IDEAS home Printed from https://ideas.repec.org/a/eee/jimfin/v133y2023ics0261560623000402.html
   My bibliography  Save this article

The low-magnitude and high-magnitude asymmetries in tail dependence structures in international equity markets and the role of bilateral exchange rate

Author

Listed:
  • Chang, Kuang-Liang

Abstract

This paper introduces a new Markov-switching mixture copula model with a GAS mechanism in the weighting process to investigate the structures of the upper-tail dependence and lower-tail dependence in a low-magnitude asymmetry state and a high-magnitude asymmetry state for international equity markets. Three important findings arise. First, there are obvious low-magnitude and high-magnitude asymmetries in the tail dependence between equity markets of the U.S. and four nations (Canada, France, Germany, and the U.K.). Second, the difference between the upper-tail dependence and lower-tail dependence increases substantially around after 2000. In general, the importance of the lower-tail dependence is stronger than that of the upper-tail dependence in the high-magnitude asymmetry state. However, the Brexit panic does not change the importance of the tail dependence for the U.S. and European equity markets. Third, the evidence for the impact of the bilateral exchange rate on the tail dependence appears only for some international equity markets during the period of a high-magnitude asymmetry state. The bilateral exchange rate has a significant asymmetric impact on the upper-tail dependence and lower-tail dependence for the U.S. and Canadian equity markets. There is weak evidence of an asymmetric effect of the bilateral exchange rate for the U.S. and U.K. equity markets.

Suggested Citation

  • Chang, Kuang-Liang, 2023. "The low-magnitude and high-magnitude asymmetries in tail dependence structures in international equity markets and the role of bilateral exchange rate," Journal of International Money and Finance, Elsevier, vol. 133(C).
  • Handle: RePEc:eee:jimfin:v:133:y:2023:i:c:s0261560623000402
    DOI: 10.1016/j.jimonfin.2023.102839
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0261560623000402
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jimonfin.2023.102839?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    2. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    3. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    4. Garcia, René & Tsafack, Georges, 2011. "Dependence structure and extreme comovements in international equity and bond markets," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1954-1970, August.
    5. Marfatia, Hardik A., 2020. "Investors’ risk perceptions in the US and global stock market integration," Research in International Business and Finance, Elsevier, vol. 52(C).
    6. Lorán Chollete & Andréas Heinen & Alfonso Valdesogo, 2009. "Modeling International Financial Returns with a Multivariate Regime-switching Copula," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 437-480, Fall.
    7. Tafakori, Laleh & Pourkhanali, Armin & Fard, Farzad Alavi, 2018. "Forecasting spikes in electricity return innovations," Energy, Elsevier, vol. 150(C), pages 508-526.
    8. Asgharian, Hossein & Hess, Wolfgang & Liu, Lu, 2013. "A spatial analysis of international stock market linkages," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4738-4754.
    9. Avdulaj, Krenar & Barunik, Jozef, 2015. "Are benefits from oil–stocks diversification gone? New evidence from a dynamic copula and high frequency data," Energy Economics, Elsevier, vol. 51(C), pages 31-44.
    10. Geert Bekaert & Robert J. Hodrick & Xiaoyan Zhang, 2009. "International Stock Return Comovements," Journal of Finance, American Finance Association, vol. 64(6), pages 2591-2626, December.
    11. Lane, Philip R. & Milesi-Ferretti, Gian Maria, 2007. "The external wealth of nations mark II: Revised and extended estimates of foreign assets and liabilities, 1970-2004," Journal of International Economics, Elsevier, vol. 73(2), pages 223-250, November.
    12. Ahmed BenSaïda & Sabri Boubaker & Duc Khuong Nguyen, 2018. "The shifting dependence dynamics between the G7 stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 18(5), pages 801-812, May.
    13. Qiu, Yue & Ren, Yu & Xie, Tian, 2022. "Global factors and stock market integration," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 526-551.
    14. Silva Filho, Osvaldo Candido da & Ziegelmann, Flavio Augusto & Dueker, Michael J., 2012. "Modeling dependence dynamics through copulas with regime switching," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 346-356.
    15. Ramchand, Latha & Susmel, Raul, 1998. "Volatility and cross correlation across major stock markets," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 397-416, October.
    16. Harald Hau & Hélène Rey, 2006. "Exchange Rates, Equity Prices, and Capital Flows," Review of Financial Studies, Society for Financial Studies, vol. 19(1), pages 273-317.
    17. Moore, Tomoe & Wang, Ping, 2014. "Dynamic linkage between real exchange rates and stock prices: Evidence from developed and emerging Asian markets," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 1-11.
    18. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    19. Nektarios Aslanidis & Denise R. Osborn & Marianne Sensier, 2010. "Co-movements between US and UK stock prices: the role of time-varying conditional correlations," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 366-380.
    20. Rodriguez, Juan Carlos, 2007. "Measuring financial contagion: A Copula approach," Journal of Empirical Finance, Elsevier, vol. 14(3), pages 401-423, June.
    21. Huang, Chai Liang, 2020. "International stock market co-movements following US financial globalization," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 788-814.
    22. Ji, Qiang & Liu, Bing-Yue & Cunado, Juncal & Gupta, Rangan, 2020. "Risk spillover between the US and the remaining G7 stock markets using time-varying copulas with Markov switching: Evidence from over a century of data," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    23. Szabolcs Blazsek & Han-Chiang Ho, 2017. "Markov regime-switching Beta--EGARCH," Applied Economics, Taylor & Francis Journals, vol. 49(47), pages 4793-4805, October.
    24. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong, 2019. "Measuring tail risk with GAS time varying copula, fat tailed GARCH model and hedging for crude oil futures," Pacific-Basin Finance Journal, Elsevier, vol. 55(C), pages 95-109.
    25. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173, March.
    26. Szabolcs Blazsek & Han-Chiang Ho & Su-Ping Liu, 2018. "Score-driven Markov-switching EGARCH models: an application to systematic risk analysis," Applied Economics, Taylor & Francis Journals, vol. 50(56), pages 6047-6060, December.
    27. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    28. De Lira Salvatierra, Irving & Patton, Andrew J., 2015. "Dynamic copula models and high frequency data," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 120-135.
    29. Castillo, Nabor O. & Gómez, Héctor W. & Leiva, Víctor & Sanhueza, Antonio, 2011. "On the Fernández-Steel distribution: Inference and application," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2951-2961, November.
    30. Okimoto, Tatsuyoshi, 2008. "New Evidence of Asymmetric Dependence Structures in International Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(3), pages 787-815, September.
    31. 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.
    32. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    33. Chollete, Lorán & de la Peña, Victor & Lu, Ching-Chih, 2011. "International diversification: A copula approach," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 403-417, February.
    34. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
    35. Huang, Wenli & Li, Shi & Qi, Zhen & Zhang, Qi, 2022. "Macro disagreement and international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    36. Yingying Xu & Donald Lien, 2020. "Optimal futures hedging for energy commodities: An application of the GAS model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1090-1108, July.
    37. Mauro Bernardi & Leopoldo Catania, 2019. "Switching generalized autoregressive score copula models with application to systemic risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(1), pages 43-65, January.
    38. Wälti, Sébastien, 2011. "Stock market synchronization and monetary integration," Journal of International Money and Finance, Elsevier, vol. 30(1), pages 96-110, February.
    Full references (including those not matched with items on IDEAS)

    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. Lorán Chollete & Andréas Heinen & Alfonso Valdesogo, 2009. "Modeling International Financial Returns with a Multivariate Regime-switching Copula," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 437-480, Fall.
    2. Aepli, Matthias D. & Füss, Roland & Henriksen, Tom Erik S. & Paraschiv, Florentina, 2017. "Modeling the multivariate dynamic dependence structure of commodity futures portfolios," Journal of Commodity Markets, Elsevier, vol. 6(C), pages 66-87.
    3. Okimoto, Tatsuyoshi, 2014. "Asymmetric increasing trends in dependence in international equity markets," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 219-232.
    4. Mauro Bernardi & Leopoldo Catania, 2016. "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling," Papers 1601.05199, arXiv.org.
    5. Rajan Sruthi & Santhakumar Shijin, 2020. "Investigating liquidity constraints as a channel of contagion: a regime switching approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-21, December.
    6. Anubha Goel & Aparna Mehra, 2019. "Analyzing Contagion Effect in Markets During Financial Crisis Using Stochastic Autoregressive Canonical Vine Model," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 921-950, March.
    7. Małgorzata Doman & Ryszard Doman, 2013. "Dynamic linkages between stock markets: the effects of crises and globalization," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 12(2), pages 87-112, August.
    8. Dimic, Nebojsa & Piljak, Vanja & Swinkels, Laurens & Vulanovic, Milos, 2021. "The structure and degree of dependence in government bond markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    9. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    10. Reboredo, Juan C., 2012. "Do food and oil prices co-move?," Energy Policy, Elsevier, vol. 49(C), pages 456-467.
    11. Cerrato, Mario & Crosby, John & Kim, Minjoo & Zhao, Yang, 2015. "US Monetary and Fiscal Policies - Conflict or Cooperation?," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 2015-78, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    12. BenSaïda, Ahmed, 2018. "The contagion effect in European sovereign debt markets: A regime-switching vine copula approach," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 153-165.
    13. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    14. Chollete, Lorán & de la Peña, Victor & Lu, Ching-Chih, 2011. "International diversification: A copula approach," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 403-417, February.
    15. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    16. Mario Cerrato & John Crosby & Minjoo Kim & Yang Zhao, 2015. "Modeling Dependence Structure and Forecasting Market Risk with Dynamic Asymmetric Copula," Working Papers 2015_15, Business School - Economics, University of Glasgow.
    17. Michael A. Goldstein & Joseph McCarthy & Alexei G. Orlov, 2019. "The Core, Periphery, and Beyond: Stock Market Comovements among EU and Non‐EU Countries," The Financial Review, Eastern Finance Association, vol. 54(1), pages 5-56, February.
    18. Cerrato, Mario & Crosby, John & Kim, Minjoo & Zhao, Yang, 2015. "US Monetary and Fiscal Policies - Conflict or Cooperation?," SIRE Discussion Papers 2015-78, Scottish Institute for Research in Economics (SIRE).
    19. Tong, Bin & Diao, Xundi & Wu, Chongfeng, 2015. "Modeling asymmetric and dynamic dependence of overnight and daytime returns: An empirical evidence from China Banking Sector," Economic Modelling, Elsevier, vol. 51(C), pages 366-382.
    20. Philippas, Dionisis & Siriopoulos, Costas, 2013. "Putting the “C” into crisis: Contagion, correlations and copulas on EMU bond markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 27(C), pages 161-176.

    More about this item

    Keywords

    Markov-switching; Mixture copula; GAS; Equity market; Exchange rate;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:jimfin:v:133:y:2023:i:c:s0261560623000402. 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/inca/30443 .

    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.