IDEAS home Printed from https://ideas.repec.org/p/cqe/wpaper/6717.html
   My bibliography  Save this paper

Volatility Transmission in Overlapping Trading Zones

Author

Listed:
  • Andreas Masuhr

Abstract

Previous volatility spillover models (Engle et al. 1990, Clements et al. 2015) use artificially non overlapping trading zones to identify sources of volatility transmission between these zones. The problem of non overlapping zones is overcome using a copula GARCH approach that allows for multiple overlaps between zones incorporating vine copulas to flexibly model the dependence structure and to meet stylized facts of return data. Stationarity conditions are examined and identifications problems concerning previous work, as well, are pointed out. To handle the relatively large parameter space, the model is estimated by Bayesian methods using a differential evolution MCMC (Braak 2006) approach. Simulation studies are carried out in order to ensure robustness against copula or error term misspecification and in order to analyze the identification problem.

Suggested Citation

  • Andreas Masuhr, 2017. "Volatility Transmission in Overlapping Trading Zones," CQE Working Papers 6717, Center for Quantitative Economics (CQE), University of Muenster.
  • Handle: RePEc:cqe:wpaper:6717
    as

    Download full text from publisher

    File URL: https://www.wiwi.uni-muenster.de/cqe/sites/cqe/files/CQE_Paper/cqe_wp_67_2017.pdf
    File Function: Version of November 2017
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Lee, Tae-Hwy & Long, Xiangdong, 2009. "Copula-based multivariate GARCH model with uncorrelated dependent errors," Journal of Econometrics, Elsevier, vol. 150(2), pages 207-218, June.
    3. Cornelia Savu & Mark Trede, 2010. "Hierarchies of Archimedean copulas," Quantitative Finance, Taylor & Francis Journals, vol. 10(3), pages 295-304.
    4. Christian Schluter & Mark Trede, 2016. "Weak convergence to the Student and Laplace distributions," Post-Print hal-01447853, HAL.
    5. Engle, Robert F & Ito, Takatoshi & Lin, Wen-Ling, 1990. "Meteor Showers or Heat Waves? Heteroskedastic Intra-daily Volatility in the Foreign Exchange Market," Econometrica, Econometric Society, vol. 58(3), pages 525-542, May.
    6. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    7. Richard J Rogalski & Joseph D Vinso, 1978. "Empirical Properties of Foreign Exchange Rates," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 9(2), pages 69-79, June.
    8. Boothe, Paul & Glassman, Debra, 1987. "The statistical distribution of exchange rates: Empirical evidence and economic implications," Journal of International Economics, Elsevier, vol. 22(3-4), pages 297-319, May.
    9. Clements, A.E. & Hurn, A.S. & Volkov, V.V., 2015. "Volatility transmission in global financial markets," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 3-18.
    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. Andreas Masuhr, 2019. "Big in Japan: Global Volatility Transmission between Assets and Trading Places," CQE Working Papers 8119, Center for Quantitative Economics (CQE), University of Muenster.
    2. Eling, Martin & Jung, Kwangmin, 2020. "Risk aggregation in non-life insurance: Standard models vs. internal models," Insurance: Mathematics and Economics, Elsevier, vol. 95(C), pages 183-198.
    3. Krupskii, Pavel & Joe, Harry, 2015. "Structured factor copula models: Theory, inference and computation," Journal of Multivariate Analysis, Elsevier, vol. 138(C), pages 53-73.
    4. Eling, Martin & Jung, Kwangmin, 2018. "Copula approaches for modeling cross-sectional dependence of data breach losses," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 167-180.
    5. Brechmann Eike Christain & Czado Claudia, 2013. "Risk management with high-dimensional vine copulas: An analysis of the Euro Stoxx 50," Statistics & Risk Modeling, De Gruyter, vol. 30(4), pages 307-342, December.
    6. GRIGORIADIS, Vasilis & EMMANOUILIDES, Christos & FOUSEKIS, Panos, 2016. "The Integration Of Pigmeat Markets In The Eu. Evidence From A Regular Mixed Vine Copula," Review of Agricultural and Applied Economics (RAAE), Faculty of Economics and Management, Slovak Agricultural University in Nitra, vol. 19(1), pages 1-10, March.
    7. Han, Yingwei & Li, Jie, 2022. "Should investors include green bonds in their portfolios? Evidence for the USA and Europe," International Review of Financial Analysis, Elsevier, vol. 80(C).
    8. Roger M. Cooke & Harry Joe & Bo Chang, 2020. "Vine copula regression for observational studies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 141-167, June.
    9. Zhi, Bangdong & Wang, Xiaojun & Xu, Fangming, 2022. "Managing inventory financing in a volatile market: A novel data-driven copula model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    10. Stöber, Jakob & Joe, Harry & Czado, Claudia, 2013. "Simplified pair copula constructions—Limitations and extensions," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 101-118.
    11. Nagler, Thomas & Czado, Claudia, 2016. "Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 69-89.
    12. Benoumechiara Nazih & Bousquet Nicolas & Michel Bertrand & Saint-Pierre Philippe, 2020. "Detecting and modeling critical dependence structures between random inputs of computer models," Dependence Modeling, De Gruyter, vol. 8(1), pages 263-297, January.
    13. Apergis, Nicholas & Gozgor, Giray & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Dependence structure in the Australian electricity markets: New evidence from regular vine copulae," Energy Economics, Elsevier, vol. 90(C).
    14. Manuel A. Hernandez & Raul Ibarra & Danilo R. Trupkin, 2014. "How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 41(2), pages 301-325.
    15. Jinyu Zhang & Kang Gao & Yong Li & Qiaosen Zhang, 2022. "Maximum Likelihood Estimation Methods for Copula Models," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 99-124, June.
    16. Benoumechiara Nazih & Bousquet Nicolas & Michel Bertrand & Saint-Pierre Philippe, 2020. "Detecting and modeling critical dependence structures between random inputs of computer models," Dependence Modeling, De Gruyter, vol. 8(1), pages 263-297, January.
    17. Maziar Sahamkhadam & Andreas Stephan, 2019. "Portfolio optimization based on forecasting models using vine copulas: An empirical assessment for the financial crisis," Papers 1912.10328, arXiv.org.
    18. Talbi, Marwa & Bedoui, Rihab & de Peretti, Christian & Belkacem, Lotfi, 2021. "Is the role of precious metals as precious as they are? A vine copula and BiVaR approaches," Resources Policy, Elsevier, vol. 73(C).
    19. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 98-134.
    20. Nguyen, Hoang & Virbickaitė, Audronė, 2023. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Energy Economics, Elsevier, vol. 124(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cqe:wpaper:6717. 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: Susanne Deckwitz (email available below). General contact details of provider: https://edirc.repec.org/data/cqmuede.html .

    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.