.1016/j.insmatheco.2007.10.005

Hansen, 1992, The likelihood ratio test under nonstandard conditions: Testing the markov switching model of gnp, J. Appl. Econom., 7, S61, 10.1002/jae.3950070506

Hao, 2018, Univariate dependence among sectors in Chinese stock market and systemic risk implication, Physica A, 510, 355, 10.1016/j.physa.2018.05.142

Hussain, 2018, The dependence structure between Chinese and other major stock markets using extreme values and copulas, Int. Rev. Econ. Financ., 10.1108/S1569-3767201819

Joe, 2005, Asymptotic efficiency of the two-stage estimation method for copula-based models, J. Multivar. Anal., 94, 401, 10.1016/j.jmva.2004.06.003

Jouini, 2014, Revisiting the shock and volatility transmissions among GCC stock and oil markets: a further investigation, Econ. Model., 38, 486, 10.1016/j.econmod.2014.02.001

Junhui, 2016

Karanasos, 2016, ‘International Review of Financial Analysis Multivariate FIAPARCH modelling of fi nancial markets with dynamic correlations in times of crisis’, Int. Rev. Financ. Anal., 45, 332, 10.1016/j.irfa.2014.09.002

Karolyi, 2003, ‘Chapter 16 are financial assets priced locally or globally?’, 975

Kolaric, 2016, ‘Are stock markets efficient in the face of fear? Evidence from the terrorist attacks in Paris and Brussels’, Financ. Res. Lett., 18, 306, 10.1016/j.frl.2016.05.003

Kollias, 2011, Terrorism and capital markets: the effects of the Madrid and London bomb attacks, Int. Rev. Econ. Financ., 20, 532, 10.1016/j.iref.2010.09.004

Kollias, 2013, The effects of terrorism and war on the oil price-stock index relationship, Energy Econ., 40, 743, 10.1016/j.eneco.2013.09.006

Kumar, 2011, Dynamics of international integration of government securities’ markets, J. Bank. Financ., 35, 142, 10.1016/j.jbankfin.2010.07.019

Leatham, 2014, Interdependence of oil prices and stock market indices: a copula approach, Energy Econ., 44, 331, 10.1016/j.eneco.2014.04.012

Longin, 2001, Extreme correlation of international, J. Finance, LVI, 649, 10.1111/0022-1082.00340

Lucey, 2011, International Review of Financial Analysis Robust global stock market interdependencies, Int. Rev. Financ. Anal., 20, 215, 10.1016/j.irfa.2011.02.001

Ma, 2014, Multifractal detrended cross-correlation analysis of the oil-dependent economies: evidence from the West Texas intermediate crude oil and the GCC stock markets, Phys. A Stat. Mech. Appl., 410, 154, 10.1016/j.physa.2014.05.023

Mensah, 2017, How are Africa’s emerging stock markets related to advanced markets? Evidence from copulas, Econ. Model., 60, 1, 10.1016/j.econmod.2016.08.022

Mensi, 2014, How do OPEC news and structural breaks impact returns and volatility in crude oil markets? Further evidence from a long memory process’, Energy Econ., 42, 343, 10.1016/j.eneco.2013.11.005

Mnasri, 2016, Impact of terrorist attacks on stock market volatility in emerging markets, Emerg. Mark. Rev., 28, 184, 10.1016/j.ememar.2016.08.002

Mohanty, 2011, Oil price movements and stock market returns: evidence from Gulf Cooperation Council (GCC) countries, Glob. Financ. J., 22, 42, 10.1016/j.gfj.2011.05.004

Mokni, 2017, Conditional dependence between international stock markets: a long memory GARCH-copula model approach, J. Multinatl. Financ. Manag., 42–43, 116, 10.1016/j.mulfin.2017.10.006

Mokni, 2019, Measuring persistence of dependence between crude oil prices and GCC stock markets: a copula approach, Q. Rev. Econ. Financ., 72, 14, 10.1016/j.qref.2019.03.003

Naifar, 2013, Nonlinear analysis among crude oil prices, stock markets’ return and macroeconomic variables, Int. Rev. Econ. Financ., 27, 416, 10.1016/j.iref.2013.01.001

Ning, 2010, Journal of International Money Dependence structure between the equity market and the foreign exchange market – a copula approach, J. Int. Money Finance, 29, 743, 10.1016/j.jimonfin.2009.12.002

Nusair, 2016, The effects of oil price shocks on the economies of the Gulf Co-operation Council countries: nonlinear analysis, Energy Policy, 91, 256, 10.1016/j.enpol.2016.01.013

Patton, 2006, Modeling asymmetric exchange rate dependence, Int. Econ. Rev., 47, 527, 10.1111/j.1468-2354.2006.00387.x

Prest, 2018, Explanations for the 2014 oil price decline: supply or demand?, Energy Econ., 74, 63, 10.1016/j.eneco.2018.05.029

Reboredo, 2016, Quantile dependence of oil price movements and stock returns, Energy Econ., 54, 33, 10.1016/j.eneco.2015.11.015

So, 2014, Vine-copula GARCH model with dynamic conditional dependence, Comput. Stat. Data Anal., 76, 655, 10.1016/j.csda.2013.08.008

Yang, 2015, Modeling dependence structures among international stock markets: evidence from hierarchical Archimedean copulas, Econ. Model., 51, 308, 10.1016/j.econmod.2015.08.017