IDEAS home Printed from https://ideas.repec.org/p/arx/papers/physics-0607176.html
   My bibliography  Save this paper

Bayesian Analysis of the Conditional Correlation Between Stock Index Returns with Multivariate SV Models

Author

Listed:
  • Anna Pajor

Abstract

In the paper we compare the modelling ability of discrete-time multivariate Stochastic Volatility models to describe the conditional correlations between stock index returns. We consider four trivariate SV models, which differ in the structure of the conditional covariance matrix. Specifications with zero, constant and time-varying conditional correlations are taken into account. As an example we study trivariate volatility models for the daily log returns on the WIG, SP500, and FTSE100 indexes. In order to formally compare the relative explanatory power of SV specifications we use the Bayesian principles of comparing statistic models. Our results are based on the Bayes factors and implemented through Markov Chain Monte Carlo techniques. The results indicate that the most adequate specifications are those that allow for time-varying conditional correlations and that have as many latent processes as there are conditional variances and covariances. The empirical results clearly show that the data strongly reject the assumption of constant conditional correlations.

Suggested Citation

  • Anna Pajor, 2006. "Bayesian Analysis of the Conditional Correlation Between Stock Index Returns with Multivariate SV Models," Papers physics/0607176, arXiv.org.
    • Handle: RePEc:arx:papers:physics/0607176
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/physics/0607176
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    2. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    3. Jacek Osiewalski & Anna Pajor & Mateusz Pipień, 2006. "Bayes Factors for Bivariate GARCH and SV Models," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Władysław Milo & Piotr Wdowiński (ed.), FindEcon Monograph Series: Advances in Financial Market Analysis, edition 1, volume 2, chapter 1, pages 15-35, University of Lodz.
    4. Anna Pajor, 2005. "Bayesian Analysis of Stochastic Volatility Model and Portfolio Allocation," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Władysław Milo & Piotr Wdowiński (ed.), Acta Universitatis Lodziensis. Folia Oeconomica nr 192/2005 - Issues in Modeling, Forecasting and Decision-Making in Financial Markets, edition 1, volume 127, chapter 14, pages 229-249, University of Lodz.
    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. Roberto Leon-Gonzalez & Blessings Majoni, 2024. "Approximate Factor Models with a Common Multiplicative Factor for Stochastic Volatility," Working Paper series 24-04, Rimini Centre for Economic Analysis.
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.

    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. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    2. Karen K. Lewis, 2011. "Global Asset Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 3(1), pages 435-466, December.
    3. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    4. Kwon, Oh Kang & Satchell, Stephen, 2018. "The distribution of cross sectional momentum returns," Journal of Economic Dynamics and Control, Elsevier, vol. 94(C), pages 225-241.
    5. José Afonso Faias & Juan Arismendi Zambrano, 2022. "Equity Risk Premium Predictability from Cross-Sectoral Downturns [International asset allocation with regime shifts]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 12(3), pages 808-842.
    6. Cotter, John & Longin, Francois, 2006. "Implied correlation from VaR," MPRA Paper 3506, University Library of Munich, Germany.
    7. L. Bauwens & E. Otranto, 2013. "Modeling the Dependence of Conditional Correlations on Volatility," Working Paper CRENoS 201304, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    8. Lundblad, Christian T & Jotikasthira, Chotibhak, 2009. "Asset fire sales and purchases and the international transmission of financial shocks," CEPR Discussion Papers 7595, C.E.P.R. Discussion Papers.
    9. Mittnik, Stefan, 2014. "VaR-implied tail-correlation matrices," Economics Letters, Elsevier, vol. 122(1), pages 69-73.
    10. 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).
    11. Meng-Jou Lu & Cathy Yi-Hsuan Chen & Wolfgang Karl Härdle, 2017. "Copula-based factor model for credit risk analysis," Review of Quantitative Finance and Accounting, Springer, vol. 49(4), pages 949-971, November.
    12. Arouri, Mohamed & M’saddek, Oussama & Nguyen, Duc Khuong & Pukthuanthong, Kuntara, 2019. "Cojumps and asset allocation in international equity markets," Journal of Economic Dynamics and Control, Elsevier, vol. 98(C), pages 1-22.
    13. Moerman, G.A. & Mahieu, R.J. & Koedijk, C.G., 2004. "Financial Integration Through Benchmarks: The European Banking Sector," ERIM Report Series Research in Management ERS-2004-110-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    14. Amira, Khaled & Taamouti, Abderrahim & Tsafack, Georges, 2011. "What drives international equity correlations? Volatility or market direction?," Journal of International Money and Finance, Elsevier, vol. 30(6), pages 1234-1263, October.
    15. Shan, Chenyu & Tang, Dragon Yongjun & Wang, Sarah Qian & Zhang, Chang, 2022. "The diversification benefits and policy risks of accessing China’s stock market," Journal of Empirical Finance, Elsevier, vol. 66(C), pages 155-175.
    16. Niţoi, Mihai & Pochea, Maria Miruna, 2020. "Time-varying dependence in European equity markets: A contagion and investor sentiment driven analysis," Economic Modelling, Elsevier, vol. 86(C), pages 133-147.
    17. Degryse, H.A. & Elahi, M.A. & Penas, M.F., 2012. "Determinants of Banking System Fragility : A Regional Perspective," Other publications TiSEM 1e72ac2e-21f6-423e-8612-c, Tilburg University, School of Economics and Management.
    18. Eli Bouri & Andre Eid & Imad Kachacha, 2014. "The Dynamic Behaviour and Determinants of Linkages among Middle Eastern and North African Stock Exchanges," Economic Issues Journal Articles, Economic Issues, vol. 19(1), pages 1-22, March.
    19. Jondeau, Eric, 2016. "Asymmetry in tail dependence in equity portfolios," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 351-368.
    20. Paulo Horta, 2013. "Contagion Effects in the European Nyse Euronext Stock Markets in the Context of the 2010 Sovereign Debt Crisis," CEFAGE-UE Working Papers 2013_12, University of Evora, CEFAGE-UE (Portugal).

    More about this item

    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:arx:papers:physics/0607176. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.