IDEAS home Printed from https://ideas.repec.org/a/eee/intfin/v56y2018icp104-127.html
   My bibliography  Save this article

A systemic risk analysis of Islamic equity markets using vine copula and delta CoVaR modeling

Author

Listed:
  • Shahzad, Syed Jawad Hussain
  • Arreola-Hernandez, Jose
  • Bekiros, Stelios
  • Shahbaz, Muhammad
  • Kayani, Ghulam Mujtaba

Abstract

We model the downside and upside spillover effects, systemic and tail dependence risks of the DJ World Islamic (DJWI) and DJ World Islamic Financial (DJWIF) indices, and of Islamic equity indices from Japan, USA and the UK. We draw our empirical results and conclusions by implementing a robust modeling framework consisting of Value-at-Risk (VaR), conditional VaR (CoVaR), Delta conditional VaR (ΔCoVaR), canonical vine conditional VaR (c-vine CoVaR), and time-varying and static bivariate and vine copula models. Full sample estimations indicate larger downside spillover effects and systemic risk for the DJ Islamic Financials World and USA Islamic indices, while Islamic indices from Japan and the DJ World financials have greater exposure to upside spillover risk effects. During the financial crisis the USA and UK Islamic indices display higher downside systemic risk; and the strongest negative tail asymmetric dependence occurs between the DJ Islamic Financials World, and the Islamic indices from Japan and the DJ World financials. Implications of the results are discussed.

Suggested Citation

  • Shahzad, Syed Jawad Hussain & Arreola-Hernandez, Jose & Bekiros, Stelios & Shahbaz, Muhammad & Kayani, Ghulam Mujtaba, 2018. "A systemic risk analysis of Islamic equity markets using vine copula and delta CoVaR modeling," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 104-127.
  • Handle: RePEc:eee:intfin:v:56:y:2018:i:c:p:104-127
    DOI: 10.1016/j.intfin.2018.02.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1042443117303335
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.intfin.2018.02.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Naifar, Nader, 2016. "Do global risk factors and macroeconomic conditions affect global Islamic index dynamics? A quantile regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 61(C), pages 29-39.
    2. Qianqiu Liu, 2009. "On portfolio optimization: How and when do we benefit from high-frequency data?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 560-582.
    3. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    4. Yun, Jaeho & Moon, Hyejung, 2014. "Measuring systemic risk in the Korean banking sector via dynamic conditional correlation models," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 94-114.
    5. Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
    6. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    7. Arreola Hernandez, Jose, 2014. "Are oil and gas stocks from the Australian market riskier than coal and uranium stocks? Dependence risk analysis and portfolio optimization," Energy Economics, Elsevier, vol. 45(C), pages 528-536.
    8. Al Janabi, Mazin A.M. & Arreola Hernandez, Jose & Berger, Theo & Nguyen, Duc Khuong, 2017. "Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1121-1131.
    9. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    10. Reboredo, Juan C. & Ugolini, Andrea, 2015. "Systemic risk in European sovereign debt markets: A CoVaR-copula approach," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 214-244.
    11. Jose Arreola Hernandez & Shawkat Hammoudeh & Duc Khuong Nguyen & Mazin A. M. Al Janabi & Juan Carlos Reboredo, 2017. "Global financial crisis and dependence risk analysis of sector portfolios: a vine copula approach," Applied Economics, Taylor & Francis Journals, vol. 49(25), pages 2409-2427, May.
    12. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    13. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    14. A. I. McLeod & W. K. Li, 1983. "Diagnostic Checking Arma Time Series Models Using Squared‐Residual Autocorrelations," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 269-273, July.
    15. Bekiros, Stelios & Hernandez, Jose Arreola & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2015. "Multivariate dependence risk and portfolio optimization: An application to mining stock portfolios," Resources Policy, Elsevier, vol. 46(P2), pages 1-11.
    16. Girardi, Giulio & Tolga Ergün, A., 2013. "Systemic risk measurement: Multivariate GARCH estimation of CoVaR," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3169-3180.
    17. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    18. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    19. David C. Ling & Andy Naranjo, 1999. "The Integration of Commercial Real Estate Markets and Stock Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 27(3), pages 483-515, September.
    20. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
    21. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2016. "Downside and upside risk spillovers between exchange rates and stock prices," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 76-96.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guo, Dong & Zhou, Peng, 2021. "Green bonds as hedging assets before and after COVID: A comparative study between the US and China," Energy Economics, Elsevier, vol. 104(C).
    2. Wang, Bo & Xiao, Yang, 2023. "Risk spillovers from China's and the US stock markets during high-volatility periods: Evidence from East Asianstock markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
    3. Shanghui Jia & Xinhui Chen & Liyan Han & Jiayu Jin, 2023. "Global climate change and commodity markets: A hedging perspective," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(10), pages 1393-1422, October.
    4. Abuzayed, Bana & Bouri, Elie & Al-Fayoumi, Nedal & Jalkh, Naji, 2021. "Systemic risk spillover across global and country stock markets during the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 180-197.
    5. Maziar Sahamkhadam & Andreas Stephan, 2023. "Portfolio optimization based on forecasting models using vine copulas: An empirical assessment for global financial crises," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2139-2166, December.
    6. Karim, Muhammad Mahmudul & Kawsar, Najmul Haque & Ariff, Mohamed & Masih, Mansur, 2022. "Does implied volatility (or fear index) affect Islamic stock returns and conventional stock returns differently? Wavelet-based granger-causality, asymmetric quantile regression and NARDL approaches," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    7. Dai, Xingyu & Wang, Qunwei & Zha, Donglan & Zhou, Dequn, 2020. "Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: A wavelet-based vine-copula approach," Energy Economics, Elsevier, vol. 88(C).
    8. Ahmed, Walid M.A., 2019. "Islamic and conventional equity markets: Two sides of the same coin, or not?," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 191-205.
    9. Suleman, Muhammad Tahir & McIver, Ron & Kang, Sang Hoon, 2021. "Asymmetric volatility connectedness between Islamic stock and commodity markets," Global Finance Journal, Elsevier, vol. 49(C).
    10. Faisal Alqahtani & Nader Trabelsi & Nahla Samargandi & Syed Jawad Hussain Shahzad, 2020. "Tail Dependence and Risk Spillover from the US to GCC Banking Sectors," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
    11. Quanrui Song & Jianxu Liu & Songsak Sriboonchitta, 2019. "Risk Measurement of Stock Markets in BRICS, G7, and G20: Vine Copulas versus Factor Copulas," Mathematics, MDPI, vol. 7(3), pages 1-16, March.
    12. Zehri, Chokri, 2021. "Stock market comovements: Evidence from the COVID-19 pandemic," The Journal of Economic Asymmetries, Elsevier, vol. 24(C).
    13. Rehman, Mobeen Ur & Katsiampa, Paraskevi & Zeitun, Rami & Vo, Xuan Vinh, 2023. "Conditional dependence structure and risk spillovers between Bitcoin and fiat currencies," Emerging Markets Review, Elsevier, vol. 55(C).
    14. Abuzayed, Bana & Al-Fayoumi, Nedal, 2021. "Risk spillover from crude oil prices to GCC stock market returns: New evidence during the COVID-19 outbreak," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    15. Jianxu Liu & Quanrui Song & Yang Qi & Sanzidur Rahman & Songsak Sriboonchitta, 2020. "Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions," Sustainability, MDPI, vol. 12(10), pages 1-15, May.
    16. Mohammad Arashi & Mohammad Mahdi Rounaghi, 2022. "Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model," Future Business Journal, Springer, vol. 8(1), pages 1-12, December.
    17. Joel Hinaunye Eita & Charles Raoul Tchuinkam Djemo, 2022. "Quantifying Foreign Exchange Risk in the Selected Listed Sectors of the Johannesburg Stock Exchange: An SV-EVT Pairwise Copula Approach," IJFS, MDPI, vol. 10(2), pages 1-29, April.
    18. Shuting Liu & Qifa Xu & Cuixia Jiang, 2021. "Systemic risk of China’s commercial banks during financial turmoils in 2010-2020: A MIDAS-QR based CoVaR approach," Applied Economics Letters, Taylor & Francis Journals, vol. 28(18), pages 1600-1609, October.
    19. Rehman, Mobeen Ur & Asghar, Nadia & Kang, Sang Hoon, 2020. "Do Islamic indices provide diversification to bitcoin? A time-varying copulas and value at risk application," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    20. Ehsan Bagheri & Seyed Babak Ebrahimi & Arman Mohammadi & Mahsa Miri & Stelios Bekiros, 2022. "The Dynamic Volatility Connectedness Structure of Energy Futures and Global Financial Markets: Evidence From a Novel Time–Frequency Domain Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1087-1111, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shahzad, Syed Jawad Hussain & Mensi, Walid & Hammoudeh, Shawkat & Rehman, Mobeen Ur & Al-Yahyaee, Khamis H., 2018. "Extreme dependence and risk spillovers between oil and Islamic stock markets," Emerging Markets Review, Elsevier, vol. 34(C), pages 42-63.
    2. Mensi, Walid & Hammoudeh, Shawkat & Shahzad, Syed Jawad Hussain & Shahbaz, Muhammad, 2017. "Modeling systemic risk and dependence structure between oil and stock markets using a variational mode decomposition-based copula method," Journal of Banking & Finance, Elsevier, vol. 75(C), pages 258-279.
    3. Mensi, Walid & Hammoudeh, Shawkat & Shahzad, Syed Jawad Hussain & Al-Yahyaee, Khamis Hamed & Shahbaz, Muhammad, 2017. "Oil and foreign exchange market tail dependence and risk spillovers for MENA, emerging and developed countries: VMD decomposition based copulas," Energy Economics, Elsevier, vol. 67(C), pages 476-495.
    4. Reboredo, Juan C. & Ugolini, Andrea, 2015. "Downside/upside price spillovers between precious metals: A vine copula approach," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 84-102.
    5. Reboredo, Juan C. & Ugolini, Andrea, 2015. "A vine-copula conditional value-at-risk approach to systemic sovereign debt risk for the financial sector," The North American Journal of Economics and Finance, Elsevier, vol. 32(C), pages 98-123.
    6. Warshaw, Evan, 2019. "Extreme dependence and risk spillovers across north american equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 237-251.
    7. Mensi, Walid & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Risk spillovers and diversification between oil and non-ferrous metals during bear and bull market states," Resources Policy, Elsevier, vol. 72(C).
    8. Cao, Yufei, 2022. "Extreme risk spillovers across financial markets under different crises," Economic Modelling, Elsevier, vol. 116(C).
    9. Yufei Cao, 2021. "Modeling the dependence structure and systemic risk of all listed insurance companies in the Chinese insurance market," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 24(4), pages 367-399, December.
    10. Rehman, Mobeen Ur & Asghar, Nadia & Kang, Sang Hoon, 2020. "Do Islamic indices provide diversification to bitcoin? A time-varying copulas and value at risk application," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    11. Shahzad, Syed Jawad Hussain & Hernandez, Jose Arreola & Al-Yahyaee, Khamis Hamed & Jammazi, Rania, 2018. "Asymmetric risk spillovers between oil and agricultural commodities," Energy Policy, Elsevier, vol. 118(C), pages 182-198.
    12. Rehman, Mobeen Ur & Katsiampa, Paraskevi & Zeitun, Rami & Vo, Xuan Vinh, 2023. "Conditional dependence structure and risk spillovers between Bitcoin and fiat currencies," Emerging Markets Review, Elsevier, vol. 55(C).
    13. Ji, Qiang & Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model," Energy Economics, Elsevier, vol. 75(C), pages 14-27.
    14. Aloui, Riadh & Ben Jabeur, Sami & Mefteh-Wali, Salma, 2022. "Tail-risk spillovers from China to G7 stock market returns during the COVID-19 outbreak: A market and sectoral analysis," Research in International Business and Finance, Elsevier, vol. 62(C).
    15. Xiao, Yang, 2020. "The risk spillovers from the Chinese stock market to major East Asian stock markets: A MSGARCH-EVT-copula approach," International Review of Economics & Finance, Elsevier, vol. 65(C), pages 173-186.
    16. Yufei Cao, 2021. "Measuring systemic risk and dependence structure between real estates and banking sectors in China using a CoVaR‐copula method," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5930-5947, October.
    17. Khamis Hamed Al‐Yahyaee & Syed Jawad Hussain Shahzad & Walid Mensi & Seong‐Min Yoon, 2021. "Is there a systemic risk between Sharia, Sukuk, and GCC stock markets? A ΔCoVaR risk metric‐based copula approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2904-2926, April.
    18. Yonghong Jiang & Jinqi Mu & He Nie & Lanxin Wu, 2022. "Time‐frequency analysis of risk spillovers from oil to BRICS stock markets: A long‐memory Copula‐CoVaR‐MODWT method," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3386-3404, July.
    19. Reboredo, Juan C. & Ugolini, Andrea, 2016. "The impact of downward/upward oil price movements on metal prices," Resources Policy, Elsevier, vol. 49(C), pages 129-141.
    20. Rehman, Mobeen Ur, 2020. "Do bitcoin and precious metals do any good together? An extreme dependence and risk spillover analysis," Resources Policy, Elsevier, vol. 68(C).

    More about this item

    Keywords

    Spillovers; Systemic risk; Conditional VaR; Copulas; Tail dependence;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfin:v:56:y:2018:i:c:p:104-127. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/intfin .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.